Adjoint modeling for acoustic inversion
Hursky, Paul; Porter, Michael B.; Cornuelle, B. D.; Hodgkiss, W. S.; Kuperman, W. A.
2004-02-01
The use of adjoint modeling for acoustic inversion is investigated. An adjoint model is derived from a linearized forward propagation model to propagate data-model misfit at the observation points back through the medium to the medium perturbations not being accounted for in the model. This adjoint model can be used to aid in inverting for these unaccounted medium perturbations. Adjoint methods are being applied to a variety of inversion problems, but have not drawn much attention from the underwater acoustic community. This paper presents an application of adjoint methods to acoustic inversion. Inversions are demonstrated in simulation for both range-independent and range-dependent sound speed profiles using the adjoint of a parabolic equation model. Sensitivity and error analyses are discussed showing how the adjoint model enables calculations to be performed in the space of observations, rather than the often much larger space of model parameters. Using an adjoint model enables directions of steepest descent in the model parameters (what we invert for) to be calculated using far fewer modeling runs than if a forward model only were used.
Inverse and Predictive Modeling
Energy Technology Data Exchange (ETDEWEB)
Syracuse, Ellen Marie [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-09-27
The LANL Seismo-Acoustic team has a strong capability in developing data-driven models that accurately predict a variety of observations. These models range from the simple – one-dimensional models that are constrained by a single dataset and can be used for quick and efficient predictions – to the complex – multidimensional models that are constrained by several types of data and result in more accurate predictions. Team members typically build models of geophysical characteristics of Earth and source distributions at scales of 1 to 1000s of km, the techniques used are applicable for other types of physical characteristics at an even greater range of scales. The following cases provide a snapshot of some of the modeling work done by the Seismo- Acoustic team at LANL.
Model-reduced inverse modeling
Vermeulen, P.T.M.
2006-01-01
Although faster computers have been developed in recent years, they tend to be used to solve even more detailed problems. In many cases this will yield enormous models that can not be solved within acceptable time constraints. Therefore, there is a need for alternative methods that simulate such
Fast computation of the inverse CMH model
Patel, Umesh D.; Della Torre, Edward
2001-12-01
A fast computational method based on differential equation approach for inverse Della Torre, Oti, Kádár (DOK) model has been extended for the inverse Complete Moving Hysteresis (CMH) model. A cobweb technique for calculating the inverse CMH model is also presented. The two techniques differ from the point of view of flexibility, accuracy, and computation time. Simulation results of the inverse computation for both methods are presented.
MODEL SELECTION FOR SPECTROPOLARIMETRIC INVERSIONS
International Nuclear Information System (INIS)
Asensio Ramos, A.; Manso Sainz, R.; Martínez González, M. J.; Socas-Navarro, H.; Viticchié, B.; Orozco Suárez, D.
2012-01-01
Inferring magnetic and thermodynamic information from spectropolarimetric observations relies on the assumption of a parameterized model atmosphere whose parameters are tuned by comparison with observations. Often, the choice of the underlying atmospheric model is based on subjective reasons. In other cases, complex models are chosen based on objective reasons (for instance, the necessity to explain asymmetries in the Stokes profiles) but it is not clear what degree of complexity is needed. The lack of an objective way of comparing models has, sometimes, led to opposing views of the solar magnetism because the inferred physical scenarios are essentially different. We present the first quantitative model comparison based on the computation of the Bayesian evidence ratios for spectropolarimetric observations. Our results show that there is not a single model appropriate for all profiles simultaneously. Data with moderate signal-to-noise ratios (S/Ns) favor models without gradients along the line of sight. If the observations show clear circular and linear polarization signals above the noise level, models with gradients along the line are preferred. As a general rule, observations with large S/Ns favor more complex models. We demonstrate that the evidence ratios correlate well with simple proxies. Therefore, we propose to calculate these proxies when carrying out standard least-squares inversions to allow for model comparison in the future.
Automatic Flight Controller With Model Inversion
Meyer, George; Smith, G. Allan
1992-01-01
Automatic digital electronic control system based on inverse-model-follower concept being developed for proposed vertical-attitude-takeoff-and-landing airplane. Inverse-model-follower control places inverse mathematical model of dynamics of controlled plant in series with control actuators of controlled plant so response of combination of model and plant to command is unity. System includes feedback to compensate for uncertainties in mathematical model and disturbances imposed from without.
Wake Vortex Inverse Model User's Guide
Lai, David; Delisi, Donald
2008-01-01
NorthWest Research Associates (NWRA) has developed an inverse model for inverting landing aircraft vortex data. The data used for the inversion are the time evolution of the lateral transport position and vertical position of both the port and starboard vortices. The inverse model performs iterative forward model runs using various estimates of vortex parameters, vertical crosswind profiles, and vortex circulation as a function of wake age. Forward model predictions of lateral transport and altitude are then compared with the observed data. Differences between the data and model predictions guide the choice of vortex parameter values, crosswind profile and circulation evolution in the next iteration. Iterations are performed until a user-defined criterion is satisfied. Currently, the inverse model is set to stop when the improvement in the rms deviation between the data and model predictions is less than 1 percent for two consecutive iterations. The forward model used in this inverse model is a modified version of the Shear-APA model. A detailed description of this forward model, the inverse model, and its validation are presented in a different report (Lai, Mellman, Robins, and Delisi, 2007). This document is a User's Guide for the Wake Vortex Inverse Model. Section 2 presents an overview of the inverse model program. Execution of the inverse model is described in Section 3. When executing the inverse model, a user is requested to provide the name of an input file which contains the inverse model parameters, the various datasets, and directories needed for the inversion. A detailed description of the list of parameters in the inversion input file is presented in Section 4. A user has an option to save the inversion results of each lidar track in a mat-file (a condensed data file in Matlab format). These saved mat-files can be used for post-inversion analysis. A description of the contents of the saved files is given in Section 5. An example of an inversion input
Forward modeling. Route to electromagnetic inversion
Energy Technology Data Exchange (ETDEWEB)
Groom, R.; Walker, P. [PetRos EiKon Incorporated, Ontario (Canada)
1996-05-01
Inversion of electromagnetic data is a topical subject in the literature, and much time has been devoted to understanding the convergence properties of various inverse methods. The relative lack of success of electromagnetic inversion techniques is partly attributable to the difficulties in the kernel forward modeling software. These difficulties come in two broad classes: (1) Completeness and robustness, and (2) convergence, execution time and model simplicity. If such problems exist in the forward modeling kernel, it was demonstrated that inversion can fail to generate reasonable results. It was suggested that classical inversion techniques, which are based on minimizing a norm of the error between data and the simulated data, will only be successful when these difficulties in forward modeling kernels are properly dealt with. 4 refs., 5 figs.
Multiscattering inversion for low-model wavenumbers
Alkhalifah, Tariq Ali
2016-09-21
A successful full-waveform inversion implementation updates the low-wavenumber model components first for a proper description of the wavefield propagation and slowly adds the high wavenumber potentially scattering parts of the model. The low-wavenumber components can be extracted from the transmission parts of the recorded wavefield emanating directly from the source or the transmission parts from the single- or double-scattered wavefield computed from a predicted scatter field acting as secondary sources.We use a combined inversion of data modeled from the source and those corresponding to single and double scattering to update the velocity model and the component of the velocity (perturbation) responsible for the single and double scattering. The combined inversion helps us access most of the potential model wavenumber information that may be embedded in the data. A scattering-angle filter is used to divide the gradient of the combined inversion, so initially the high-wavenumber (low-scattering-angle) components of the gradient are directed to the perturbation model and the low-wavenumber (highscattering- angle) components are directed to the velocity model. As our background velocity matures, the scatteringangle divide is slowly lowered to allow for more of the higher wavenumbers to contribute the velocity model. Synthetic examples including the Marmousi model are used to demonstrate the additional illumination and improved velocity inversion obtained when including multiscattered energy. © 2016 Society of Exploration Geophysicists.
Previous Experience a Model of Practice UNAE
Ruiz, Ormary Barberi; Pesántez Palacios, María Dolores
2017-01-01
The statements presented in this article represents a preliminary version of the proposed model of pre-professional practices (PPP) of the National University of Education (UNAE) of Ecuador, an urgent institutional necessity is revealed in the descriptive analyzes conducted from technical support - administrative (reports, interviews, testimonials), pedagogical foundations of UNAE (curricular directionality, transverse axes in practice, career plan, approach and diagnostic examination as subj...
Modeling and Inversion of Scattered Surface waves
Riyanti, C.D.
2005-01-01
In this thesis, we present a modeling method based on a domain-type integral representation for waves propagating along the surface of the Earth which have been scattered in the vicinity of the source or the receivers. Using this model as starting point, we formulate an inversion scheme to estimate
Atmospheric inverse modeling via sparse reconstruction
Directory of Open Access Journals (Sweden)
N. Hase
2017-10-01
Full Text Available Many applications in atmospheric science involve ill-posed inverse problems. A crucial component of many inverse problems is the proper formulation of a priori knowledge about the unknown parameters. In most cases, this knowledge is expressed as a Gaussian prior. This formulation often performs well at capturing smoothed, large-scale processes but is often ill equipped to capture localized structures like large point sources or localized hot spots. Over the last decade, scientists from a diverse array of applied mathematics and engineering fields have developed sparse reconstruction techniques to identify localized structures. In this study, we present a new regularization approach for ill-posed inverse problems in atmospheric science. It is based on Tikhonov regularization with sparsity constraint and allows bounds on the parameters. We enforce sparsity using a dictionary representation system. We analyze its performance in an atmospheric inverse modeling scenario by estimating anthropogenic US methane (CH4 emissions from simulated atmospheric measurements. Different measures indicate that our sparse reconstruction approach is better able to capture large point sources or localized hot spots than other methods commonly used in atmospheric inversions. It captures the overall signal equally well but adds details on the grid scale. This feature can be of value for any inverse problem with point or spatially discrete sources. We show an example for source estimation of synthetic methane emissions from the Barnett shale formation.
Atmospheric inverse modeling via sparse reconstruction
Hase, Nils; Miller, Scot M.; Maaß, Peter; Notholt, Justus; Palm, Mathias; Warneke, Thorsten
2017-10-01
Many applications in atmospheric science involve ill-posed inverse problems. A crucial component of many inverse problems is the proper formulation of a priori knowledge about the unknown parameters. In most cases, this knowledge is expressed as a Gaussian prior. This formulation often performs well at capturing smoothed, large-scale processes but is often ill equipped to capture localized structures like large point sources or localized hot spots. Over the last decade, scientists from a diverse array of applied mathematics and engineering fields have developed sparse reconstruction techniques to identify localized structures. In this study, we present a new regularization approach for ill-posed inverse problems in atmospheric science. It is based on Tikhonov regularization with sparsity constraint and allows bounds on the parameters. We enforce sparsity using a dictionary representation system. We analyze its performance in an atmospheric inverse modeling scenario by estimating anthropogenic US methane (CH4) emissions from simulated atmospheric measurements. Different measures indicate that our sparse reconstruction approach is better able to capture large point sources or localized hot spots than other methods commonly used in atmospheric inversions. It captures the overall signal equally well but adds details on the grid scale. This feature can be of value for any inverse problem with point or spatially discrete sources. We show an example for source estimation of synthetic methane emissions from the Barnett shale formation.
Modelling and inversion of local magnetic anomalies
International Nuclear Information System (INIS)
Quesnel, Y; Langlais, B; Sotin, C; Galdéano, A
2008-01-01
We present a method—named as MILMA for modelling and inversion of local magnetic anomalies—that combines forward and inverse modelling of aeromagnetic data to characterize both magnetization properties and location of unconstrained local sources. Parameters of simple-shape magnetized bodies (cylinder, prism or sphere) are first adjusted by trial and error to predict the signal. Their parameters provide a priori information for inversion of the measurements. Here, a generalized nonlinear approach with a least-squares criterion is adopted to seek the best parameters of the sphere (dipole). This inversion step allows the model to be more objectively adjusted to fit the magnetic signal. The validity of the MILMA method is demonstrated through synthetic and real cases using aeromagnetic measurements. Tests with synthetic data reveal accurate results in terms of depth source, whatever be the number of sources. The MILMA method is then used with real measurements to constrain the properties of the magnetized units of the Champtoceaux complex (France). The resulting parameters correlate with the crustal structure and properties revealed by other geological and geophysical surveys in the same area. The MILMA method can therefore be used to investigate the properties of poorly constrained lithospheric magnetized sources
Stochastic inverse problems: Models and metrics
International Nuclear Information System (INIS)
Sabbagh, Elias H.; Sabbagh, Harold A.; Murphy, R. Kim; Aldrin, John C.; Annis, Charles; Knopp, Jeremy S.
2015-01-01
In past work, we introduced model-based inverse methods, and applied them to problems in which the anomaly could be reasonably modeled by simple canonical shapes, such as rectangular solids. In these cases the parameters to be inverted would be length, width and height, as well as the occasional probe lift-off or rotation. We are now developing a formulation that allows more flexibility in modeling complex flaws. The idea consists of expanding the flaw in a sequence of basis functions, and then solving for the expansion coefficients of this sequence, which are modeled as independent random variables, uniformly distributed over their range of values. There are a number of applications of such modeling: 1. Connected cracks and multiple half-moons, which we have noted in a POD set. Ideally we would like to distinguish connected cracks from one long shallow crack. 2. Cracks of irregular profile and shape which have appeared in cold work holes during bolt-hole eddy-current inspection. One side of such cracks is much deeper than other. 3. L or C shaped crack profiles at the surface, examples of which have been seen in bolt-hole cracks. By formulating problems in a stochastic sense, we are able to leverage the stochastic global optimization algorithms in NLSE, which is resident in VIC-3D®, to answer questions of global minimization and to compute confidence bounds using the sensitivity coefficient that we get from NLSE. We will also address the issue of surrogate functions which are used during the inversion process, and how they contribute to the quality of the estimation of the bounds
Stochastic inverse problems: Models and metrics
Sabbagh, Elias H.; Sabbagh, Harold A.; Murphy, R. Kim; Aldrin, John C.; Annis, Charles; Knopp, Jeremy S.
2015-03-01
In past work, we introduced model-based inverse methods, and applied them to problems in which the anomaly could be reasonably modeled by simple canonical shapes, such as rectangular solids. In these cases the parameters to be inverted would be length, width and height, as well as the occasional probe lift-off or rotation. We are now developing a formulation that allows more flexibility in modeling complex flaws. The idea consists of expanding the flaw in a sequence of basis functions, and then solving for the expansion coefficients of this sequence, which are modeled as independent random variables, uniformly distributed over their range of values. There are a number of applications of such modeling: 1. Connected cracks and multiple half-moons, which we have noted in a POD set. Ideally we would like to distinguish connected cracks from one long shallow crack. 2. Cracks of irregular profile and shape which have appeared in cold work holes during bolt-hole eddy-current inspection. One side of such cracks is much deeper than other. 3. L or C shaped crack profiles at the surface, examples of which have been seen in bolt-hole cracks. By formulating problems in a stochastic sense, we are able to leverage the stochastic global optimization algorithms in NLSE, which is resident in VIC-3D®, to answer questions of global minimization and to compute confidence bounds using the sensitivity coefficient that we get from NLSE. We will also address the issue of surrogate functions which are used during the inversion process, and how they contribute to the quality of the estimation of the bounds.
Stochastic inverse problems: Models and metrics
Energy Technology Data Exchange (ETDEWEB)
Sabbagh, Elias H.; Sabbagh, Harold A.; Murphy, R. Kim [Victor Technologies, LLC, Bloomington, IN 47407-7706 (United States); Aldrin, John C. [Computational Tools, Gurnee, IL 60031 (United States); Annis, Charles [Statistical Engineering, Palm Beach Gardens, FL 33418 (United States); Knopp, Jeremy S. [Air Force Research Laboratory (AFRL/RXCA), Wright Patterson AFB, OH 45433-7817 (United States)
2015-03-31
In past work, we introduced model-based inverse methods, and applied them to problems in which the anomaly could be reasonably modeled by simple canonical shapes, such as rectangular solids. In these cases the parameters to be inverted would be length, width and height, as well as the occasional probe lift-off or rotation. We are now developing a formulation that allows more flexibility in modeling complex flaws. The idea consists of expanding the flaw in a sequence of basis functions, and then solving for the expansion coefficients of this sequence, which are modeled as independent random variables, uniformly distributed over their range of values. There are a number of applications of such modeling: 1. Connected cracks and multiple half-moons, which we have noted in a POD set. Ideally we would like to distinguish connected cracks from one long shallow crack. 2. Cracks of irregular profile and shape which have appeared in cold work holes during bolt-hole eddy-current inspection. One side of such cracks is much deeper than other. 3. L or C shaped crack profiles at the surface, examples of which have been seen in bolt-hole cracks. By formulating problems in a stochastic sense, we are able to leverage the stochastic global optimization algorithms in NLSE, which is resident in VIC-3D®, to answer questions of global minimization and to compute confidence bounds using the sensitivity coefficient that we get from NLSE. We will also address the issue of surrogate functions which are used during the inversion process, and how they contribute to the quality of the estimation of the bounds.
Inverse modeling of April 2013 radioxenon detections
Hofman, Radek; Seibert, Petra; Philipp, Anne
2014-05-01
Significant concentrations of radioactive xenon isotopes (radioxenon) were detected by the International Monitoring System (IMS) for verification of the Comprehensive Nuclear-Test-Ban Treaty (CTBT) in April 2013 in Japan. Particularly, three detections of Xe-133 made between 2013-04-07 18:00 UTC and 2013-04-09 06:00 UTC at the station JPX38 are quite notable with respect to the measurement history of the station. Our goal is to analyze the data and perform inverse modeling under different assumptions. This work is useful with respect to nuclear test monitoring as well as for the analysis of and response to nuclear emergencies. Two main scenarios will be pursued: (i) Source location is assumed to be known (DPRK test site). (ii) Source location is considered unknown. We attempt to estimate the source strength and the source strength along with its plausible location compatible with the data in scenario (i) and (ii), respectively. We are considering also the possibility of a vertically distributed source. Calculations of source-receptor sensitivity (SRS) fields and the subsequent inversion are aimed at going beyond routine calculations performed by the CTBTO. For SRS calculations, we employ the Lagrangian particle dispersion model FLEXPART with high resolution ECMWF meteorological data (grid cell sizes of 0.5, 0.25 and ca. 0.125 deg). This is important in situations where receptors or sources are located in complex terrain which is the case of the likely source of detections-the DPRK test site. SRS will be calculated with convection enabled in FLEXPART which will also increase model accuracy. In the variational inversion procedure attention will be paid not only to all significant detections and their uncertainties but also to non-detections which can have a large impact on inversion quality. We try to develop and implement an objective algorithm for inclusion of relevant data where samples from temporal and spatial vicinity of significant detections are added in an
Confidence bands for inverse regression models
International Nuclear Information System (INIS)
Birke, Melanie; Bissantz, Nicolai; Holzmann, Hajo
2010-01-01
We construct uniform confidence bands for the regression function in inverse, homoscedastic regression models with convolution-type operators. Here, the convolution is between two non-periodic functions on the whole real line rather than between two periodic functions on a compact interval, since the former situation arguably arises more often in applications. First, following Bickel and Rosenblatt (1973 Ann. Stat. 1 1071–95) we construct asymptotic confidence bands which are based on strong approximations and on a limit theorem for the supremum of a stationary Gaussian process. Further, we propose bootstrap confidence bands based on the residual bootstrap and prove consistency of the bootstrap procedure. A simulation study shows that the bootstrap confidence bands perform reasonably well for moderate sample sizes. Finally, we apply our method to data from a gel electrophoresis experiment with genetically engineered neuronal receptor subunits incubated with rat brain extract
The inverse gravimetric problem in gravity modelling
Sanso, F.; Tscherning, C. C.
1989-01-01
One of the main purposes of geodesy is to determine the gravity field of the Earth in the space outside its physical surface. This purpose can be pursued without any particular knowledge of the internal density even if the exact shape of the physical surface of the Earth is not known, though this seems to entangle the two domains, as it was in the old Stoke's theory before the appearance of Molodensky's approach. Nevertheless, even when large, dense and homogeneous data sets are available, it was always recognized that subtracting from the gravity field the effect of the outer layer of the masses (topographic effect) yields a much smoother field. This is obviously more important when a sparse data set is bad so that any smoothing of the gravity field helps in interpolating between the data without raising the modeling error, this approach is generally followed because it has become very cheap in terms of computing time since the appearance of spectral techniques. The mathematical description of the Inverse Gravimetric Problem (IGP) is dominated mainly by two principles, which in loose terms can be formulated as follows: the knowledge of the external gravity field determines mainly the lateral variations of the density; and the deeper the density anomaly giving rise to a gravity anomaly, the more improperly posed is the problem of recovering the former from the latter. The statistical relation between rho and n (and its inverse) is also investigated in its general form, proving that degree cross-covariances have to be introduced to describe the behavior of rho. The problem of the simultaneous estimate of a spherical anomalous potential and of the external, topographic masses is addressed criticizing the choice of the mixed collection approach.
Artificial Neural Network Modeling of an Inverse Fluidized Bed ...
African Journals Online (AJOL)
The application of neural networks to model a laboratory scale inverse fluidized bed reactor has been studied. A Radial Basis Function neural network has been successfully employed for the modeling of the inverse fluidized bed reactor. In the proposed model, the trained neural network represents the kinetics of biological ...
Soft leptogenesis in the inverse seesaw model
Garayoa, Julia; Concepion Gonzalez-Garcia, Maria; Rius, Nuria
2007-02-01
We consider leptogenesis induced by soft supersymmetry breaking terms (``soft leptogenesis''), in the context of the inverse seesaw mechanism. In this model there are lepton number (L) conserving and L-violating soft supersymmetry-breaking B-terms involving the singlet sneutrinos which, together with the — generically small — L-violating parameter responsible of the neutrino mass, give a small mass splitting between the four singlet sneutrino states of a single generation. In combination with the trilinear soft supersymmetry breaking terms they also provide new CP violating phases needed to generate a lepton asymmetry in the singlet sneutrino decays. We obtain that in this scenario the lepton asymmetry is proportional to the L-conserving soft supersymmetry-breaking B-term, and it is not suppressed by the L-violating parameters. Consequently we find that, as in the standard see-saw case, this mechanism can lead to sucessful leptogenesis only for relatively small value of the relevant soft bilinear coupling. The right-handed neutrino masses can be sufficiently low to elude the gravitino problem. Also the corresponding Yukawa couplings involving the lightest of the right-handed neutrinos are constrained to be ∑|Y1k|2lesssim10-7 which generically implies that the neutrino mass spectrum has to be strongly hierarchical.
Inverse Modelling Problems in Linear Algebra Undergraduate Courses
Martinez-Luaces, Victor E.
2013-01-01
This paper will offer an analysis from a theoretical point of view of mathematical modelling, applications and inverse problems of both causation and specification types. Inverse modelling problems give the opportunity to establish connections between theory and practice and to show this fact, a simple linear algebra example in two different…
CICAAR - Convolutive ICA with an Auto-Regressive Inverse Model
DEFF Research Database (Denmark)
Dyrholm, Mads; Hansen, Lars Kai
2004-01-01
We invoke an auto-regressive IIR inverse model for convolutive ICA and derive expressions for the likelihood and its gradient. We argue that optimization will give a stable inverse. When there are more sensors than sources the mixing model parameters are estimated in a second step by least squares...
RUMBLE Technical Report on Inversion Models
Simons, Dick G.; Ainslie, Michael A.; Muller, Simonette H. E.; Boek, Wilco
2002-06-01
The performance of long range low frequency active sonar (LFAS) systems in shallow water is very sensitive to the properties of the sea bed, because of the impact of these on propagation, reverberation and (to a lesser extent) ambient noise. Direct measurement of sea bed parameters using cores or grab samples is impractical for covering a wide area, and instead we consider the possibility of using the LFAS system itself to measure its operating environment. The advantages of this approach are that it exploits existing (or planned) equipment and potentially offers a wide coverage. Geo-acoustic inversion methods are reviewed, with particular consideration for the problems associated with inversion of reverberation data. Three global optimisation methods are described, known as "simulated annealing", "genetic algorithms" and "differential evolution". The Levenberg-Marquardt and downhill simplex local methods are also described. The advantages and disadvantages of each individual method, as well as some hybrid combinations, are discussed in the context of geo-acoustic inversion. A new inversion method has been developed that exploits both the shape and height of the reverberation vs time curve to obtain information about the sea bed reflection loss and scattering strength separately. Tests on synthetic reverberation data show that the inversion method is able to extract parameters representing reflection loss and scattering strength, but cannot always unambiguously separate the effects of sediment sound speed and attenuation. The method is robust to small mismatches in water depth, sonar depth, sediment sound speed gradient and wind speed.
Stochastic forward and inverse groundwater flow and solute transport modeling
Janssen, G.M.C.M.
2008-01-01
Keywords: calibration, inverse modeling, stochastic modeling, nonlinear biodegradation, stochastic-convective, advective-dispersive, travel time, network design, non-Gaussian distribution, multimodal distribution, representers
This thesis offers three new approaches that contribute
Mercury's Internal Magnetic Field: Modeling Core Fields with Smooth Inversions
Uno, H.; Johnson, C. L.; Anderson, B. J.; Korth, H.; Purucker, M. E.; Solomon, S. C.
2008-12-01
MESSENGER's second flyby (M2) of Mercury on 6 October 2008 will provide significantly improved geographical sampling of the planet's internal magnetic field over previous measurements. Latitudinal coverage and spacecraft altitudes will be similar to those during MESSENGER's first encounter (M1), but the spacecraft trajectory will be displaced by about 180° in longitude, yielding the first magnetic measurements in the western hemisphere. We investigate spatial structure in Mercury's internal magnetic field by applying methods from inverse theory to construct low-degree-and-order spherical harmonic models. External fields predicted by a parameterized magnetospheric model are subtracted from the vector field observations. The approach takes into account noise contributions from long-wavelength uncertainties in the external field models, unexplained short-wavelength features, and spacecraft attitude errors. We investigate the effect of different regularization (smoothness) constraints on our inversions. Analyses of data from M1 and the two Mariner 10 flybys that penetrated the magnetosphere yield a preferred spherical harmonic solution to degree and order eight with the centered, axial dipole term g10 dominating. The model shows structure at low and mid-latitude regions near the flybys. Terms predicted by an analytical model for long- wavelength crustal fields - namely g10, g30 and g32 - are present, but their relative amplitudes are not consistent with such a field. We conclude that structure in our models is dominated by core, rather than by crustal, fields. We also investigate, through simulations, field morphologies that are recoverable while the spacecraft is in orbit about Mercury, under the assumption that the long-wavelength contributions from external sources can be accurately modeled and removed. Although the elliptical orbit of MESSENGER will impede the recovery of southern hemisphere structure, we obtain excellent recovery of the dipole field and of
Application Of Shared Gamma And Inverse-Gaussian Frailty Models ...
African Journals Online (AJOL)
Shared Gamma and Inverse-Gaussian Frailty models are used to analyze the survival times of patients who are clustered according to cancer/tumor types under Parametric Proportional Hazard framework. The result of the ... However, no evidence is strong enough for preference of either Gamma or Inverse Gaussian Frailty.
Optimal experimental designs for inverse quadratic regression models
Dette, Holger; Kiss, Christine
2007-01-01
In this paper optimal experimental designs for inverse quadratic regression models are determined. We consider two different parameterizations of the model and investigate local optimal designs with respect to the $c$-, $D$- and $E$-criteria, which reflect various aspects of the precision of the maximum likelihood estimator for the parameters in inverse quadratic regression models. In particular it is demonstrated that for a sufficiently large design space geometric allocation rules are optim...
Reservoir Modeling Combining Geostatistics with Markov Chain Monte Carlo Inversion
DEFF Research Database (Denmark)
Zunino, Andrea; Lange, Katrine; Melnikova, Yulia
2014-01-01
We present a study on the inversion of seismic reflection data generated from a synthetic reservoir model. Our aim is to invert directly for rock facies and porosity of the target reservoir zone. We solve this inverse problem using a Markov chain Monte Carlo (McMC) method to handle the nonlinear,...... constitute samples of the posterior distribution.......We present a study on the inversion of seismic reflection data generated from a synthetic reservoir model. Our aim is to invert directly for rock facies and porosity of the target reservoir zone. We solve this inverse problem using a Markov chain Monte Carlo (McMC) method to handle the nonlinear......, multi-step forward model (rock physics and seismology) and to provide realistic estimates of uncertainties. To generate realistic models which represent samples of the prior distribution, and to overcome the high computational demand, we reduce the search space utilizing an algorithm drawn from...
Inverse modelling for flow and transport in porous media
International Nuclear Information System (INIS)
Giudici, M.
2004-01-01
The problem of parameter identification for flow and transport model in porous media is discussed in this communication. First, a general framework for the development and application of environmental models is discussed. Then the forward and inverse problems for discrete models are described in detail, introducing fundamental concepts (uniqueness, identifiability, stability, conditioning). The importance of model scales is reviewed and is shown its link with the stability and conditioning issues. Finally some remarks are given to the use of several independent sets of data in inverse modelling
Reservoir Modeling Combining Geostatistics with Markov Chain Monte Carlo Inversion
DEFF Research Database (Denmark)
Zunino, Andrea; Lange, Katrine; Melnikova, Yulia
2014-01-01
We present a study on the inversion of seismic reflection data generated from a synthetic reservoir model. Our aim is to invert directly for rock facies and porosity of the target reservoir zone. We solve this inverse problem using a Markov chain Monte Carlo (McMC) method to handle the nonlinear......, multi-step forward model (rock physics and seismology) and to provide realistic estimates of uncertainties. To generate realistic models which represent samples of the prior distribution, and to overcome the high computational demand, we reduce the search space utilizing an algorithm drawn from...... geostatistics. The geostatistical algorithm learns the multiple-point statistics from prototype models, then generates proposal models which are tested by a Metropolis sampler. The solution of the inverse problem is finally represented by a collection of reservoir models in terms of facies and porosity, which...
Towards inverse modeling of intratumor heterogeneity
Brutovsky, Branislav; Horvath, Denis
2015-08-01
Development of resistance limits efficiency of present anticancer therapies and preventing it remains a big challenge in cancer research. It is accepted, at the intuitive level, that resistance emerges as a consequence of the heterogeneity of cancer cells at the molecular, genetic and cellular levels. Produced by many sources, tumor heterogeneity is extremely complex time dependent statistical characteristics which may be quantified by measures defined in many different ways, most of them coming from statistical mechanics. In this paper, we apply the Markovian framework to relate population heterogeneity to the statistics of the environment. As, from an evolutionary viewpoint, therapy corresponds to a purposeful modi- fication of the cells' fitness landscape, we assume that understanding general relationship between the spatiotemporal statistics of a tumor microenvironment and intratumor heterogeneity will allow to conceive the therapy as an inverse problem and to solve it by optimization techniques. To account for the inherent stochasticity of biological processes at cellular scale, the generalized distancebased concept was applied to express distances between probabilistically described cell states and environmental conditions, respectively.
Why operational risk modelling creates inverse incentives
Doff, R.
2015-01-01
Operational risk modelling has become commonplace in large international banks and is gaining popularity in the insurance industry as well. This is partly due to financial regulation (Basel II, Solvency II). This article argues that operational risk modelling is fundamentally flawed, despite efforts
Henderson, Laura S.; Subbarao, Kamesh
2017-12-01
This work presents a case wherein the selection of models when producing synthetic light curves affects the estimation of the size of unresolved space objects. Through this case, "inverse crime" (using the same model for the generation of synthetic data and data inversion), is illustrated. This is done by using two models to produce the synthetic light curve and later invert it. It is shown here that the choice of model indeed affects the estimation of the shape/size parameters. When a higher fidelity model (henceforth the one that results in the smallest error residuals after the crime is committed) is used to both create, and invert the light curve model the estimates of the shape/size parameters are significantly better than those obtained when a lower fidelity model (in comparison) is implemented for the estimation. It is therefore of utmost importance to consider the choice of models when producing synthetic data, which later will be inverted, as the results might be misleadingly optimistic.
Multi-scattering inversion for low model wavenumbers
Alkhalifah, Tariq Ali
2015-08-19
A successful full wavenumber inversion (FWI) implementation updates the low wavenumber model components first for proper wavefield propagation description, and slowly adds the high-wavenumber potentially scattering parts of the model. The low-wavenumber components can be extracted from the transmission parts of the recorded data given by direct arrivals or the transmission parts of the single and double-scattering wave-fields developed from a predicted scatter field. We develop a combined inversion of data modeled from the source and those corresponding to single and double scattering to update both the velocity model and the component of the velocity (perturbation) responsible for the single and double scattering. The combined inversion helps us access most of the potential model wavenumber information that may be embedded in the data. A scattering angle filter is used to divide the gradient of the combined inversion so initially the high wavenumber (low scattering angle) components of the gradient is directed to the perturbation model and the low wavenumber (high scattering angle) components to the velocity model. As our background velocity matures, the scattering angle divide is slowly lowered to allow for more of the higher wavenumbers to contribute the velocity model.
Data-Driven Model Order Reduction for Bayesian Inverse Problems
Cui, Tiangang
2014-01-06
One of the major challenges in using MCMC for the solution of inverse problems is the repeated evaluation of computationally expensive numerical models. We develop a data-driven projection- based model order reduction technique to reduce the computational cost of numerical PDE evaluations in this context.
Data inversion in coupled subsurface flow and geomechanics models
International Nuclear Information System (INIS)
Iglesias, Marco A; McLaughlin, Dennis
2012-01-01
We present an inverse modeling approach to estimate petrophysical and elastic properties of the subsurface. The aim is to use the fully coupled geomechanics-flow model of Girault et al (2011 Math. Models Methods Appl. Sci. 21 169–213) to jointly invert surface deformation and pressure data from wells. We use a functional-analytic framework to construct a forward operator (parameter-to-output map) that arises from the geomechanics-flow model of Girault et al. Then, we follow a deterministic approach to pose the inverse problem of finding parameter estimates from measurements of the output of the forward operator. We prove that this inverse problem is ill-posed in the sense of stability. The inverse problem is then regularized with the implementation of the Newton-conjugate gradient (CG) algorithm of Hanke (1997 Numer. Funct. Anal. Optim. 18 18–971). For a consistent application of the Newton-CG scheme, we establish the differentiability of the forward map and characterize the adjoint of its linearization. We provide assumptions under which the theory of Hanke ensures convergence and regularizing properties of the Newton-CG scheme. These properties are verified in our numerical experiments. In addition, our synthetic experiments display the capabilities of the proposed inverse approach to estimate parameters of the subsurface by means of data inversion. In particular, the added value of measurements of surface deformation in the estimation of absolute permeability is quantified with respect to the standard history matching approach of inverting production data with flow models. The proposed methodology can be potentially used to invert satellite geodetic data (e.g. InSAR and GPS) in combination with production data for optimal monitoring and characterization of the subsurface. (paper)
Prediction and assimilation of surf-zone processes using a Bayesian network: Part II: Inverse models
Plant, Nathaniel G.; Holland, K. Todd
2011-01-01
A Bayesian network model has been developed to simulate a relatively simple problem of wave propagation in the surf zone (detailed in Part I). Here, we demonstrate that this Bayesian model can provide both inverse modeling and data-assimilation solutions for predicting offshore wave heights and depth estimates given limited wave-height and depth information from an onshore location. The inverse method is extended to allow data assimilation using observational inputs that are not compatible with deterministic solutions of the problem. These inputs include sand bar positions (instead of bathymetry) and estimates of the intensity of wave breaking (instead of wave-height observations). Our results indicate that wave breaking information is essential to reduce prediction errors. In many practical situations, this information could be provided from a shore-based observer or from remote-sensing systems. We show that various combinations of the assimilated inputs significantly reduce the uncertainty in the estimates of water depths and wave heights in the model domain. Application of the Bayesian network model to new field data demonstrated significant predictive skill (R2 = 0.7) for the inverse estimate of a month-long time series of offshore wave heights. The Bayesian inverse results include uncertainty estimates that were shown to be most accurate when given uncertainty in the inputs (e.g., depth and tuning parameters). Furthermore, the inverse modeling was extended to directly estimate tuning parameters associated with the underlying wave-process model. The inverse estimates of the model parameters not only showed an offshore wave height dependence consistent with results of previous studies but the uncertainty estimates of the tuning parameters also explain previously reported variations in the model parameters.
Seismic Model-Based Inversion Using Matlab
Leite, Emilson Pereira
2010-01-01
A simple methodology for mapping acoustic impedance and effective porosity from 3D seismic amplitude data using MatlabÂ® was presented. This methodology can be used for a quick evaluation of reservoir properties, especially when powerful commercial programs are not available. An example with real data was also presented, showing that consistent 3D acoustic impedance models can be obtained if well-logs and 3D seismic data are available. A further improvement would be to obtain the low-frequenc...
Hybrid Adaptive Flight Control with Model Inversion Adaptation
Nguyen, Nhan
2011-01-01
This study investigates a hybrid adaptive flight control method as a design possibility for a flight control system that can enable an effective adaptation strategy to deal with off-nominal flight conditions. The hybrid adaptive control blends both direct and indirect adaptive control in a model inversion flight control architecture. The blending of both direct and indirect adaptive control provides a much more flexible and effective adaptive flight control architecture than that with either direct or indirect adaptive control alone. The indirect adaptive control is used to update the model inversion controller by an on-line parameter estimation of uncertain plant dynamics based on two methods. The first parameter estimation method is an indirect adaptive law based on the Lyapunov theory, and the second method is a recursive least-squares indirect adaptive law. The model inversion controller is therefore made to adapt to changes in the plant dynamics due to uncertainty. As a result, the modeling error is reduced that directly leads to a decrease in the tracking error. In conjunction with the indirect adaptive control that updates the model inversion controller, a direct adaptive control is implemented as an augmented command to further reduce any residual tracking error that is not entirely eliminated by the indirect adaptive control.
Influence of seeing effects on cloud model inversions
Czech Academy of Sciences Publication Activity Database
Tziotziou, K.; Heinzel, Petr; Tsiropoula, G.
2007-01-01
Roč. 472, č. 1 (2007), s. 287-292 ISSN 0004-6361 Institutional research plan: CEZ:AV0Z10030501 Keywords : cloud model * inversions * seeing effects Subject RIV: BN - Astronomy, Celestial Mechanics, Astrophysics Impact factor: 4.259, year: 2007
inverse gaussian model for small area estimation via gibbs sampling
African Journals Online (AJOL)
ADMIN
(1994) extended the work by Fries and. Bhattacharyya (1983) to include the maximum likelihood analysis of the two-factor inverse. Gaussian model for the unbalanced and interaction case for the estimation of small area parameters in finite populations. The object of this article is to develop a Bayesian approach for small ...
Inverse Gaussian model for small area estimation via Gibbs sampling
African Journals Online (AJOL)
We present a Bayesian method for estimating small area parameters under an inverse Gaussian model. The method is extended to estimate small area parameters for finite populations. The Gibbs sampler is proposed as a mechanism for implementing the Bayesian paradigm. We illustrate the method by application to ...
Constraint on Parameters of Inverse Compton Scattering Model for ...
Indian Academy of Sciences (India)
J. Astrophys. Astr. (2011) 32, 299–300 c Indian Academy of Sciences. Constraint on Parameters of Inverse Compton Scattering Model for PSR B2319+60. H. G. Wang. ∗. & M. Lv. Center for Astrophysics,Guangzhou University, Guangzhou, China. ∗ e-mail: cosmic008@yahoo.com.cn. Abstract. Using the multifrequency radio ...
Sun, Y.; Hou, Z.; Huang, M.; Tian, F.; Leung, L. Ruby
2013-12-01
This study demonstrates the possibility of inverting hydrologic parameters using surface flux and runoff observations in version 4 of the Community Land Model (CLM4). Previous studies showed that surface flux and runoff calculations are sensitive to major hydrologic parameters in CLM4 over different watersheds, and illustrated the necessity and possibility of parameter calibration. Both deterministic least-square fitting and stochastic Markov-chain Monte Carlo (MCMC)-Bayesian inversion approaches are evaluated by applying them to CLM4 at selected sites with different climate and soil conditions. The unknowns to be estimated include surface and subsurface runoff generation parameters and vadose zone soil water parameters. We find that using model parameters calibrated by the sampling-based stochastic inversion approaches provides significant improvements in the model simulations compared to using default CLM4 parameter values, and that as more information comes in, the predictive intervals (ranges of posterior distributions) of the calibrated parameters become narrower. In general, parameters that are identified to be significant through sensitivity analyses and statistical tests are better calibrated than those with weak or nonlinear impacts on flux or runoff observations. Temporal resolution of observations has larger impacts on the results of inverse modeling using heat flux data than runoff data. Soil and vegetation cover have important impacts on parameter sensitivities, leading to different patterns of posterior distributions of parameters at different sites. Overall, the MCMC-Bayesian inversion approach effectively and reliably improves the simulation of CLM under different climates and environmental conditions. Bayesian model averaging of the posterior estimates with different reference acceptance probabilities can smooth the posterior distribution and provide more reliable parameter estimates, but at the expense of wider uncertainty bounds.
Bergamaschi, Peter; Karstens, Ute; Manning, Alistair J.; Saunois, Marielle; Tsuruta, Aki; Berchet, Antoine; Vermeulen, Alexander T.; Arnold, Tim; Janssens-Maenhout, Greet; Hammer, Samuel; Levin, Ingeborg; Schmidt, Martina; Ramonet, Michel; Lopez, Morgan; Lavric, Jost; Aalto, Tuula; Chen, Huilin; Feist, Dietrich G.; Gerbig, Christoph; Haszpra, László; Hermansen, Ove; Manca, Giovanni; Moncrieff, John; Meinhardt, Frank; Necki, Jaroslaw; Galkowski, Michal; O'Doherty, Simon; Paramonova, Nina; Scheeren, Hubertus A.; Steinbacher, Martin; Dlugokencky, Ed
2018-01-01
We present inverse modelling (top down) estimates of European methane (CH4) emissions for 2006-2012 based on a new quality-controlled and harmonised in situ data set from 18 European atmospheric monitoring stations. We applied an ensemble of seven inverse models and performed four inversion experiments, investigating the impact of different sets of stations and the use of a priori information on emissions. The inverse models infer total CH4 emissions of 26.8 (20.2-29.7) Tg CH4 yr-1 (mean, 10th and 90th percentiles from all inversions) for the EU-28 for 2006-2012 from the four inversion experiments. For comparison, total anthropogenic CH4 emissions reported to UNFCCC (bottom up, based on statistical data and emissions factors) amount to only 21.3 Tg CH4 yr-1 (2006) to 18.8 Tg CH4 yr-1 (2012). A potential explanation for the higher range of top-down estimates compared to bottom-up inventories could be the contribution from natural sources, such as peatlands, wetlands, and wet soils. Based on seven different wetland inventories from the Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP), total wetland emissions of 4.3 (2.3-8.2) Tg CH4 yr-1 from the EU-28 are estimated. The hypothesis of significant natural emissions is supported by the finding that several inverse models yield significant seasonal cycles of derived CH4 emissions with maxima in summer, while anthropogenic CH4 emissions are assumed to have much lower seasonal variability. Taking into account the wetland emissions from the WETCHIMP ensemble, the top-down estimates are broadly consistent with the sum of anthropogenic and natural bottom-up inventories. However, the contribution of natural sources and their regional distribution remain rather uncertain. Furthermore, we investigate potential biases in the inverse models by comparison with regular aircraft profiles at four European sites and with vertical profiles obtained during the Infrastructure for Measurement of the European Carbon
Two radiative inverse seesaw models, dark matter, and baryogenesis
International Nuclear Information System (INIS)
Baldes, Iason; Bell, Nicole F.; Petraki, Kalliopi; Volkas, Raymond R.
2013-01-01
The inverse seesaw mechanism allows the neutrino masses to be generated by new physics at an experimentally accessible scale, even with O(1) Yukawa couplings. In the inverse seesaw scenario, the smallness of neutrino masses is linked to the smallness of a lepton number violating parameter. This parameter may arise radiatively. In this paper, we study the cosmological implications of two contrasting radiative inverse seesaw models, one due to Ma and the other to Law and McDonald. The former features spontaneous, the latter explicit lepton number violation. First, we examine the effect of the lepton-number violating interactions introduced in these models on the baryon asymmetry of the universe. We investigate under what conditions a pre-existing baryon asymmetry does not get washed out. While both models allow a baryon asymmetry to survive only once the temperature has dropped below the mass of their heaviest fields, the Ma model can create the baryon asymmetry through resonant leptogenesis. Then we investigate the viability of the dark matter candidates arising within these models, and explore the prospects for direct detection. We find that the Law/McDonald model allows a simple dark matter scenario similar to the Higgs portal, while in the Ma model the simplest cold dark matter scenario would tend to overclose the universe
Non-cavitating propeller noise modeling and inversion
Kim, Dongho; Lee, Keunhwa; Seong, Woojae
2014-12-01
Marine propeller is the dominant exciter of the hull surface above it causing high level of noise and vibration in the ship structure. Recent successful developments have led to non-cavitating propeller designs and thus present focus is the non-cavitating characteristics of propeller such as hydrodynamic noise and its induced hull excitation. In this paper, analytic source model of propeller non-cavitating noise, described by longitudinal quadrupoles and dipoles, is suggested based on the propeller hydrodynamics. To find the source unknown parameters, the multi-parameter inversion technique is adopted using the pressure data obtained from the model scale experiment and pressure field replicas calculated by boundary element method. The inversion results show that the proposed source model is appropriate in modeling non-cavitating propeller noise. The result of this study can be utilized in the prediction of propeller non-cavitating noise and hull excitation at various stages in design and analysis.
Directory of Open Access Journals (Sweden)
N. Evangeliou
2017-07-01
Full Text Available This paper describes the results of an inverse modeling study for the determination of the source term of the radionuclides 134Cs, 137Cs and 131I released after the Chernobyl accident. The accident occurred on 26 April 1986 in the Former Soviet Union and released about 1019 Bq of radioactive materials that were transported as far away as the USA and Japan. Thereafter, several attempts to assess the magnitude of the emissions were made that were based on the knowledge of the core inventory and the levels of the spent fuel. More recently, when modeling tools were further developed, inverse modeling techniques were applied to the Chernobyl case for source term quantification. However, because radioactivity is a sensitive topic for the public and attracts a lot of attention, high-quality measurements, which are essential for inverse modeling, were not made available except for a few sparse activity concentration measurements far from the source and far from the main direction of the radioactive fallout. For the first time, we apply Bayesian inversion of the Chernobyl source term using not only activity concentrations but also deposition measurements from the most recent public data set. These observations refer to a data rescue attempt that started more than 10 years ago, with a final goal to provide available measurements to anyone interested. In regards to our inverse modeling results, emissions of 134Cs were estimated to be 80 PBq or 30–50 % higher than what was previously published. From the released amount of 134Cs, about 70 PBq were deposited all over Europe. Similar to 134Cs, emissions of 137Cs were estimated as 86 PBq, on the same order as previously reported results. Finally, 131I emissions of 1365 PBq were found, which are about 10 % less than the prior total releases. The inversion pushes the injection heights of the three radionuclides to higher altitudes (up to about 3 km than previously assumed (≈ 2.2 km in order
Evangeliou, Nikolaos; Hamburger, Thomas; Cozic, Anne; Balkanski, Yves; Stohl, Andreas
2017-07-01
This paper describes the results of an inverse modeling study for the determination of the source term of the radionuclides 134Cs, 137Cs and 131I released after the Chernobyl accident. The accident occurred on 26 April 1986 in the Former Soviet Union and released about 1019 Bq of radioactive materials that were transported as far away as the USA and Japan. Thereafter, several attempts to assess the magnitude of the emissions were made that were based on the knowledge of the core inventory and the levels of the spent fuel. More recently, when modeling tools were further developed, inverse modeling techniques were applied to the Chernobyl case for source term quantification. However, because radioactivity is a sensitive topic for the public and attracts a lot of attention, high-quality measurements, which are essential for inverse modeling, were not made available except for a few sparse activity concentration measurements far from the source and far from the main direction of the radioactive fallout. For the first time, we apply Bayesian inversion of the Chernobyl source term using not only activity concentrations but also deposition measurements from the most recent public data set. These observations refer to a data rescue attempt that started more than 10 years ago, with a final goal to provide available measurements to anyone interested. In regards to our inverse modeling results, emissions of 134Cs were estimated to be 80 PBq or 30-50 % higher than what was previously published. From the released amount of 134Cs, about 70 PBq were deposited all over Europe. Similar to 134Cs, emissions of 137Cs were estimated as 86 PBq, on the same order as previously reported results. Finally, 131I emissions of 1365 PBq were found, which are about 10 % less than the prior total releases. The inversion pushes the injection heights of the three radionuclides to higher altitudes (up to about 3 km) than previously assumed (≈ 2.2 km) in order to better match both concentration
Energy Technology Data Exchange (ETDEWEB)
Dobranszky, G.
2005-12-15
Stratigraphic modeling aims at rebuilding the history of the sedimentary basins by simulating the processes of erosion, transport and deposit of sediments using physical models. The objective is to determine the location of the bed-rocks likely to contain the organic matter, the location of the porous rocks that could trap the hydrocarbons during their migration and the location of the impermeable rocks likely to seal the reservoir. The model considered within this thesis is based on a multi-lithological diffusive transport model and applies to large scales of time and space. Due to the complexity of the phenomena and scales considered, none of the model parameters is directly measurable. Therefore it is essential to inverse them. The standard approach, which consists in inverting all the parameters by minimizing a cost function using a gradient method, proved very sensitive to the choice of the parameterization, to the weights given to the various terms of the cost function (hearing on data of very diverse nature) and to the numerical noise. These observations led us to give up this method and to carry out the in-version step by step by decoupling the parameters. This decoupling is not obtained by fixing the parameters but by making several assumptions on the model resulting in a range of reduced but relevant models. In this thesis, we show how these models enable us to inverse all the parameters in a robust and interactive way. (author)
Why does inverse modeling of drainage inventories work?
White, Nicky; Roberts, Gareth
2016-04-01
We describe and apply a linear inverse model which calculates spatial and temporal patterns of uplift rate by minimizing the misfit between inventories of observed and predicted longitudinal river profiles. This approach builds upon a more general, non-linear, optimization model, which suggests that shapes of river profiles are dominantly controlled by upstream advection of kinematic waves of incision produced by spatial and temporal changes in regional uplift rate. We have tested both algorithms by inverting thousands of river profiles from Africa, Eurasia, the Americas, and Australia. For each continent, the drainage network was constructed from a digital elevation model and the fidelity of river profiles extracted from this network was carefully checked using satellite imagery. Spatial and temporal patterns of both uplift rate and cumulative uplift were calibrated using independent geologic and geophysical observations. Inverse modeling of these substantial inventories of river profiles suggests that drainage networks contain coherent signals that record the regional growth of elevation. In the second part of this presentation, we use spectral analysis of river profiles to suggest why drainage networks behave in a coherent, albeit non-linear, fashion. Our analysis implies that large-scale topographic signals injected into landscapes generate spectral slopes that are usually red (i.e. Brownian). At wavelengths shorter than tens of km, spectral slopes whiten which suggests that coherent topographic signals cease to exist at these shorter length scales. Our results suggest that inverse modeling of drainage networks can reveal useful information about landscape growth through space and time.
Basic principles of forward and inverse geochemical modelization
International Nuclear Information System (INIS)
Gimeno, M.J.; Pena, J.
1994-01-01
Geochemical modeling consists in the application of thermodynamic and physicochemical principles in the hydrogeochemical systems interpretation. It has been developed following two different approaches: a) inverse modeling (or mass balance calculations), which uses observed chemical and isotopic data from waters and rocks to identify geochemical reactions responsible of them, in a quantitative way; and b) forward modeling, which attempts to predict water compositions and mass transfer that can result from hypothesized reactions, from observed initial conditions on water-rock system compositions. Both of them have intrinsic uses and limitations which drive to their use in specific problems. For systems with adequate chemical, isotopic, and mineralogic data, the inverse modeling approach of speciation and mass-balance modeling provides the most direct means of determining quantitative geochemical reaction models. In contrast, for systems with missing or inadequate data, reaction-path modeling provides an a priori method of predicting geochemical reactions. In some cases it is useful to combine forward modeling with the results from inverse models. The mass-balance results determine the net mass transfer along the flow path, but these results are only partially constrained by thermodynamics. The forward modeling can be used both, to prove thermodynamic consistency for them, and to predict water quality at points where there are no enough data. Recent advances in geochemical modeling are focused on finding the most efficient numerical procedures for coupling geochemical reactions (both equilibrium and kinetic) with the hydrodynamic transport equations in compositionally-complex systems, on uncertainty analysis, and on model validation for actual geochemical systems
Anatomy of Higgs mass in supersymmetric inverse seesaw models
Energy Technology Data Exchange (ETDEWEB)
Chun, Eung Jin, E-mail: ejchun@kias.re.kr [Korea Institute for Advanced Study, Seoul 130-722 (Korea, Republic of); Mummidi, V. Suryanarayana, E-mail: soori9@cts.iisc.ernet.in [Centre for High Energy Physics, Indian Institute of Science, Bangalore 560012 (India); Vempati, Sudhir K., E-mail: vempati@cts.iisc.ernet.in [Centre for High Energy Physics, Indian Institute of Science, Bangalore 560012 (India)
2014-09-07
We compute the one loop corrections to the CP-even Higgs mass matrix in the supersymmetric inverse seesaw model to single out the different cases where the radiative corrections from the neutrino sector could become important. It is found that there could be a significant enhancement in the Higgs mass even for Dirac neutrino masses of O(30) GeV if the left-handed sneutrino soft mass is comparable or larger than the right-handed neutrino mass. In the case where right-handed neutrino masses are significantly larger than the supersymmetry breaking scale, the corrections can utmost account to an upward shift of 3 GeV. For very heavy multi TeV sneutrinos, the corrections replicate the stop corrections at 1-loop. We further show that general gauge mediation with inverse seesaw model naturally accommodates a 125 GeV Higgs with TeV scale stops.
Effects of Induced Stress on Seismic Forward Modelling and Inversion
Tromp, Jeroen; Trampert, Jeannot
2018-01-01
We demonstrate how effects of induced stress may be incorporated in seismic modelling and inversion. Our approach is motivated by the accommodation of prestress in global seismology. Induced stress modifies both the equation of motion and the constitutive relationship. The theory predicts that induced pressure linearly affects the unstressed isotropic moduli with a slope determined by their adiabatic pressure derivatives. The induced deviatoric stress produces anisotropic compressional and shear wavespeeds; the latter result in shear-wave splitting. For forward modelling purposes, we determine the weak form of the equation of motion under induced stress. In the context of the inverse problem, we determine induced stress sensitivity kernels, which may be used for adjoint tomography. The theory is illustrated by considering 2D propagation of SH waves and related Fréchet derivatives based on a spectral-element method.
Sparse optimization for inverse problems in atmospheric modelling
Czech Academy of Sciences Publication Activity Database
Adam, Lukáš; Branda, Martin
2016-01-01
Roč. 79, č. 3 (2016), s. 256-266 ISSN 1364-8152 R&D Projects: GA MŠk(CZ) 7F14287 Institutional support: RVO:67985556 Keywords : Inverse modelling * Sparse optimization * Integer optimization * Least squares * European tracer experiment * Free Matlab codes Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 4.404, year: 2016 http://library.utia.cas.cz/separaty/2016/MTR/adam-0457037.pdf
Understanding the inverse magnetocaloric effect through a simple theoretical model
Energy Technology Data Exchange (ETDEWEB)
Ranke, P.J. von, E-mail: von.ranke@uol.com.b [Instituto de Fisica Armando Dias Tavares-Universidade do Estado do Rio de Janeiro, Rua Sao Francisco Xavier, 524, Rio de Janeiro 20550-013 (Brazil); Alho, B.P.; Nobrega, E.P.; Oliveira, N.A. de [Instituto de Fisica Armando Dias Tavares-Universidade do Estado do Rio de Janeiro, Rua Sao Francisco Xavier, 524, Rio de Janeiro 20550-013 (Brazil)
2009-10-15
We investigated the inverse magnetocaloric effect using a theoretical magnetic model formed by two coupled magnetic lattices to describe a ferrimagnetic system. The influence of the compensation temperature, and the ferrimagnetic-paramagnetic phase transition on the magnetocaloric effect was analyzed. Also, a relation between the area under the magnetocaloric curve and the net magnetic moment of a ferrimagnetic system was established in this work.
Inverse Modeling of Emissions and their Time Profiles
Czech Academy of Sciences Publication Activity Database
Resler, Jaroslav; Eben, Kryštof; Juruš, Pavel; Liczki, Jitka
2010-01-01
Roč. 1, č. 4 (2010), s. 288-295 ISSN 1309-1042 R&D Projects: GA MŽP SP/1A4/107/07 Grant - others:COST(XE) ES0602 Institutional research plan: CEZ:AV0Z10300504 Keywords : 4DVar * inverse modeling * diurnal time profile of emission * CMAQ adjoint * satellite observations Subject RIV: DG - Athmosphere Sciences, Meteorology
Efficient Stochastic Inversion Using Adjoint Models and Kernel-PCA
Energy Technology Data Exchange (ETDEWEB)
Thimmisetty, Charanraj A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Center for Applied Scientific Computing; Zhao, Wenju [Florida State Univ., Tallahassee, FL (United States). Dept. of Scientific Computing; Chen, Xiao [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Center for Applied Scientific Computing; Tong, Charles H. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Center for Applied Scientific Computing; White, Joshua A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Atmospheric, Earth and Energy Division
2017-10-18
Performing stochastic inversion on a computationally expensive forward simulation model with a high-dimensional uncertain parameter space (e.g. a spatial random field) is computationally prohibitive even when gradient information can be computed efficiently. Moreover, the ‘nonlinear’ mapping from parameters to observables generally gives rise to non-Gaussian posteriors even with Gaussian priors, thus hampering the use of efficient inversion algorithms designed for models with Gaussian assumptions. In this paper, we propose a novel Bayesian stochastic inversion methodology, which is characterized by a tight coupling between the gradient-based Langevin Markov Chain Monte Carlo (LMCMC) method and a kernel principal component analysis (KPCA). This approach addresses the ‘curse-of-dimensionality’ via KPCA to identify a low-dimensional feature space within the high-dimensional and nonlinearly correlated parameter space. In addition, non-Gaussian posterior distributions are estimated via an efficient LMCMC method on the projected low-dimensional feature space. We will demonstrate this computational framework by integrating and adapting our recent data-driven statistics-on-manifolds constructions and reduction-through-projection techniques to a linear elasticity model.
Computer modeling of inversion layer MOS solar cells and arrays
Ho, Fat Duen
1991-02-01
A two dimensional numerical model of the inversion layer metal insulator semiconductor (IL/MIS) solar cell is proposed by using the finite element method. The two-dimensional current flow in the device is taken into account in this model. The electrostatic potential distribution, the electron concentration distribution, and the hole concentration distribution for different terminal voltages are simulated. The results of simple calculation are presented. The existing problems for this model are addressed. Future work is proposed. The MIS structures are studied and some of the results are reported.
Inverse modeling of biomass smoke emissions using the TOMS AI
Zhang, S. Y.; Penner, J. E.; Torres, O.
2003-12-01
Results of inverse modeling of biomass smoke emissions using the TOMS AI and a three-dimensional transport model are presented. The IMPACT model with DAO meteorology data in 1997 are utilized to obtain aerosol spatial and temporal distributions. Two absorbing aerosol types are considered, including biomass smoke and mineral dust. First, a radiative transfer model is applied to generate the modeled AI. Then a Bayesian inverse technique is applied to optimize the difference between the modeled AI and the EP TOMS AI in the same period by regulating monthly a priori biomass smoke emissions, while the dust emissions are fixed. The modeled AI with a posteriori emissions generally is in better agreement with the EP TOMS AI. The annual global a posteriori source increases by about 13% for the year 1997 (6.31 Tg/yr BC) in the base scenario, with a larger adjustment of monthly regional emissions. Five sensitivity scenarios are carried out, including sensitivity to the a priori uncertainties, the height of the smoke layer, the cloud screening criteria of the daily EP TOMS AI, the adjustment of emissions in a lumped region outside of the major biomass burning regions, and the covariances between observations. Results suggest that a posteriori annual global emissions in the sensitivity scenarios are within 15% of that of the base scenario. However, the difference of annual a posteriori emissions between the sensitivity scenarios and the base scenario can be as large as 50% on regional scale. We are also applying the inverse model technique to the year 2000 to compare with biomass emissions deduced from an analysis based on burned areas.
Directory of Open Access Journals (Sweden)
Hudes Mark
2008-12-01
Full Text Available Abstract Background The prevalence of hypertension and its contribution to cardiovascular disease risk makes it imperative to identify factors that may help prevent this disorder. Extensive biological and biochemical data suggest that plasma ascorbic acid may be such a factor. In this study we examined the association between plasma ascorbic acid concentration and blood pressure (BP in young-adult women. Methods Participants were 242 Black and White women aged 18–21 yr from the Richmond, CA, cohort of the National Heart, Lung and Blood Institute Growth and Health Study. We examined the associations of plasma ascorbic acid with BP at follow-up year 10, and with change in BP during the previous year. Results In cross-sectional analysis, plasma ascorbic acid at year 10 was inversely associated with systolic BP and diastolic BP after adjusting for race, body mass index, education, and dietary intake of fat and sodium. Persons in the highest one-fourth of the plasma ascorbic acid distribution had 4.66 mmHg lower systolic BP (95% CI 1.10 to 8.22 mmHg, p = 0.005 and 6.04 mmHg lower diastolic BP (95% CI 2.70 to 9.38 mmHg, p = 0.0002 than those in the lowest one-fourth of the distribution. In analysis of the change in BP, plasma ascorbic acid was also inversely associated with change in systolic BP and diastolic BP during the previous year. While diastolic blood pressure among persons in the lowest quartile of plasma ascorbic acid increased by 5.97 mmHg (95% CI 3.82 to 8.13 mmHg from year 9 to year 10, those in the highest quartile of plasma vitamin C increased by only 0.23 mmHg (95% CI -1.90 to +2.36 mmHg (test for linear trend: p Conclusion Plasma ascorbic acid was found to be inversely associated with BP and change in BP during the prior year. The findings suggest the possibility that vitamin C may influence BP in healthy young adults. Since lower BP in young adulthood may lead to lower BP and decreased incidence of age-associated vascular events in
Inverse Analysis and Modeling for Tunneling Thrust on Shield Machine
Directory of Open Access Journals (Sweden)
Qian Zhang
2013-01-01
Full Text Available With the rapid development of sensor and detection technologies, measured data analysis plays an increasingly important role in the design and control of heavy engineering equipment. The paper proposed a method for inverse analysis and modeling based on mass on-site measured data, in which dimensional analysis and data mining techniques were combined. The method was applied to the modeling of the tunneling thrust on shield machines and an explicit expression for thrust prediction was established. Combined with on-site data from a tunneling project in China, the inverse identification of model coefficients was carried out using the multiple regression method. The model residual was analyzed by statistical methods. By comparing the on-site data and the model predicted results in the other two projects with different tunneling conditions, the feasibility of the model was discussed. The work may provide a scientific basis for the rational design and control of shield tunneling machines and also a new way for mass on-site data analysis of complex engineering systems with nonlinear, multivariable, time-varying characteristics.
Joint Inversion Modelling of Geophysical Data From Lough Neagh Basin
Vozar, J.; Moorkamp, M.; Jones, A. G.; Rath, V.; Muller, M. R.
2015-12-01
Multi-dimensional modelling of geophysical data collected in the Lough Neagh Basin is presented in the frame of the IRETHERM project. The Permo-Triassic Lough Neagh Basin, situated in the southeastern part of Northern Ireland, exhibits elevated geothermal gradient (~30 °C/km) in the exploratory drilled boreholes. This is taken to indicate good geothermal exploitation potential in the Sherwood Sandstone aquifer for heating, and possibly even electricity production, purposes. We have used a 3-D joint inversion framework for modelling the magnetotelluric (MT) and gravity data collected to the north of the Lough Neagh to derive robust subsurface geological models. Comprehensive supporting geophysical and geological data (e.g. borehole logs and reflection seismic images) have been used in order to analyze and model the MT and gravity data. The geophysical data sets were provided by the Geological Survey of Northern Ireland (GSNI). Considering correct objective function weighting in favor of noise-free MT response functions is particularly important in joint inversion. There is no simple way how to correct distortion effects the 3-D responses as can be done in 1-D or 2-D case. We have used the Tellus Project airborne EM data to constrain magnetotelluric data and correct them for near surface effects. The shallow models from airborne data are used to constrain the uppermost part of 3-D inversion model. Preliminary 3-D joint inversion modeling reveals that the Sherwood Sandstone Group and the Permian Sandstone Formation are imaged as a conductive zone at the depth range of 500 m to 2000 m with laterally varying thickness, depth, and conductance. The conductive target sediments become shallower and thinner to the north and they are laterally continuous. To obtain better characterization of thermal transport properties of investigated area we used porosity and resistivity data from the Annaghmore and Ballymacilroy boreholes to estimate the relations between porosity
A nonlinear inversion for the velocity background and perturbation models
Wu, Zedong
2015-08-19
Reflected waveform inversion (RWI) provides a method to reduce the nonlinearity of the standard full waveform inversion (FWI) by inverting for the single scattered wavefield obtained using an image. However, current RWI methods usually neglect diving waves, which is an important source of information for extracting the long wavelength components of the velocity model. Thus, we propose a new optimization problem through breaking the velocity model into the background and the perturbation in the wave equation directly. In this case, the perturbed model is no longer the single scattering model, but includes all scattering. We optimize both components simultaneously, and thus, the objective function is nonlinear with respect to both the background and perturbation. The new introduced w can absorb the non-smooth update of background naturally. Application to the Marmousi model with frequencies that start at 5 Hz shows that this method can converge to the accurate velocity starting from a linearly increasing initial velocity. Application to the SEG2014 demonstrates the versatility of the approach.
Incorporating modelled subglacial hydrology into inversions for basal drag
Koziol, Conrad P.; Arnold, Neil
2017-12-01
A key challenge in modelling coupled ice-flow-subglacial hydrology is initializing the state and parameters of the system. We address this problem by presenting a workflow for initializing these values at the start of a summer melt season. The workflow depends on running a subglacial hydrology model for the winter season, when the system is not forced by meltwater inputs, and ice velocities can be assumed constant. Key parameters of the winter run of the subglacial hydrology model are determined from an initial inversion for basal drag using a linear sliding law. The state of the subglacial hydrology model at the end of winter is incorporated into an inversion of basal drag using a non-linear sliding law which is a function of water pressure. We demonstrate this procedure in the Russell Glacier area and compare the output of the linear sliding law with two non-linear sliding laws. Additionally, we compare the modelled winter hydrological state to radar observations and find that it is in line with summer rather than winter observations.
Incorporating modelled subglacial hydrology into inversions for basal drag
Directory of Open Access Journals (Sweden)
C. P. Koziol
2017-12-01
Full Text Available A key challenge in modelling coupled ice-flow–subglacial hydrology is initializing the state and parameters of the system. We address this problem by presenting a workflow for initializing these values at the start of a summer melt season. The workflow depends on running a subglacial hydrology model for the winter season, when the system is not forced by meltwater inputs, and ice velocities can be assumed constant. Key parameters of the winter run of the subglacial hydrology model are determined from an initial inversion for basal drag using a linear sliding law. The state of the subglacial hydrology model at the end of winter is incorporated into an inversion of basal drag using a non-linear sliding law which is a function of water pressure. We demonstrate this procedure in the Russell Glacier area and compare the output of the linear sliding law with two non-linear sliding laws. Additionally, we compare the modelled winter hydrological state to radar observations and find that it is in line with summer rather than winter observations.
Goal Directed Model Inversion: A Study of Dynamic Behavior
Colombano, Silvano P.; Compton, Michael; Raghavan, Bharathi; Lum, Henry, Jr. (Technical Monitor)
1994-01-01
Goal Directed Model Inversion (GDMI) is an algorithm designed to generalize supervised learning to the case where target outputs are not available to the learning system. The output of the learning system becomes the input to some external device or transformation, and only the output of this device or transformation can be compared to a desired target. The fundamental driving mechanism of GDMI is to learn from success. Given that a wrong outcome is achieved, one notes that the action that produced that outcome 0 "would have been right if the outcome had been the desired one." The algorithm then proceeds as follows: (1) store the action that produced the wrong outcome as a "target" (2) redefine the wrong outcome as a desired goal (3) submit the new desired goal to the system (4) compare the new action with the target action and modify the system by using a suitable algorithm for credit assignment (Back propagation in our example) (5) resubmit the original goal. Prior publications by our group in this area focused on demonstrating empirical results based on the inverse kinematic problem for a simulated robotic arm. In this paper we apply the inversion process to much simpler analytic functions in order to elucidate the dynamic behavior of the system and to determine the sensitivity of the learning process to various parameters. This understanding will be necessary for the acceptance of GDMI as a practical tool.
Alloy design as an inverse problem of cluster expansion models
DEFF Research Database (Denmark)
Larsen, Peter Mahler; Kalidindi, Arvind R.; Schmidt, Søren
2017-01-01
Central to a lattice model of an alloy system is the description of the energy of a given atomic configuration, which can be conveniently developed through a cluster expansion. Given a specific cluster expansion, the ground state of the lattice model at 0 K can be solved by finding the configurat......Central to a lattice model of an alloy system is the description of the energy of a given atomic configuration, which can be conveniently developed through a cluster expansion. Given a specific cluster expansion, the ground state of the lattice model at 0 K can be solved by finding...... the inverse problem in terms of energetically distinct configurations, using a constraint satisfaction model to identify constructible configurations, and show that a convex hull can be used to identify ground states. To demonstrate the approach, we solve for all ground states for a binary alloy in a 2D...
Inverse geothermal modelling applied to Danish sedimentary basins
Poulsen, Søren E.; Balling, Niels; Bording, Thue S.; Mathiesen, Anders; Nielsen, Søren B.
2017-10-01
This paper presents a numerical procedure for predicting subsurface temperatures and heat-flow distribution in 3-D using inverse calibration methodology. The procedure is based on a modified version of the groundwater code MODFLOW by taking advantage of the mathematical similarity between confined groundwater flow (Darcy's law) and heat conduction (Fourier's law). Thermal conductivity, heat production and exponential porosity-depth relations are specified separately for the individual geological units of the model domain. The steady-state temperature model includes a model-based transient correction for the long-term palaeoclimatic thermal disturbance of the subsurface temperature regime. Variable model parameters are estimated by inversion of measured borehole temperatures with uncertainties reflecting their quality. The procedure facilitates uncertainty estimation for temperature predictions. The modelling procedure is applied to Danish onshore areas containing deep sedimentary basins. A 3-D voxel-based model, with 14 lithological units from surface to 5000 m depth, was built from digital geological maps derived from combined analyses of reflection seismic lines and borehole information. Matrix thermal conductivity of model lithologies was estimated by inversion of all available deep borehole temperature data and applied together with prescribed background heat flow to derive the 3-D subsurface temperature distribution. Modelled temperatures are found to agree very well with observations. The numerical model was utilized for predicting and contouring temperatures at 2000 and 3000 m depths and for two main geothermal reservoir units, the Gassum (Lower Jurassic-Upper Triassic) and Bunter/Skagerrak (Triassic) reservoirs, both currently utilized for geothermal energy production. Temperature gradients to depths of 2000-3000 m are generally around 25-30 °C km-1, locally up to about 35 °C km-1. Large regions have geothermal reservoirs with characteristic temperatures
Stochastic reduced order models for inverse problems under uncertainty.
Warner, James E; Aquino, Wilkins; Grigoriu, Mircea D
2015-03-01
This work presents a novel methodology for solving inverse problems under uncertainty using stochastic reduced order models (SROMs). Given statistical information about an observed state variable in a system, unknown parameters are estimated probabilistically through the solution of a model-constrained, stochastic optimization problem. The point of departure and crux of the proposed framework is the representation of a random quantity using a SROM - a low dimensional, discrete approximation to a continuous random element that permits e cient and non-intrusive stochastic computations. Characterizing the uncertainties with SROMs transforms the stochastic optimization problem into a deterministic one. The non-intrusive nature of SROMs facilitates e cient gradient computations for random vector unknowns and relies entirely on calls to existing deterministic solvers. Furthermore, the method is naturally extended to handle multiple sources of uncertainty in cases where state variable data, system parameters, and boundary conditions are all considered random. The new and widely-applicable SROM framework is formulated for a general stochastic optimization problem in terms of an abstract objective function and constraining model. For demonstration purposes, however, we study its performance in the specific case of inverse identification of random material parameters in elastodynamics. We demonstrate the ability to efficiently recover random shear moduli given material displacement statistics as input data. We also show that the approach remains effective for the case where the loading in the problem is random as well.
Inverse modeling with RZWQM2 to predict water quality
Nolan, Bernard T.; Malone, Robert W.; Ma, Liwang; Green, Christopher T.; Fienen, Michael N.; Jaynes, Dan B.
2011-01-01
This chapter presents guidelines for autocalibration of the Root Zone Water Quality Model (RZWQM2) by inverse modeling using PEST parameter estimation software (Doherty, 2010). Two sites with diverse climate and management were considered for simulation of N losses by leaching and in drain flow: an almond [Prunus dulcis (Mill.) D.A. Webb] orchard in the San Joaquin Valley, California and the Walnut Creek watershed in central Iowa, which is predominantly in corn (Zea mays L.)–soybean [Glycine max (L.) Merr.] rotation. Inverse modeling provides an objective statistical basis for calibration that involves simultaneous adjustment of model parameters and yields parameter confidence intervals and sensitivities. We describe operation of PEST in both parameter estimation and predictive analysis modes. The goal of parameter estimation is to identify a unique set of parameters that minimize a weighted least squares objective function, and the goal of predictive analysis is to construct a nonlinear confidence interval for a prediction of interest by finding a set of parameters that maximizes or minimizes the prediction while maintaining the model in a calibrated state. We also describe PEST utilities (PAR2PAR, TSPROC) for maintaining ordered relations among model parameters (e.g., soil root growth factor) and for post-processing of RZWQM2 outputs representing different cropping practices at the Iowa site. Inverse modeling provided reasonable fits to observed water and N fluxes and directly benefitted the modeling through: (i) simultaneous adjustment of multiple parameters versus one-at-a-time adjustment in manual approaches; (ii) clear indication by convergence criteria of when calibration is complete; (iii) straightforward detection of nonunique and insensitive parameters, which can affect the stability of PEST and RZWQM2; and (iv) generation of confidence intervals for uncertainty analysis of parameters and model predictions. Composite scaled sensitivities, which
Directory of Open Access Journals (Sweden)
Alexander eHanuschkin
2013-06-01
Full Text Available Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop a correlation-based theory of interactions between a sensory and a motor area. We show that a simple eligibility-weighted Hebbian learning rule, operating within a sensorimotor loop during motor explorations and stabilized by heterosynaptic competition, naturally gives rise to mirror neurons as well as control theoretic inverse models encoded in the synaptic weights from sensory to motor neurons. Crucially, we find that the correlational structure or stereotypy of the neural code underlying motor explorations determines the nature of the learned inverse model: Random motor codes lead to causal inverses that map sensory activity patterns to their motor causes; such inverses are maximally useful, they allow for imitating arbitrary sensory target sequences. By contrast, stereotyped motor codes lead to less useful predictive inverses that map sensory activity to future motor actions.Our theory generalizes previous work on inverse models by showing that such models can be learned in a simple Hebbian framework without the need for error signals or backpropagation, and it makes new conceptual connections between the causal nature of inverse models, the statistical structure of motor variability, and the time-lag between sensory and motor responses of mirror neurons. Applied to bird song learning, our theory can account for puzzling aspects of the song system, including necessity of sensorimotor gating and selectivity of auditory responses to bird’s own song
Hanuschkin, A; Ganguli, S; Hahnloser, R H R
2013-01-01
Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map-desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop a correlation-based theory of interactions between a sensory and a motor area. We show that a simple eligibility-weighted Hebbian learning rule, operating within a sensorimotor loop during motor explorations and stabilized by heterosynaptic competition, naturally gives rise to mirror neurons as well as control theoretic inverse models encoded in the synaptic weights from sensory to motor neurons. Crucially, we find that the correlational structure or stereotypy of the neural code underlying motor explorations determines the nature of the learned inverse model: random motor codes lead to causal inverses that map sensory activity patterns to their motor causes; such inverses are maximally useful, by allowing the imitation of arbitrary sensory target sequences. By contrast, stereotyped motor codes lead to less useful predictive inverses that map sensory activity to future motor actions. Our theory generalizes previous work on inverse models by showing that such models can be learned in a simple Hebbian framework without the need for error signals or backpropagation, and it makes new conceptual connections between the causal nature of inverse models, the statistical structure of motor variability, and the time-lag between sensory and motor responses of mirror neurons. Applied to bird song learning, our theory can account for puzzling aspects of the song system, including necessity of sensorimotor gating and selectivity of auditory responses to bird's own song (BOS) stimuli.
Sneutrino dark matter in gauged inverse seesaw models for neutrinos.
An, Haipeng; Dev, P S Bhupal; Cai, Yi; Mohapatra, R N
2012-02-24
Extending the minimal supersymmetric standard model to explain small neutrino masses via the inverse seesaw mechanism can lead to a new light supersymmetric scalar partner which can play the role of inelastic dark matter (IDM). It is a linear combination of the superpartners of the neutral fermions in the theory (the light left-handed neutrino and two heavy standard model singlet neutrinos) which can be very light with mass in ~5-20 GeV range, as suggested by some current direct detection experiments. The IDM in this class of models has keV-scale mass splitting, which is intimately connected to the small Majorana masses of neutrinos. We predict the differential scattering rate and annual modulation of the IDM signal which can be testable at future germanium- and xenon-based detectors.
A new inverse regression model applied to radiation biodosimetry
Higueras, Manuel; Puig, Pedro; Ainsbury, Elizabeth A.; Rothkamm, Kai
2015-01-01
Biological dosimetry based on chromosome aberration scoring in peripheral blood lymphocytes enables timely assessment of the ionizing radiation dose absorbed by an individual. Here, new Bayesian-type count data inverse regression methods are introduced for situations where responses are Poisson or two-parameter compound Poisson distributed. Our Poisson models are calculated in a closed form, by means of Hermite and negative binomial (NB) distributions. For compound Poisson responses, complete and simplified models are provided. The simplified models are also expressible in a closed form and involve the use of compound Hermite and compound NB distributions. Three examples of applications are given that demonstrate the usefulness of these methodologies in cytogenetic radiation biodosimetry and in radiotherapy. We provide R and SAS codes which reproduce these examples. PMID:25663804
Irrigation Requirement Estimation Using Vegetation Indices and Inverse Biophysical Modeling
Bounoua, Lahouari; Imhoff, Marc L.; Franks, Shannon
2010-01-01
We explore an inverse biophysical modeling process forced by satellite and climatological data to quantify irrigation requirements in semi-arid agricultural areas. We constrain the carbon and water cycles modeled under both equilibrium, balance between vegetation and climate, and non-equilibrium, water added through irrigation. We postulate that the degree to which irrigated dry lands vary from equilibrium climate conditions is related to the amount of irrigation. The amount of water required over and above precipitation is considered as an irrigation requirement. For July, results show that spray irrigation resulted in an additional amount of water of 1.3 mm per occurrence with a frequency of 24.6 hours. In contrast, the drip irrigation required only 0.6 mm every 45.6 hours or 46% of that simulated by the spray irrigation. The modeled estimates account for 87% of the total reported irrigation water use, when soil salinity is not important and 66% in saline lands.
Incorporating model parameter uncertainty into inverse treatment planning
International Nuclear Information System (INIS)
Lian Jun; Xing Lei
2004-01-01
Radiobiological treatment planning depends not only on the accuracy of the models describing the dose-response relation of different tumors and normal tissues but also on the accuracy of tissue specific radiobiological parameters in these models. Whereas the general formalism remains the same, different sets of model parameters lead to different solutions and thus critically determine the final plan. Here we describe an inverse planning formalism with inclusion of model parameter uncertainties. This is made possible by using a statistical analysis-based frameset developed by our group. In this formalism, the uncertainties of model parameters, such as the parameter a that describes tissue-specific effect in the equivalent uniform dose (EUD) model, are expressed by probability density function and are included in the dose optimization process. We found that the final solution strongly depends on distribution functions of the model parameters. Considering that currently available models for computing biological effects of radiation are simplistic, and the clinical data used to derive the models are sparse and of questionable quality, the proposed technique provides us with an effective tool to minimize the effect caused by the uncertainties in a statistical sense. With the incorporation of the uncertainties, the technique has potential for us to maximally utilize the available radiobiology knowledge for better IMRT treatment
An inverse problem for a mathematical model of aquaponic agriculture
Bobak, Carly; Kunze, Herb
2017-01-01
Aquaponic agriculture is a sustainable ecosystem that relies on a symbiotic relationship between fish and macrophytes. While the practice has been growing in popularity, relatively little mathematical models exist which aim to study the system processes. In this paper, we present a system of ODEs which aims to mathematically model the population and concetrations dynamics present in an aquaponic environment. Values of the parameters in the system are estimated from the literature so that simulated results can be presented to illustrate the nature of the solutions to the system. As well, a brief sensitivity analysis is performed in order to identify redundant parameters and highlight those which may need more reliable estimates. Specifically, an inverse problem with manufactured data for fish and plants is presented to demonstrate the ability of the collage theorem to recover parameter estimates.
Bayesian inversion using a geologically realistic and discrete model space
Jaeggli, C.; Julien, S.; Renard, P.
2017-12-01
Since the early days of groundwater modeling, inverse methods play a crucial role. Many research and engineering groups aim to infer extensive knowledge of aquifer parameters from a sparse set of observations. Despite decades of dedicated research on this topic, there are still several major issues to be solved. In the hydrogeological framework, one is often confronted with underground structures that present very sharp contrasts of geophysical properties. In particular, subsoil structures such as karst conduits, channels, faults, or lenses, strongly influence groundwater flow and transport behavior of the underground. For this reason it can be essential to identify their location and shape very precisely. Unfortunately, when inverse methods are specially trained to consider such complex features, their computation effort often becomes unaffordably high. The following work is an attempt to solve this dilemma. We present a new method that is, in some sense, a compromise between the ergodicity of Markov chain Monte Carlo (McMC) methods and the efficient handling of data by the ensemble based Kalmann filters. The realistic and complex random fields are generated by a Multiple-Point Statistics (MPS) tool. Nonetheless, it is applicable with any conditional geostatistical simulation tool. Furthermore, the algorithm is independent of any parametrization what becomes most important when two parametric systems are equivalent (permeability and resistivity, speed and slowness, etc.). When compared to two existing McMC schemes, the computational effort was divided by a factor of 12.
NACP Regional: Gridded 1-deg Observation Data and Biosphere and Inverse Model Outputs
National Aeronautics and Space Administration — ABSTRACT: This data set contains standardized gridded observation data, terrestrial biosphere model output data, and inverse model simulations of carbon flux...
NACP Regional: Gridded 1-deg Observation Data and Biosphere and Inverse Model Outputs
National Aeronautics and Space Administration — This data set contains standardized gridded observation data, terrestrial biosphere model output data, and inverse model simulations of carbon flux parameters that...
NACP Regional: Original Observation Data and Biosphere and Inverse Model Outputs
National Aeronautics and Space Administration — This data set contains the originally-submitted observation measurement data, terrestrial biosphere model output data, and inverse model simulations that various...
NACP Regional: Original Observation Data and Biosphere and Inverse Model Outputs
National Aeronautics and Space Administration — ABSTRACT: This data set contains the originally-submitted observation measurement data, terrestrial biosphere model output data, and inverse model simulations that...
Anthropomorphic Coding of Speech and Audio: A Model Inversion Approach
Directory of Open Access Journals (Sweden)
W. Bastiaan Kleijn
2005-06-01
Full Text Available Auditory modeling is a well-established methodology that provides insight into human perception and that facilitates the extraction of signal features that are most relevant to the listener. The aim of this paper is to provide a tutorial on perceptual speech and audio coding using an invertible auditory model. In this approach, the audio signal is converted into an auditory representation using an invertible auditory model. The auditory representation is quantized and coded. Upon decoding, it is then transformed back into the acoustic domain. This transformation converts a complex distortion criterion into a simple one, thus facilitating quantization with low complexity. We briefly review past work on auditory models and describe in more detail the components of our invertible model and its inversion procedure, that is, the method to reconstruct the signal from the output of the auditory model. We summarize attempts to use the auditory representation for low-bit-rate coding. Our approach also allows the exploitation of the inherent redundancy of the human auditory system for the purpose of multiple description (joint source-channel coding.
Koepke, C.; Irving, J.; Roubinet, D.
2014-12-01
Geophysical methods have gained much interest in hydrology over the past two decades because of their ability to provide estimates of the spatial distribution of subsurface properties at a scale that is often relevant to key hydrological processes. Because of an increased desire to quantify uncertainty in hydrological predictions, many hydrogeophysical inverse problems have recently been posed within a Bayesian framework, such that estimates of hydrological properties and their corresponding uncertainties can be obtained. With the Bayesian approach, it is often necessary to make significant approximations to the associated hydrological and geophysical forward models such that stochastic sampling from the posterior distribution, for example using Markov-chain-Monte-Carlo (MCMC) methods, is computationally feasible. These approximations lead to model structural errors, which, so far, have not been properly treated in hydrogeophysical inverse problems. Here, we study the inverse problem of estimating unsaturated hydraulic properties, namely the van Genuchten-Mualem (VGM) parameters, in a layered subsurface from time-lapse, zero-offset-profile (ZOP) ground penetrating radar (GPR) data, collected over the course of an infiltration experiment. In particular, we investigate the effects of assumptions made for computational tractability of the stochastic inversion on model prediction errors as a function of depth and time. These assumptions are that (i) infiltration is purely vertical and can be modeled by the 1D Richards equation, and (ii) the petrophysical relationship between water content and relative dielectric permittivity is known. Results indicate that model errors for this problem are far from Gaussian and independently identically distributed, which has been the common assumption in previous efforts in this domain. In order to develop a more appropriate likelihood formulation, we use (i) a stochastic description of the model error that is obtained through
Amalia, Junita; Purhadi, Otok, Bambang Widjanarko
2017-11-01
Poisson distribution is a discrete distribution with count data as the random variables and it has one parameter defines both mean and variance. Poisson regression assumes mean and variance should be same (equidispersion). Nonetheless, some case of the count data unsatisfied this assumption because variance exceeds mean (over-dispersion). The ignorance of over-dispersion causes underestimates in standard error. Furthermore, it causes incorrect decision in the statistical test. Previously, paired count data has a correlation and it has bivariate Poisson distribution. If there is over-dispersion, modeling paired count data is not sufficient with simple bivariate Poisson regression. Bivariate Poisson Inverse Gaussian Regression (BPIGR) model is mix Poisson regression for modeling paired count data within over-dispersion. BPIGR model produces a global model for all locations. In another hand, each location has different geographic conditions, social, cultural and economic so that Geographically Weighted Regression (GWR) is needed. The weighting function of each location in GWR generates a different local model. Geographically Weighted Bivariate Poisson Inverse Gaussian Regression (GWBPIGR) model is used to solve over-dispersion and to generate local models. Parameter estimation of GWBPIGR model obtained by Maximum Likelihood Estimation (MLE) method. Meanwhile, hypothesis testing of GWBPIGR model acquired by Maximum Likelihood Ratio Test (MLRT) method.
UCODE, a computer code for universal inverse modeling
Poeter, Eileen P.; Hill, Mary C.
1999-05-01
This article presents the US Geological Survey computer program UCODE, which was developed in collaboration with the US Army Corps of Engineers Waterways Experiment Station and the International Ground Water Modeling Center of the Colorado School of Mines. UCODE performs inverse modeling, posed as a parameter-estimation problem, using nonlinear regression. Any application model or set of models can be used; the only requirement is that they have numerical (ASCII or text only) input and output files and that the numbers in these files have sufficient significant digits. Application models can include preprocessors and postprocessors as well as models related to the processes of interest (physical, chemical and so on), making UCODE extremely powerful for model calibration. Estimated parameters can be defined flexibly with user-specified functions. Observations to be matched in the regression can be any quantity for which a simulated equivalent value can be produced, thus simulated equivalent values are calculated using values that appear in the application model output files and can be manipulated with additive and multiplicative functions, if necessary. Prior, or direct, information on estimated parameters also can be included in the regression. The nonlinear regression problem is solved by minimizing a weighted least-squares objective function with respect to the parameter values using a modified Gauss-Newton method. Sensitivities needed for the method are calculated approximately by forward or central differences and problems and solutions related to this approximation are discussed. Statistics are calculated and printed for use in (1) diagnosing inadequate data or identifying parameters that probably cannot be estimated with the available data, (2) evaluating estimated parameter values, (3) evaluating the model representation of the actual processes and (4) quantifying the uncertainty of model simulated values. UCODE is intended for use on any computer operating
Determination of hydraulic properties of unsaturated soil via inverse modeling
International Nuclear Information System (INIS)
Kodesova, R.
2004-01-01
The method for determining the hydraulic properties of unsaturated soil with inverse modeling is presented. A modified cone penetrometer has been designed to inject water into the soil through a screen, and measure the progress of the wetting front with two tensiometer rings positioned above the screen. Cumulative inflow and pressure head readings are analyzed to obtain estimates of the hydraulic parameters describing K(h) and θ(h). Optimization results for tests at one side are used to demonstrate the possibility to evaluate either the wetting branches of the soil hydraulic properties, or the wetting and drying curves simultaneously, via analysis of different parts of the experiment. The optimization results are compared to the results of standard laboratory and field methods. (author)
Unified dark energy-dark matter model with inverse quintessence
International Nuclear Information System (INIS)
Ansoldi, Stefano; Guendelman, Eduardo I.
2013-01-01
We consider a model where both dark energy and dark matter originate from the coupling of a scalar field with a non-canonical kinetic term to, both, a metric measure and a non-metric measure. An interacting dark energy/dark matter scenario can be obtained by introducing an additional scalar that can produce non constant vacuum energy and associated variations in dark matter. The phenomenology is most interesting when the kinetic term of the additional scalar field is ghost-type, since in this case the dark energy vanishes in the early universe and then grows with time. This constitutes an ''inverse quintessence scenario'', where the universe starts from a zero vacuum energy density state, instead of approaching it in the future
Locatelli, R.; Bousquet, P.; Chevallier, F.; Fortems-Cheney, A.; Szopa, S.; Saunois, M.; Agusti-Panareda, A.; Bergmann, D.; Bian, H.; Cameron-Smith, P.; Chipperfield, M.P.; Gloor, E.; Houweling, S.; Kawa, S.R.; Krol, M.C.; Patra, P.K.; Prinn, R.G.; Rigby, M.; Saito, R.; Wilson, C.
2013-01-01
A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained from 10 different model outputs from the international TransCom-CH4 model inter-comparison exercise,
Locatelli, R.; Bousquet, P.; Chevallier, F.; Fortems-Cheney, A.; Szopa, S.; Saunois, M.; Agusti-Panareda, A.; Bergmann, D.; Bian, H.; Cameron-Smith, P.; Chipperfield, M. P.; Gloor, E.; Houweling, S.; Kawa, S. R.; Krol, M.; Patra, P. K.; Prinn, R. G.; Rigby, M.; Saito, R.; Wilson, C.
2013-10-01
A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained from 10 different model outputs from the international TransCom-CH4 model inter-comparison exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the three-component PYVAR-LMDZ-SACS (PYthon VARiational-Laboratoire de Météorologie Dynamique model with Zooming capability-Simplified Atmospheric Chemistry System) inversion system to produce 10 different methane emission estimates at the global scale for the year 2005. The same methane sinks, emissions and initial conditions have been applied to produce the 10 synthetic observation datasets. The same inversion set-up (statistical errors, prior emissions, inverse procedure) is then applied to derive flux estimates by inverse modelling. Consequently, only differences in the modelling of atmospheric transport may cause differences in the estimated fluxes. In our framework, we show that transport model errors lead to a discrepancy of 27 Tg yr-1 at the global scale, representing 5% of total methane emissions. At continental and annual scales, transport model errors are proportionally larger than at the global scale, with errors ranging from 36 Tg yr-1 in North America to 7 Tg yr-1 in Boreal Eurasia (from 23 to 48%, respectively). At the model grid-scale, the spread of inverse estimates can reach 150% of the prior flux. Therefore, transport model errors contribute significantly to overall uncertainties in emission estimates by inverse modelling, especially when small spatial scales are examined. Sensitivity tests have been carried out to estimate the impact of the measurement network and the advantage of higher horizontal resolution in transport models. The large differences found between methane flux estimates inferred in these different configurations highly question the consistency of
Directory of Open Access Journals (Sweden)
R. Locatelli
2013-10-01
Full Text Available A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained from 10 different model outputs from the international TransCom-CH4 model inter-comparison exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the three-component PYVAR-LMDZ-SACS (PYthon VARiational-Laboratoire de Météorologie Dynamique model with Zooming capability-Simplified Atmospheric Chemistry System inversion system to produce 10 different methane emission estimates at the global scale for the year 2005. The same methane sinks, emissions and initial conditions have been applied to produce the 10 synthetic observation datasets. The same inversion set-up (statistical errors, prior emissions, inverse procedure is then applied to derive flux estimates by inverse modelling. Consequently, only differences in the modelling of atmospheric transport may cause differences in the estimated fluxes. In our framework, we show that transport model errors lead to a discrepancy of 27 Tg yr−1 at the global scale, representing 5% of total methane emissions. At continental and annual scales, transport model errors are proportionally larger than at the global scale, with errors ranging from 36 Tg yr−1 in North America to 7 Tg yr−1 in Boreal Eurasia (from 23 to 48%, respectively. At the model grid-scale, the spread of inverse estimates can reach 150% of the prior flux. Therefore, transport model errors contribute significantly to overall uncertainties in emission estimates by inverse modelling, especially when small spatial scales are examined. Sensitivity tests have been carried out to estimate the impact of the measurement network and the advantage of higher horizontal resolution in transport models. The large differences found between methane flux estimates inferred in these different configurations highly
Temporal rainfall estimation using input data reduction and model inversion
Wright, A. J.; Vrugt, J. A.; Walker, J. P.; Pauwels, V. R. N.
2016-12-01
Floods are devastating natural hazards. To provide accurate, precise and timely flood forecasts there is a need to understand the uncertainties associated with temporal rainfall and model parameters. The estimation of temporal rainfall and model parameter distributions from streamflow observations in complex dynamic catchments adds skill to current areal rainfall estimation methods, allows for the uncertainty of rainfall input to be considered when estimating model parameters and provides the ability to estimate rainfall from poorly gauged catchments. Current methods to estimate temporal rainfall distributions from streamflow are unable to adequately explain and invert complex non-linear hydrologic systems. This study uses the Discrete Wavelet Transform (DWT) to reduce rainfall dimensionality for the catchment of Warwick, Queensland, Australia. The reduction of rainfall to DWT coefficients allows the input rainfall time series to be simultaneously estimated along with model parameters. The estimation process is conducted using multi-chain Markov chain Monte Carlo simulation with the DREAMZS algorithm. The use of a likelihood function that considers both rainfall and streamflow error allows for model parameter and temporal rainfall distributions to be estimated. Estimation of the wavelet approximation coefficients of lower order decomposition structures was able to estimate the most realistic temporal rainfall distributions. These rainfall estimates were all able to simulate streamflow that was superior to the results of a traditional calibration approach. It is shown that the choice of wavelet has a considerable impact on the robustness of the inversion. The results demonstrate that streamflow data contains sufficient information to estimate temporal rainfall and model parameter distributions. The extent and variance of rainfall time series that are able to simulate streamflow that is superior to that simulated by a traditional calibration approach is a
Kachar, H.; Mobasheri, M. R.; Abkar, A. A.; Rahim Zadegan, M.
2015-12-01
Increase of temperature with height in the troposphere is called temperature inversion. Parameters such as strength and depth are characteristics of temperature inversion. Inversion strength is defined as the temperature difference between the surface and the top of the inversion and the depth of inversion is defined as the height of the inversion from the surface. The common approach in determination of these parameters is the use of Radiosonde where these measurements are too sparse. The main objective of this study is detection and modeling the temperature inversion using MODIS thermal infrared data. There are more than 180 days per year in which the temperature inversion conditions are present in Kermanshah city. Kermanshah weather station was selected as the study area. 90 inversion days was selected from 2007 to 2008 where the sky was clear and the Radiosonde data were available. Brightness temperature for all thermal infrared bands of MODIS was calculated for these days. Brightness temperature difference between any of the thermal infrared bands of MODIS and band 31 was found to be sensitive to strength and depth of temperature inversion. Then correlation coefficients between these pairs and the inversion depth and strength both calculated from Radiosonde were evaluated. The results showed poor linear correlation. This was found to be due to the change of the atmospheric water vapor content and the relatively weak temperature inversion strength and depth occurring in Kermanshah. The polynomial mathematical models and Artificial intelligence algorithms were deployed for detection and modeling the temperature inversion. A model with the lowest terms and highest possible accuracy was obtained. The Model was tested using 20 independent test data. Results indicate that the inversion strength can be estimated with RMSE of 0.84° C and R2 of 0.90. Also inversion depth can be estimated with RMSE of 54.56 m and R2 of 0.86.
A MATLAB based 3D modeling and inversion code for MT data
Singh, Arun; Dehiya, Rahul; Gupta, Pravin K.; Israil, M.
2017-07-01
The development of a MATLAB based computer code, AP3DMT, for modeling and inversion of 3D Magnetotelluric (MT) data is presented. The code comprises two independent components: grid generator code and modeling/inversion code. The grid generator code performs model discretization and acts as an interface by generating various I/O files. The inversion code performs core computations in modular form - forward modeling, data functionals, sensitivity computations and regularization. These modules can be readily extended to other similar inverse problems like Controlled-Source EM (CSEM). The modular structure of the code provides a framework useful for implementation of new applications and inversion algorithms. The use of MATLAB and its libraries makes it more compact and user friendly. The code has been validated on several published models. To demonstrate its versatility and capabilities the results of inversion for two complex models are presented.
Inverse modeling of pumping tests to parameterize three-dimensional hydrofacies models
Medina-Ortega, P.; Morales-Casique, E.; Escolero-Fuentes, O.; Hernandez Espriu, A.
2013-05-01
We model the spatial distribution of hydrofacies in the aquifer of Mexico City and present a procedure for parameterizing those hydrofacies by inverse modeling of pumping tests . The aquifer is composed of a highly heterogeneous mixture of alluvial deposits and volcanic rocks. Lithological records from 111 production water wells are analyzed using indicator geostatistics. The different lithological categories are grouped into four hydrofacies, where a hydrofacies is a set of lithological categories which have similar hydraulic properties. An exponential variogram model was fitted to each hydrofacies by minimizing cross validation errors. The data set is then kriged to obtain the three-dimensional distribution of each hydrofacies within the alluvial aquifer of Mexico City. We present a procedure to parameterize the four hydrofacies by inverse modeling of two pumping tests and test the predictive capabilities of the inversion results by forward modeling of two more pumping tests.
Forecasting wind-driven wildfires using an inverse modelling approach
Directory of Open Access Journals (Sweden)
O. Rios
2014-06-01
Full Text Available A technology able to rapidly forecast wildfire dynamics would lead to a paradigm shift in the response to emergencies, providing the Fire Service with essential information about the ongoing fire. This paper presents and explores a novel methodology to forecast wildfire dynamics in wind-driven conditions, using real-time data assimilation and inverse modelling. The forecasting algorithm combines Rothermel's rate of spread theory with a perimeter expansion model based on Huygens principle and solves the optimisation problem with a tangent linear approach and forward automatic differentiation. Its potential is investigated using synthetic data and evaluated in different wildfire scenarios. The results show the capacity of the method to quickly predict the location of the fire front with a positive lead time (ahead of the event in the order of 10 min for a spatial scale of 100 m. The greatest strengths of our method are lightness, speed and flexibility. We specifically tailor the forecast to be efficient and computationally cheap so it can be used in mobile systems for field deployment and operativeness. Thus, we put emphasis on producing a positive lead time and the means to maximise it.
Inverse modeling for heat conduction problem in human abdominal phantom.
Huang, Ming; Chen, Wenxi
2011-01-01
Noninvasive methods for deep body temperature measurement are based on the principle of heat equilibrium between the thermal sensor and the target location theoretically. However, the measurement position is not able to be definitely determined. In this study, a 2-dimensional mathematical model was built based upon some assumptions for the physiological condition of the human abdomen phantom. We evaluated the feasibility in estimating the internal organs temperature distribution from the readings of the temperature sensors arranged on the skin surface. It is a typical inverse heat conduction problem (IHCP), and is usually mathematically ill-posed. In this study, by integrating some physical and physiological a-priori information, we invoked the quasi-linear (QL) method to reconstruct the internal temperature distribution. The solutions of this method were improved by increasing the accuracy of the sensors and adjusting their arrangement on the outer surface, and eventually reached the state of converging at the best state accurately. This study suggests that QL method is able to reconstruct the internal temperature distribution in this phantom and might be worthy of a further study in an anatomical based model.
More Bayesian Transdimensional Inversion for Thermal History Modelling (Invited)
Gallagher, K.
2013-12-01
Since the publication of Dodson (1973) quantifying the relationship between geochronogical ages and closure temperatures, an ongoing concern in thermochronology is reconstruction of thermal histories consistent with the measured data. Extracting this thermal history information is best treated as an inverse problem, given the complex relationship between the observations and the thermal history. When solving the inverse problem (i.e. finding thermal acceptable thermal histories), stochastic sampling methods have often been used, as these are relatively global when searching the model space. However, the issue remains how best to estimate those parts of the thermal history unconstrained by independent information, i.e. what is required to fit the data ? To solve this general problem, we use a Bayesian transdimensional Markov Chain Monte Carlo method and this has been integrated into user-friendly software, QTQt (Quantitative Thermochronology with Qt), which runs on both Macintosh and PC. The Bayesian approach allows us to consider a wide range of possible thermal history as general prior information on time, temperature (and temperature offset for multiple samples in a vertical profile). We can also incorporate more focussed geological constraints in terms of more specific priors. In this framework, it is the data themselves (and their errors) that determine the complexity of the thermal history solutions. For example, more precise data will justify a more complex solution, while more noisy data will be happy with simpler solutions. We can express complexity in terms of the number of time-temperature points defining the total thermal history. Another useful feature of this method is that was can easily deal with imprecise parameter values (e.g. kinetics, data errors), by drawing samples from a user specified probability distribution, rather than using a single value. Finally, the method can be applied to either single samples, or multiple samples (from a borehole or
Affordable and personalized lighting using inverse modeling and virtual sensors
Basu, Chandrayee; Chen, Benjamin; Richards, Jacob; Dhinakaran, Aparna; Agogino, Alice; Martin, Rodney
2014-03-01
Wireless sensor networks (WSN) have great potential to enable personalized intelligent lighting systems while reducing building energy use by 50%-70%. As a result WSN systems are being increasingly integrated in state-ofart intelligent lighting systems. In the future these systems will enable participation of lighting loads as ancillary services. However, such systems can be expensive to install and lack the plug-and-play quality necessary for user-friendly commissioning. In this paper we present an integrated system of wireless sensor platforms and modeling software to enable affordable and user-friendly intelligent lighting. It requires ⇠ 60% fewer sensor deployments compared to current commercial systems. Reduction in sensor deployments has been achieved by optimally replacing the actual photo-sensors with real-time discrete predictive inverse models. Spatially sparse and clustered sub-hourly photo-sensor data captured by the WSN platforms are used to develop and validate a piece-wise linear regression of indoor light distribution. This deterministic data-driven model accounts for sky conditions and solar position. The optimal placement of photo-sensors is performed iteratively to achieve the best predictability of the light field desired for indoor lighting control. Using two weeks of daylight and artificial light training data acquired at the Sustainability Base at NASA Ames, the model was able to predict the light level at seven monitored workstations with 80%-95% accuracy. We estimate that 10% adoption of this intelligent wireless sensor system in commercial buildings could save 0.2-0.25 quads BTU of energy nationwide.
Modelling and genetic algorithm based optimisation of inverse supply chain
Bányai, T.
2009-04-01
(Recycling of household appliances with emphasis on reuse options). The purpose of this paper is the presentation of a possible method for avoiding the unnecessary environmental risk and landscape use through unprovoked large supply chain of collection systems of recycling processes. In the first part of the paper the author presents the mathematical model of recycling related collection systems (applied especially for wastes of electric and electronic products) and in the second part of the work a genetic algorithm based optimisation method will be demonstrated, by the aid of which it is possible to determine the optimal structure of the inverse supply chain from the point of view economical, ecological and logistic objective functions. The model of the inverse supply chain is based on a multi-level, hierarchical collection system. In case of this static model it is assumed that technical conditions are permanent. The total costs consist of three parts: total infrastructure costs, total material handling costs and environmental risk costs. The infrastructure-related costs are dependent only on the specific fixed costs and the specific unit costs of the operation points (collection, pre-treatment, treatment, recycling and reuse plants). The costs of warehousing and transportation are represented by the material handling related costs. The most important factors determining the level of environmental risk cost are the number of out of time recycled (treated or reused) products, the number of supply chain objects and the length of transportation routes. The objective function is the minimization of the total cost taking into consideration the constraints. However a lot of research work discussed the design of supply chain [8], but most of them concentrate on linear cost functions. In the case of this model non-linear cost functions were used. The non-linear cost functions and the possible high number of objects of the inverse supply chain leaded to the problem of choosing a
Improvement of PM10 prediction in East Asia using inverse modeling
Koo, Youn-Seo; Choi, Dae-Ryun; Kwon, Hi-Yong; Jang, Young-Kee; Han, Jin-Seok
2015-04-01
Aerosols from anthropogenic emissions in industrialized region in China as well as dust emissions from southern Mongolia and northern China that transport along prevailing northwestern wind have a large influence on the air quality in Korea. The emission inventory in the East Asia region is an important factor in chemical transport modeling (CTM) for PM10 (particulate matters less than 10 ㎛ in aerodynamic diameter) forecasts and air quality management in Korea. Most previous studies showed that predictions of PM10 mass concentration by the CTM were underestimated when comparing with observational data. In order to fill the gap in discrepancies between observations and CTM predictions, the inverse Bayesian approach with Comprehensive Air-quality Model with extension (CAMx) forward model was applied to obtain optimized a posteriori PM10 emissions in East Asia. The predicted PM10 concentrations with a priori emission were first compared with observations at monitoring sites in China and Korea for January and August 2008. The comparison showed that PM10 concentrations with a priori PM10 emissions for anthropogenic and dust sources were generally under-predicted. The result from the inverse modeling indicated that anthropogenic PM10 emissions in the industrialized and urbanized areas in China were underestimated while dust emissions from desert and barren soil in southern Mongolia and northern China were overestimated. A priori PM10 emissions from northeastern China regions including Shenyang, Changchun, and Harbin were underestimated by about 300% (i.e., the ratio of a posteriori to a priori PM10 emission was a factor of about 3). The predictions of PM10 concentrations with a posteriori emission showed better agreement with the observations, implying that the inverse modeling minimized the discrepancies in the model predictions by improving PM10 emissions in East Asia.
Partridge, Daniel; Morales, Ricardo; Stier, Philip
2015-04-01
Many previous studies have compared droplet activation parameterisations against adiabatic parcel models (e.g. Ghan et al., 2001). However, these have often involved comparisons for a limited number of parameter combinations based upon certain aerosol regimes. Recent studies (Morales et al., 2014) have used wider ranges when evaluating their parameterisations, however, no study has explored the full possible multi-dimensional parameter space that would be experienced by droplet activations within a global climate model (GCM). It is important to be able to efficiently highlight regions of the entire multi-dimensional parameter space in which we can expect the largest discrepancy between parameterisation and cloud parcel models in order to ascertain which regions simulated by a GCM can be expected to be a less accurate representation of the process of cloud droplet activation. This study provides a new, efficient, inverse modelling framework for comparing droplet activation parameterisations to more complex cloud parcel models. To achieve this we couple a Markov Chain Monte Carlo algorithm (Partridge et al., 2012) to two independent adiabatic cloud parcel models and four droplet activation parameterisations. This framework is computationally faster than employing a brute force Monte Carlo simulation, and allows us to transparently highlight which parameterisation provides the closest representation across all aerosol physiochemical and meteorological environments. The parameterisations are demonstrated to perform well for a large proportion of possible parameter combinations, however, for certain key parameters; most notably the vertical velocity and accumulation mode aerosol concentration, large discrepancies are highlighted. These discrepancies correspond for parameter combinations that result in very high/low simulated values of maximum supersaturation. By identifying parameter interactions or regimes within the multi-dimensional parameter space we hope to guide
Inverse modeling of methane sources and sinks using the adjoint of a global transport model
Houweling, S; Kaminski, T; Dentener, F; Lelieveld, J; Heimann, M
1999-01-01
An inverse modeling method is presented to evaluate the sources and sinks of atmospheric methane. An adjoint version of a global transport model has been used to estimate these fluxes at a relatively high spatial and temporal resolution. Measurements from 34 monitoring stations and 11 locations
Inverse modeling of FIB milling by dose profile optimization
International Nuclear Information System (INIS)
Lindsey, S.; Waid, S.; Hobler, G.; Wanzenböck, H.D.; Bertagnolli, E.
2014-01-01
FIB technologies possess a unique ability to form topographies that are difficult or impossible to generate with binary etching through typical photo-lithography. The ability to arbitrarily vary the spatial dose distribution and therefore the amount of milling opens possibilities for the production of a wide range of functional structures with applications in biology, chemistry, and optics. However in practice, the realization of these goals is made difficult by the angular dependence of the sputtering yield and redeposition effects that vary as the topography evolves. An inverse modeling algorithm that optimizes dose profiles, defined as the superposition of time invariant pixel dose profiles (determined from the beam parameters and pixel dwell times), is presented. The response of the target to a set of pixel dwell times in modeled by numerical continuum simulations utilizing 1st and 2nd order sputtering and redeposition, the resulting surfaces are evaluated with respect to a target topography in an error minimization routine. Two algorithms for the parameterization of pixel dwell times are presented, a direct pixel dwell time method, and an abstracted method that uses a refineable piecewise linear cage function to generate pixel dwell times from a minimal number of parameters. The cage function method demonstrates great flexibility and efficiency as compared to the direct fitting method with performance enhancements exceeding ∼10× as compared to direct fitting for medium to large simulation sets. Furthermore, the refineable nature of the cage function enables solutions to adapt to the desired target function. The optimization algorithm, although working with stationary dose profiles, is demonstrated to be applicable also outside the quasi-static approximation. Experimental data confirms the viability of the solutions for 5 × 7 μm deep lens like structures defined by 90 pixel dwell times
Risk evaluation of uranium mining: A geochemical inverse modelling approach
Rillard, J.; Zuddas, P.; Scislewski, A.
2011-12-01
It is well known that uranium extraction operations can increase risks linked to radiation exposure. The toxicity of uranium and associated heavy metals is the main environmental concern regarding exploitation and processing of U-ore. In areas where U mining is planned, a careful assessment of toxic and radioactive element concentrations is recommended before the start of mining activities. A background evaluation of harmful elements is important in order to prevent and/or quantify future water contamination resulting from possible migration of toxic metals coming from ore and waste water interaction. Controlled leaching experiments were carried out to investigate processes of ore and waste (leached ore) degradation, using samples from the uranium exploitation site located in Caetité-Bahia, Brazil. In experiments in which the reaction of waste with water was tested, we found that the water had low pH and high levels of sulphates and aluminium. On the other hand, in experiments in which ore was tested, the water had a chemical composition comparable to natural water found in the region of Caetité. On the basis of our experiments, we suggest that waste resulting from sulphuric acid treatment can induce acidification and salinization of surface and ground water. For this reason proper storage of waste is imperative. As a tool to evaluate the risks, a geochemical inverse modelling approach was developed to estimate the water-mineral interaction involving the presence of toxic elements. We used a method earlier described by Scislewski and Zuddas 2010 (Geochim. Cosmochim. Acta 74, 6996-7007) in which the reactive surface area of mineral dissolution can be estimated. We found that the reactive surface area of rock parent minerals is not constant during time but varies according to several orders of magnitude in only two months of interaction. We propose that parent mineral heterogeneity and particularly, neogenic phase formation may explain the observed variation of the
Bayesian inversions of a dynamic vegetation model in four European grassland sites
Minet, J.; Laloy, E.; Tychon, B.; François, L.
2015-01-01
Eddy covariance data from four European grassland sites are used to probabilistically invert the CARAIB dynamic vegetation model (DVM) with ten unknown parameters, using the DREAM(ZS) Markov chain Monte Carlo (MCMC) sampler. We compare model inversions considering both homoscedastic and heteroscedastic eddy covariance residual errors, with variances either fixed a~priori or jointly inferred with the model parameters. Agreements between measured and simulated data during calibration are comparable with previous studies, with root-mean-square error (RMSE) of simulated daily gross primary productivity (GPP), ecosystem respiration (RECO) and evapotranspiration (ET) ranging from 1.73 to 2.19 g C m-2 day-1, 1.04 to 1.56 g C m-2 day-1, and 0.50 to 1.28 mm day-1, respectively. In validation, mismatches between measured and simulated data are larger, but still with Nash-Sutcliffe efficiency scores above 0.5 for three out of the four sites. Although measurement errors associated with eddy covariance data are known to be heteroscedastic, we showed that assuming a classical linear heteroscedastic model of the residual errors in the inversion do not fully remove heteroscedasticity. Since the employed heteroscedastic error model allows for larger deviations between simulated and measured data as the magnitude of the measured data increases, this error model expectedly lead to poorer data fitting compared to inversions considering a constant variance of the residual errors. Furthermore, sampling the residual error variances along with model parameters results in overall similar model parameter posterior distributions as those obtained by fixing these variances beforehand, while slightly improving model performance. Despite the fact that the calibrated model is generally capable of fitting the data within measurement errors, systematic bias in the model simulations are observed. These are likely due to model inadequacies such as shortcomings in the photosynthesis modelling
No inverse magnetic catalysis in the QCD hard and soft wall models
Dudal, David; Granado, Diego R.; Mertens, Thomas G.
2016-06-01
In this paper, we study the influence of an external magnetic field in holographic QCD models where the backreaction is modeled via an appropriate choice of the background metric. We add a phenomenological soft wall dilaton to incorporate better IR behavior (confinement). Elaborating on previous studies conducted by [K. A. Mamo, J. High Energy Phys. 05 (2015) 121.], we first discuss the Hawking-Page transition, the dual of the deconfinement transition, as a function of the magnetic field. We confirm that the critical deconfinement temperature can drop with the magnetic field. Secondly, we study the quark condensate holographically as a function of the applied magnetic field and demonstrate that this model does not exhibit inverse magnetic catalysis at the level of the chiral transition. The quest for a holographic QCD model that qualitatively describes the inverse magnetic catalysis at finite temperature is thus still open. Throughout this work, we pay special attention to the different holographic parameters and we attempt to fix them by making the link to genuine QCD as close as possible. This leads to several unanticipated and so far overlooked complications (such as the relevance of an additional length scale ℓc in the confined geometry) that we discuss in detail.
A Direct inverse model to determine permeability fields from pressure and flow rate measurements
Brouwer, G.K.; Fokker, P.A.; Wilschut, F.; Zijl, W.
2008-01-01
The determination of the permeability field from pressure and flow rate measurements in wells is a key problem in reservoir engineering. This paper presents a Double Constraint method for inverse modeling that is an example of direct inverse modeling. The method is used with a standard
Losada, David E.; Barreiro, Alvaro
2003-01-01
Proposes an approach to incorporate term similarity and inverse document frequency into a logical model of information retrieval. Highlights include document representation and matching; incorporating term similarity into the measure of distance; new algorithms for implementation; inverse document frequency; and logical versus classical models of…
Energy Technology Data Exchange (ETDEWEB)
Vermeul, Vincent R.; Cole, Charles R.; Bergeron, Marcel P.; Thorne, Paul D.; Wurstner, Signe K.
2001-08-29
The baseline three-dimensional transient inverse model for the estimation of site-wide scale flow parameters, including their uncertainties, using data on the transient behavior of the unconfined aquifer system over the entire historical period of Hanford operations, has been modified to account for the effects of basalt intercommunication between the Hanford unconfined aquifer and the underlying upper basalt confined aquifer. Both the baseline and alternative conceptual models (ACM-1) considered only the groundwater flow component and corresponding observational data in the 3-Dl transient inverse calibration efforts. Subsequent efforts will examine both groundwater flow and transport. Comparisons of goodness of fit measures and parameter estimation results for the ACM-1 transient inverse calibrated model with those from previous site-wide groundwater modeling efforts illustrate that the new 3-D transient inverse model approach will strengthen the technical defensibility of the final model(s) and provide the ability to incorporate uncertainty in predictions related to both conceptual model and parameter uncertainty. These results, however, indicate that additional improvements are required to the conceptual model framework. An investigation was initiated at the end of this basalt inverse modeling effort to determine whether facies-based zonation would improve specific yield parameter estimation results (ACM-2). A description of the justification and methodology to develop this zonation is discussed.
Goal Directed Model Inversion: Learning Within Domain Constraints
Colombano, Silvano P.; Compton, Michael; Raghavan, Bharathi; Lum, Henry, Jr. (Technical Monitor)
1994-01-01
Goal Directed Model Inversion (GDMI) is an algorithm designed to generalize supervised learning to the case where target outputs are not available to the learning system. The output of the learning system becomes the input to some external device or transformation, and only the output of this device or transformation can be compared to a desired target. The fundamental driving mechanism of GDMI is to learn from success. Given that a wrong outcome is achieved, one notes that the action that produced that outcome "would have been right if the outcome had been the desired one." The algorithm makes use of these intermediate "successes" to achieve the final goal. A unique and potentially very important feature of this algorithm is the ability to modify the output of the learning module to force upon it a desired syntactic structure. This differs from ordinary supervised learning in the following way: in supervised learning the exact desired output pattern must be provided. In GDMI instead, it is possible to require simply that the output obey certain rules, i.e., that it "make sense" in some way determined by the knowledge domain. The exact pattern that will achieve the desired outcome is then found by the system. The ability to impose rules while allowing the system to search for its own answers in the context of neural networks is potentially a major breakthrough in two ways: 1) it may allow the construction of networks that can incorporate immediately some important knowledge, i.e. would not need to learn everything from scratch as normally required at present, and 2) learning and searching would be limited to the areas where it is necessary, thus facilitating and speeding up the process. These points are illustrated with examples from robotic path planning and parametric design.
DEFF Research Database (Denmark)
Oh, Geok Lian
properties such as the elastic wave speeds and soil densities. One processing method is casting the estimation problem into an inverse problem to solve for the unknown material parameters. The forward model for the seismic signals used in the literatures include ray tracing methods that consider only...... density values of the discretized ground medium, which leads to time-consuming computations and instability behaviour of the inversion process. In addition, the geophysics inverse problem is generally ill-posed due to non-exact forward model that introduces errors. The Bayesian inversion method through...... the probability density function permits the incorporation of a priori information about the parameters, and also allow for incorporation of theoretical errors. This opens up the possibilities of application of inverse paradigm in the real-world geophysics inversion problems. In this PhD study, the Bayesian...
Lloyd, Stephen F.
The purpose of this research is to test the effectiveness of forward and inverse modeling approaches in wave propagation analysis problems with complex settings and scenarios that include fluid-solid interfaces, non-stationary sources, and non-point sources not previously investigated. The research is made up of three components. First, finite element method modeling and a genetic algorithm are employed to assess the feasibility of using inverse modeling to determine the thickness of the surface ice on Europa, one of Jupiter's moons, and the depth of a possible subsurface ocean. The feasibility study presented in this dissertation considers the specific case in which inverse modeling might be used to determine the depths of ice and ocean layers on Europa for a possible space mission in which the effects of a spacecraft-released impactor on Europa's surface are measured. Second, reconstructing dynamic distributed loads, such as truck loads on highways, require inverting for large numbers of parameters. To address solving for the large number of unknowns in such problems, an adjoint-method-based acoustic-source inversion procedure for reconstructing multiple moving, non-point acoustic sources is developed and tested with numerical experiments. Third, forward modeling of moving sources in three-dimensional (3D) settings is tested with numerical experiments using SPECFEM3D, an open source spectral element method program. Researching forward modeling for complicated scenarios such as moving acoustic sources in fluid-solid coupled systems in 3D is an important step toward using SPECFEM3D for moving-source inversion problems in 3D. The conclusions of the research presented are as follows: It is feasible to estimate the thickness of the ice layer on the surface of Europa and the depth of a subsurface ocean with inverse modeling based on measured wave motions in the ice caused by a planned impact. The adjoint method is effective in reconstructing large numbers of acoustic
Liu, YanBin; Li, YuHui; Jin, FeiTeng
2017-01-01
The inversion design approach is a very useful tool for the complex multiple-input-multiple-output nonlinear systems to implement the decoupling control goal, such as the airplane model and spacecraft model. In this work, the flight control law is proposed using the neural-based inversion design method associated with the nonlinear compensation for a general longitudinal model of the airplane. First, the nonlinear mathematic model is converted to the equivalent linear model based on the feedb...
Cousquer, Yohann; Pryet, Alexandre; Atteia, Olivier; Ferré, Ty P. A.; Delbart, Célestine; Valois, Rémi; Dupuy, Alain
2018-03-01
The inverse problem of groundwater models is often ill-posed and model parameters are likely to be poorly constrained. Identifiability is improved if diverse data types are used for parameter estimation. However, some models, including detailed solute transport models, are further limited by prohibitive computation times. This often precludes the use of concentration data for parameter estimation, even if those data are available. In the case of surface water-groundwater (SW-GW) models, concentration data can provide SW-GW mixing ratios, which efficiently constrain the estimate of exchange flow, but are rarely used. We propose to reduce computational limits by simulating SW-GW exchange at a sink (well or drain) based on particle tracking under steady state flow conditions. Particle tracking is used to simulate advective transport. A comparison between the particle tracking surrogate model and an advective-dispersive model shows that dispersion can often be neglected when the mixing ratio is computed for a sink, allowing for use of the particle tracking surrogate model. The surrogate model was implemented to solve the inverse problem for a real SW-GW transport problem with heads and concentrations combined in a weighted hybrid objective function. The resulting inversion showed markedly reduced uncertainty in the transmissivity field compared to calibration on head data alone.
On the modeling and inversion of seismic data
Stolk, C.C.
2000-01-01
In this thesis we investigate some mathematical questions related to the inversion of seismic data. In Chapter 2 we review results in the literature and give some new results on wave equations with coefficients that are just bounded and measurable. We show that these equations have unique
Rosas-Carbajal, M.; Linde, N.; Kalscheuer, T.; Vrugt, J.A.
2014-01-01
Probabilistic inversion methods based on Markov chain Monte Carlo (MCMC) simulation are well suited to quantify parameter and model uncertainty of nonlinear inverse problems. Yet, application of such methods to CPU-intensive forward models can be a daunting task, particularly if the parameter space
Forward and Inverse Analysis of Chemical Transport Models
Ruiz-Lapuente, Pilar
Assessing the discrepancy between modeled and observed distributions of aerosols is a persistent problem on many scales. Tools for analyzing the evolution of aerosol size distributions using the adjoint method are presented in idealized box model calculations. The ability to recover information about aerosol growth rates and initial size distributions is assessed given a range of simulated observations of evolving systems. While such tools alone could facilitate analysis of chamber measurements, improving estimates of aerosol sources on regional and global scales requires explicit consideration of many additional chemical and physical processes that govern secondary formation of atmospheric aerosols from emissions of gas-phase precursors. The adjoint of the global chemical transport model GEOS-Chem is derived, affording detailed analysis of the relationship between gas-phase aerosol precursor emissions (SOx, NOx and NH 3) and the subsequent distributions of sulfate - ammonium - nitrate aerosol. Assimilation of surface measurements of sulfate and nitrate aerosol is shown to provide valuable constraints on emissions of ammonia. Adjoint sensitivities are used to propose strategies for air quality control, suggesting, for example, that reduction of SOx emissions in the summer and NH3 emissions in the winter would most effectively reduce non-attainment of aerosol air quality standards. The ability of this model to estimate global distributions of carbonaceous aerosol is also addressed. Based on new yield data from environmental chamber studies, mechanisms for incorporating the dependence of secondary organic aerosol (SOA) formation on NOx concentrations are developed for use in global models. When NOx levels are appropriately accounted for, it is demonstrated that sources such as isoprene and aromatics, previously neglected as sources of aerosol in global models, significantly contribute to predicted SOA burdens downwind of polluted areas (owing to benzene and toluene
Lin, Youzuo; Huang, Lianjie
2017-11-01
Reverse-time migration has the potential to image complex subsurface structures, including steeply-dipping fault zones, but the method requires an accurate velocity model. Acoustic- and elastic-waveform inversion is a promising tool for high-resolution velocity model building. Because of the ill-posedness of acoustic- and elastic-waveform inversion, it is a great challenge to obtain accurate velocity models containing sharp interfaces. To improve velocity model building, we develop an acoustic- and elastic-waveform inversion method with an interface-guided modified total-variation regularization scheme to improve the inversion accuracy and robustness, particularly for models with sharp interfaces and steeply-dipping fault zones with widths much smaller than the seismic wavelength. The new regularization scheme incorporates interface information into seismic full-waveform inversion. The interface information of subsurface interfaces is obtained iteratively using migration imaging during waveform inversion. Seismic migration is robust for subsurface imaging. Our new acoustic- and elastic-waveform inversion takes advantage of the robustness of migration imaging to improve velocity estimation. We use synthetic seismic data for a complex model containing sharp interfaces and several steeply-dipping fault zones to validate the improved capability of our new acoustic- and elastic-waveform inversion method. Our inversion results are much better than those produced without using interface-guided regularization. Acoustic- and elastic-waveform inversion with an interface-guided modified total-variation regularization scheme has the potential to accurately build subsurface velocity models with sharp interfaces and/or steep fault zones.
Directory of Open Access Journals (Sweden)
YanBin Liu
2017-01-01
Full Text Available The inversion design approach is a very useful tool for the complex multiple-input-multiple-output nonlinear systems to implement the decoupling control goal, such as the airplane model and spacecraft model. In this work, the flight control law is proposed using the neural-based inversion design method associated with the nonlinear compensation for a general longitudinal model of the airplane. First, the nonlinear mathematic model is converted to the equivalent linear model based on the feedback linearization theory. Then, the flight control law integrated with this inversion model is developed to stabilize the nonlinear system and relieve the coupling effect. Afterwards, the inversion control combined with the neural network and nonlinear portion is presented to improve the transient performance and attenuate the uncertain effects on both external disturbances and model errors. Finally, the simulation results demonstrate the effectiveness of this controller.
Liu, YanBin; Li, YuHui; Jin, FeiTeng
2017-01-01
The inversion design approach is a very useful tool for the complex multiple-input-multiple-output nonlinear systems to implement the decoupling control goal, such as the airplane model and spacecraft model. In this work, the flight control law is proposed using the neural-based inversion design method associated with the nonlinear compensation for a general longitudinal model of the airplane. First, the nonlinear mathematic model is converted to the equivalent linear model based on the feedback linearization theory. Then, the flight control law integrated with this inversion model is developed to stabilize the nonlinear system and relieve the coupling effect. Afterwards, the inversion control combined with the neural network and nonlinear portion is presented to improve the transient performance and attenuate the uncertain effects on both external disturbances and model errors. Finally, the simulation results demonstrate the effectiveness of this controller.
Directory of Open Access Journals (Sweden)
Lingen Chen
2012-01-01
Full Text Available A thermodynamic model of an open combined regenerative Brayton and inverse Brayton cycles with regeneration before the inverse cycle is established in this paper by using thermodynamic optimization theory. The flow processes of the working fluid with the pressure drops and the size constraint of the real power plant are modeled. There are 13 flow resistances encountered by the working fluid stream for the cycle model. Four of these, the friction through the blades and vanes of the compressors and the turbines, are related to the isentropic efficiencies. The remaining nine flow resistances are always present because of the changes in flow cross-section at the compressor inlet of the top cycle, regenerator inlet and outlet, combustion chamber inlet and outlet, turbine outlet of the top cycle, turbine outlet of the bottom cycle, heat exchanger inlet, and compressor inlet of the bottom cycle. These resistances associated with the flow through various cross-sectional areas are derived as functions of the compressor inlet relative pressure drop of the top cycle, and control the air flow rate, the net power output and the thermal efficiency. The analytical formulae about the power output, efficiency and other coefficients are derived with 13 pressure drop losses. It is found that the combined cycle with regenerator can reach higher thermal efficiency but smaller power output than those of the base combined cycle at small compressor inlet relative pressure drop of the top cycle.
Zhang, Sophia; Penner, Joyce E.; Torres, Omar
2005-11-01
We present results from an inverse model study to determine biomass smoke emissions for the year 1997 by comparison of modeled aerosol index (AI) with that measured by the EP TOMS instrument. The IMPACT model with Data Assimilation Office (DAO) meteorology data in 1997 is utilized to obtain aerosol spatial and temporal distributions. Then a radiative transfer model is applied to generate the modeled AI. A Bayesian inverse technique is applied to optimize the difference between the modeled AI and the EP TOMS AI in the same period by regulating monthly a priori biomass smoke emissions in seven predefined regions. The modeled AI with a posteriori emissions is generally in better agreement with the EP TOMS AI. The a posteriori emissions from Indonesia increase by a factor of 8-10 over the a priori emissions due to the Indonesian fires in 1997. The annual total a posteriori source increases by about 13% for the year 1997 (6.31 Tg/yr black carbon and 67.27 Tg/yr smoke) in the base scenario, with a larger adjustment of monthly emissions. The sensitivity of this result to the a priori uncertainties, the height of the smoke layer, the cloud screening criteria, the inclusion of an adjustment of emissions outside the main biomass burning regions, and the inclusion of the covariances between observations in different locations is discussed in a set of sensitivity scenarios. The sensitivity scenarios suggest that the inverse model results are most sensitive to the assumed uncertainty for a priori emissions and the altitude of aerosol layer in the model and are less sensitive to other factors. In the scenario where the uncertainty of a priori emissions is increased to 100% (300% in Indonesia), the total annual black carbon emission is increased to 6.87 Tg/yr, and the smoke emission increases to 73.39 Tg/yr. The a posteriori emissions in Indonesia in the scenario with increased uncertainty are in better agreement with both the TOMS AI and with previous estimates for the
Mathematical and numerical modeling of inverse heat conduction problem
Directory of Open Access Journals (Sweden)
Sterian DANAILA
2014-12-01
Full Text Available The present paper refers to the assessment of three numerical methods for solving the inverse heat conduction problem: the Alifanov’s iterative regularization method, the Tikhonov local regularization method and the Tikhonov equation regularization method, respectively. For all methods we developed numerical algorithms for reconstruction of the unsteady boundary condition imposing some restrictions for the unsteady temperature field in the interior points. Numerical tests allow evaluating the accuracy of the considered methods.
Directory of Open Access Journals (Sweden)
R. Marklein
2005-01-01
Full Text Available This paper presents recent advances and future challenges of the application of different linear and nonlinear inversion algorithms in acoustics, electromagnetics, and elastodynamics. The presented material can be understood as an extension of our previous work on this topic. The inversion methods considered in this presentation vary from linear schemes, like the Synthetic Aperture Radar (SAR applied electromagnetics and the Synthetic Aperture Focussing Technique (SAFT as its counterpart in ultrasonics, and the linearized Diffraction Tomography (DT, to nonlinear schemes, like the Contrast Source Inversion (CSI combined with different regularization approaches. Inversion results of the above mentioned inversion schemes are presented and compared for instance for time-domain ultrasonic data from the Fraunhofer-Institute for Nondestructive Testing (IZFP, Saarbrücken, Germany. Convenient tools for nondestructive evaluation of solids can be electromagnetic and/or elastodynamic waves; since their governing equations, including acoustics, exhibit strong structural similarities, the same inversion concepts apply. In particular, the heuristic SAFT algorithm can be and has been utilized for all kinds of waves, once a scalar approximation can be justified. Relating SAFT to inverse scattering in terms of diffraction tomography, it turns out that linearization is the most stringent inherent approximation. A comparison of the inversion results using the linear time-domain inversion scheme SAFT and well tested nonlinear frequency-domain inversion schemes demonstrates the considerable potential to extend and improve the ultrasonic imaging technique SAFT while consulting the mathematics of wavefield inversion, yet, in particular if the underlying effort is considered, the relatively simple and effective SAFT algorithm works surprisingly well. Since SAFT is a widely accepted imaging tool in ultrasonic NDE it seems worthwhile to check its formal restrictions and
Marklein, R.; Langenberg, K. J.; Mayer, K.; Miao, J.; Shlivinski, A.; Zimmer, A.; Müller, W.; Schmitz, V.; Kohl, C.; Mletzko, U.
2005-05-01
This paper presents recent advances and future challenges of the application of different linear and nonlinear inversion algorithms in acoustics, electromagnetics, and elastodynamics. The presented material can be understood as an extension of our previous work on this topic. The inversion methods considered in this presentation vary from linear schemes, like the Synthetic Aperture Radar (SAR) applied electromagnetics and the Synthetic Aperture Focussing Technique (SAFT) as its counterpart in ultrasonics, and the linearized Diffraction Tomography (DT), to nonlinear schemes, like the Contrast Source Inversion (CSI) combined with different regularization approaches. Inversion results of the above mentioned inversion schemes are presented and compared for instance for time-domain ultrasonic data from the Fraunhofer-Institute for Nondestructive Testing (IZFP, Saarbrücken, Germany). Convenient tools for nondestructive evaluation of solids can be electromagnetic and/or elastodynamic waves; since their governing equations, including acoustics, exhibit strong structural similarities, the same inversion concepts apply. In particular, the heuristic SAFT algorithm can be and has been utilized for all kinds of waves, once a scalar approximation can be justified. Relating SAFT to inverse scattering in terms of diffraction tomography, it turns out that linearization is the most stringent inherent approximation. A comparison of the inversion results using the linear time-domain inversion scheme SAFT and well tested nonlinear frequency-domain inversion schemes demonstrates the considerable potential to extend and improve the ultrasonic imaging technique SAFT while consulting the mathematics of wavefield inversion, yet, in particular if the underlying effort is considered, the relatively simple and effective SAFT algorithm works surprisingly well. Since SAFT is a widely accepted imaging tool in ultrasonic NDE it seems worthwhile to check its formal restrictions and assumptions
Alkhalifah, Tariq Ali
2012-09-25
Traveltime inversion focuses on the geometrical features of the waveform (traveltimes), which is generally smooth, and thus, tends to provide averaged (smoothed) information of the model. On other hand, general waveform inversion uses additional elements of the wavefield including amplitudes to extract higher resolution information, but this comes at the cost of introducing non-linearity to the inversion operator, complicating the convergence process. We use unwrapped phase-based objective functions in waveform inversion as a link between the two general types of inversions in a domain in which such contributions to the inversion process can be easily identified and controlled. The instantaneous traveltime is a measure of the average traveltime of the energy in a trace as a function of frequency. It unwraps the phase of wavefields yielding far less non-linearity in the objective function than that experienced with conventional wavefields, yet it still holds most of the critical wavefield information in its frequency dependency. However, it suffers from non-linearity introduced by the model (or reflectivity), as reflections from independent events in our model interact with each other. Unwrapping the phase of such a model can mitigate this non-linearity as well. Specifically, a simple modification to the inverted domain (or model), can reduce the effect of the model-induced non-linearity and, thus, make the inversion more convergent. Simple numerical examples demonstrate these assertions.
Marklein, R.; Langenberg, K. J.; Mayer, K.; Miao, J.; Shlivinski, A.; Zimmer, A.; Müller, W.; Schmitz, V.; Kohl, C.; Mletzko, U.
2005-01-01
This paper presents recent advances and future challenges of the application of different linear and nonlinear inversion algorithms in acoustics, electromagnetics, and elastodynamics. The presented material can be understood as an extension of our previous work on this topic. The inversion methods considered in this presentation vary from linear schemes, like the Synthetic Aperture Radar (SAR) applied electromagnetics and the Synthetic Aperture Focussing Technique (SAFT) as its counterpart in...
Inverse modeling of multicomponent reactive transport through single and dual porosity media
Samper, Javier; Zheng, Liange; Fernández, Ana María; Montenegro, Luis
2008-06-01
Compacted bentonite is foreseen as buffer material for high-level radioactive waste in deep geological repositories because it provides hydraulic isolation, chemical stability, and radionuclide sorption. A wide range of laboratory tests were performed within the framework of FEBEX ( Full-scale Engineered Barrier EXperiment) project to characterize buffer properties and develop numerical models for FEBEX bentonite. Here we present inverse single and dual-continuum multicomponent reactive transport models of a long-term permeation test performed on a 2.5 cm long sample of FEBEX bentonite. Initial saline bentonite porewater was flushed with 5.5 pore volumes of fresh granitic water. Water flux and chemical composition of effluent waters were monitored during almost 4 years. The model accounts for solute advection and diffusion and geochemical reactions such as aqueous complexation, acid-base, cation exchange, protonation/deprotonation by surface complexation and dissolution/precipitation of calcite, chalcedony and gypsum. All of these processes are assumed at local equilibrium. Similar to previous studies of bentonite porewater chemistry on batch systems which attest the relevance of protonation/deprotonation on buffering pH, our results confirm that protonation/deprotonation is a key process in maintaining a stable pH under dynamic transport conditions. Breakthrough curves of reactive species are more sensitive to initial porewater concentration than to effective diffusion coefficient. Optimum estimates of initial porewater chemistry of saturated compacted FEBEX bentonite are obtained by solving the inverse problem of multicomponent reactive transport. While the single-continuum model reproduces the trends of measured data for most chemical species, it fails to match properly the long tails of most breakthrough curves. Such limitation is overcome by resorting to a dual-continuum reactive transport model.
Lu, Xiao-Ping; Huang, Xiang-Jie; Ip, Wing-Huen; Hsia, Chi-Hao
2018-04-01
In the lightcurve inversion process where asteroid's physical parameters such as rotational period, pole orientation and overall shape are searched, the numerical calculations of the synthetic photometric brightness based on different shape models are frequently implemented. Lebedev quadrature is an efficient method to numerically calculate the surface integral on the unit sphere. By transforming the surface integral on the Cellinoid shape model to that on the unit sphere, the lightcurve inversion process based on the Cellinoid shape model can be remarkably accelerated. Furthermore, Matlab codes of the lightcurve inversion process based on the Cellinoid shape model are available on Github for free downloading. The photometric models, i.e., the scattering laws, also play an important role in the lightcurve inversion process, although the shape variations of asteroids dominate the morphologies of the lightcurves. Derived from the radiative transfer theory, the Hapke model can describe the light reflectance behaviors from the viewpoint of physics, while there are also many empirical models in numerical applications. Numerical simulations are implemented for the comparison of the Hapke model with the other three numerical models, including the Lommel-Seeliger, Minnaert, and Kaasalainen models. The results show that the numerical models with simple function expressions can fit well with the synthetic lightcurves generated based on the Hapke model; this good fit implies that they can be adopted in the lightcurve inversion process for asteroids to improve the numerical efficiency and derive similar results to those of the Hapke model.
Dehghan, A.; Mariani, Z.; Gascon, G.; Bélair, S.; Milbrandt, J.; Joe, P. I.; Crawford, R.; Melo, S.
2017-12-01
Environment and Climate Change Canada (ECCC) is implementing a 2.5-km resolution version of the Global Environmental Multiscale (GEM) model over the Canadian Arctic. Radiosonde observations were used to evaluate the numerical representation of surface-based temperature inversion which is a major feature in the Arctic region. Arctic surface-based inversions are often created by imbalance between radiative cooling processes at surface and warm air advection above. This can have a significant effect on vertical mixing of pollutants and moisture, and ultimately, on cloud formation. It is therefore important to correctly predict the existence of surface inversions along with their characteristics (i.e., intensity and depth). Previous climatological studies showed that the frequency and intensity of surface-based inversions are larger during colder months in the Arctic. Therefore, surface-based inversions were estimated using radiosonde measurements during winter (December 2015 to February 2016) at Iqaluit (Nunavut, Canada). Results show that the inversion intensity can exceed 10 K with depths as large as 1 km. Preliminary evaluation of GEM outputs reveals that the model tends to underestimate the intensity of near-surface inversions, and in some cases, the model failed to predict an inversion. This study presents the factors contributing to this bias including surface temperature and snow cover.
Fitting inverse power-law quintessence models using the SNAP satellite
International Nuclear Information System (INIS)
Eriksson, Martin; Amanullah, Rahman
2002-01-01
We investigate the possibility of using the proposed SNAP satellite in combination with low-z supernova searches to distinguish between different inverse power-law quintessence models. If the true model is that of a cosmological constant, we determine the prospects of ruling out the inverse power-law potential. We show that SNAP combined with e.g., the SNfactory and an independent measurement of the mass energy density to 17% accuracy can distinguish between an inverse power-law potential and a cosmological constant and put severe constraints on the power-law exponent
Er, Li; Xiangying, Zeng
2014-01-01
To simulate the variation of biochemical oxygen demand (BOD) in the tidal Foshan River, inverse calculations based on time domain are applied to the longitudinal dispersion coefficient (E(x)) and BOD decay rate (K(x)) in the BOD model for the tidal Foshan River. The derivatives of the inverse calculation have been respectively established on the basis of different flow directions in the tidal river. The results of this paper indicate that the calculated values of BOD based on the inverse calculation developed for the tidal Foshan River match the measured ones well. According to the calibration and verification of the inversely calculated BOD models, K(x) is more sensitive to the models than E(x) and different data sets of E(x) and K(x) hardly affect the precision of the models.
Application of Lead Field Theory and Computerized Thorax Modeling for the ECG Inverse Problem
National Research Council Canada - National Science Library
Puurtinen, H
2001-01-01
.... In this study, one anatomically detailed 3D FDM model of the human thorax as a volume conductor was employed for forward and inverse estimation of ECG potentials and cardiac sources, respectively...
Sainz-Maza, S.; Montesinos, F. G.; Martí, J.; Arnoso, J.; Calvo, M.; Borreguero, A.
2017-08-01
Recent volcanism in El Hierro Island is mostly concentrated along three elongated and narrow zones which converge at the center of the island. These zones with extensive volcanism have been identified as rift zones. The presence of similar structures is common in many volcanic oceanic islands, so understanding their origin, dynamics and structure is important to conduct hazard assessment in such environments. There is still not consensus on the origin of the El Hierro rift zones, having been associated with mantle uplift or interpreted as resulting from gravitational spreading and flank instability. To further understand the internal structure and origin of the El Hierro rift systems, starting from the previous gravity studies, we developed a new 3D gravity inversion model for its shallower layers, gathering a detailed picture of this part of the island, which has permitted a new interpretation about these rifts. Previous models already identified a main central magma accumulation zone and several shallower high density bodies. The new model allows a better resolution of the pathways that connect both levels and the surface. Our results do not point to any correspondence between the upper parts of these pathways and the rift identified at the surface. Non-clear evidence of progression toward deeper parts into the volcanic system is shown, so we interpret them as very shallow structures, probably originated by local extensional stresses derived from gravitational loading and flank instability, which are used to facilitate the lateral transport of magma when it arrives close to the surface.
An Adaptive ANOVA-based PCKF for High-Dimensional Nonlinear Inverse Modeling
Energy Technology Data Exchange (ETDEWEB)
LI, Weixuan; Lin, Guang; Zhang, Dongxiao
2014-02-01
The probabilistic collocation-based Kalman filter (PCKF) is a recently developed approach for solving inverse problems. It resembles the ensemble Kalman filter (EnKF) in every aspect—except that it represents and propagates model uncertainty by polynomial chaos expansion (PCE) instead of an ensemble of model realizations. Previous studies have shown PCKF is a more efficient alternative to EnKF for many data assimilation problems. However, the accuracy and efficiency of PCKF depends on an appropriate truncation of the PCE series. Having more polynomial chaos bases in the expansion helps to capture uncertainty more accurately but increases computational cost. Bases selection is particularly important for high-dimensional stochastic problems because the number of polynomial chaos bases required to represent model uncertainty grows dramatically as the number of input parameters (random dimensions) increases. In classic PCKF algorithms, the PCE bases are pre-set based on users’ experience. Also, for sequential data assimilation problems, the bases kept in PCE expression remain unchanged in different Kalman filter loops, which could limit the accuracy and computational efficiency of classic PCKF algorithms. To address this issue, we present a new algorithm that adaptively selects PCE bases for different problems and automatically adjusts the number of bases in different Kalman filter loops. The algorithm is based on adaptive functional ANOVA (analysis of variance) decomposition, which approximates a high-dimensional function with the summation of a set of low-dimensional functions. Thus, instead of expanding the original model into PCE, we implement the PCE expansion on these low-dimensional functions, which is much less costly. We also propose a new adaptive criterion for ANOVA that is more suited for solving inverse problems. The new algorithm is tested with different examples and demonstrated great effectiveness in comparison with non-adaptive PCKF and En
An adaptive ANOVA-based PCKF for high-dimensional nonlinear inverse modeling
Energy Technology Data Exchange (ETDEWEB)
Li, Weixuan, E-mail: weixuan.li@usc.edu [Sonny Astani Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, CA 90089 (United States); Lin, Guang, E-mail: guang.lin@pnnl.gov [Pacific Northwest National Laboratory, Richland, WA 99352 (United States); Zhang, Dongxiao, E-mail: dxz@pku.edu.cn [Department of Energy and Resources Engineering, College of Engineering, Peking University, Beijing 100871 (China)
2014-02-01
The probabilistic collocation-based Kalman filter (PCKF) is a recently developed approach for solving inverse problems. It resembles the ensemble Kalman filter (EnKF) in every aspect—except that it represents and propagates model uncertainty by polynomial chaos expansion (PCE) instead of an ensemble of model realizations. Previous studies have shown PCKF is a more efficient alternative to EnKF for many data assimilation problems. However, the accuracy and efficiency of PCKF depends on an appropriate truncation of the PCE series. Having more polynomial chaos basis functions in the expansion helps to capture uncertainty more accurately but increases computational cost. Selection of basis functions is particularly important for high-dimensional stochastic problems because the number of polynomial chaos basis functions required to represent model uncertainty grows dramatically as the number of input parameters (random dimensions) increases. In classic PCKF algorithms, the PCE basis functions are pre-set based on users' experience. Also, for sequential data assimilation problems, the basis functions kept in PCE expression remain unchanged in different Kalman filter loops, which could limit the accuracy and computational efficiency of classic PCKF algorithms. To address this issue, we present a new algorithm that adaptively selects PCE basis functions for different problems and automatically adjusts the number of basis functions in different Kalman filter loops. The algorithm is based on adaptive functional ANOVA (analysis of variance) decomposition, which approximates a high-dimensional function with the summation of a set of low-dimensional functions. Thus, instead of expanding the original model into PCE, we implement the PCE expansion on these low-dimensional functions, which is much less costly. We also propose a new adaptive criterion for ANOVA that is more suited for solving inverse problems. The new algorithm was tested with different examples and
Irving, J.; Koepke, C.; Elsheikh, A. H.
2017-12-01
Bayesian solutions to geophysical and hydrological inverse problems are dependent upon a forward process model linking subsurface parameters to measured data, which is typically assumed to be known perfectly in the inversion procedure. However, in order to make the stochastic solution of the inverse problem computationally tractable using, for example, Markov-chain-Monte-Carlo (MCMC) methods, fast approximations of the forward model are commonly employed. This introduces model error into the problem, which has the potential to significantly bias posterior statistics and hamper data integration efforts if not properly accounted for. Here, we present a new methodology for addressing the issue of model error in Bayesian solutions to hydrogeophysical inverse problems that is geared towards the common case where these errors cannot be effectively characterized globally through some parametric statistical distribution or locally based on interpolation between a small number of computed realizations. Rather than focusing on the construction of a global or local error model, we instead work towards identification of the model-error component of the residual through a projection-based approach. In this regard, pairs of approximate and detailed model runs are stored in a dictionary that grows at a specified rate during the MCMC inversion procedure. At each iteration, a local model-error basis is constructed for the current test set of model parameters using the K-nearest neighbour entries in the dictionary, which is then used to separate the model error from the other error sources before computing the likelihood of the proposed set of model parameters. We demonstrate the performance of our technique on the inversion of synthetic crosshole ground-penetrating radar traveltime data for three different subsurface parameterizations of varying complexity. The synthetic data are generated using the eikonal equation, whereas a straight-ray forward model is assumed in the inversion
Directory of Open Access Journals (Sweden)
А. В. Кирюхин
2017-04-01
Full Text Available Numerical 3D model of Mutnovsky geothermal field (Dachny springs, which consist of 517 elements and partially takes into account double porosity, was developed in 1992-1993 using computer program TOUGH2. Calibration of the model was based on data from test yield of the wells and initial distribution of temperature and pressure in the reservoir. This model was used for techno-economic justification of power plant construction (Mutnovskaya GeoES, 2002. The model was recreated in the program PetraSim v.5.2, the calibration was carried out using additional data on production history before year 2006 and inversion iTOUGH2-EOS1 modeling. Comparison of reservoir parameters, estimated using inversion modeling, with previous parameter estimations (given in brackets showed the following: upflow rate of heat-transfer agent in natural conditions 80.5 (54.1 kg/s, heat flux enthalpy 1430 (1390 kJ/kg, reservoir permeability 27∙10–15-616∙10–15 (3∙10–15-90∙10–15 m2. Inversion modeling was also used to estimate reinjection rates, inflow of meteoric water in the central part of geothermal field and compressibility of reservoir rocks.
Energy Technology Data Exchange (ETDEWEB)
Privette, J.L.
1994-12-31
The angular distribution of radiation scattered by the earth surface contains information on the structural and optical properties of the surface. Potentially, this information may be retrieved through the inversion of surface bidirectional reflectance distribution function (BRDF) models. This report details the limitations and efficient application of BRDF model inversions using data from ground- and satellite-based sensors. A turbid medium BRDF model, based on the discrete ordinates solution to the transport equation, was used to quantify the sensitivity of top-of-canopy reflectance to vegetation and soil parameters. Results were used to define parameter sets for inversions. Using synthetic reflectance values, the invertibility of the model was investigated for different optimization algorithms, surface and sampling conditions. Inversions were also conducted with field data from a ground-based radiometer. First, a soil BRDF model was inverted for different soil and sampling conditions. A condition-invariant solution was determined and used as the lower boundary condition in canopy model inversions. Finally, a scheme was developed to improve the speed and accuracy of inversions.
Zeng, Y.; Schaepman, M.E.; Huang, H.A.; Bruin, de S.; Clevers, J.G.P.W.
2008-01-01
We compare the inversion of two canopy reflectance models to estimate forest crown closure (CC) using an EO-1 Hyperion image: the Kuusk¿Nilson forest reflectance and transmittance (FRT) model, and the Li¿Strahler geometric¿optical model. For predicting CC on a per-pixel basis, the FRT model
GAO, J.; White, M. J.; Bieger, K.; Yen, H.; Arnold, J. G.
2017-12-01
Over the past 20 years, the Soil and Water Assessment Tool (SWAT) has been adopted by many researches to assess water quantity and quality in watersheds around the world. As the demand increases in facilitating model support, maintenance, and future development, the SWAT source code and data have undergone major modifications over the past few years. To make the model more flexible in terms of interactions of spatial units and processes occurring in watersheds, a completely revised version of SWAT (SWAT+) was developed to improve SWAT's ability in water resource modelling and management. There are only several applications of SWAT+ in large watersheds, however, no study pays attention to validate the new model at field level and assess its performance. To test the basic hydrologic function of SWAT+, it was implemented in five field cases across five states in the U.S. and compared the SWAT+ created results with that from the previous models at the same fields. Additionally, an automatic calibration tool was used to test which model is easier to be calibrated well in a limited number of parameter adjustments. The goal of the study was to evaluate the performance of SWAT+ in simulating stream flow on field level at different geographical locations. The results demonstrate that SWAT+ demonstrated similar performance with previous SWAT model, but the flexibility offered by SWAT+ via the connection of different spatial objects can result in a more accurate simulation of hydrological processes in spatial, especially for watershed with artificial facilities. Autocalibration shows that SWAT+ is much easier to obtain a satisfied result compared with the previous SWAT. Although many capabilities have already been enhanced in SWAT+, there exist inaccuracies in simulation. This insufficiency will be improved with advancements in scientific knowledge on hydrologic process in specific watersheds. Currently, SWAT+ is prerelease, and any errors are being addressed.
Inversion of CO and NOx emissions using the adjoint of the IMAGES model
Directory of Open Access Journals (Sweden)
J.-F. Müller
2005-01-01
Full Text Available We use ground-based observations of CO mixing ratios and vertical column abundances together with tropospheric NO2 columns from the GOME satellite instrument as constraints for improving the global annual emission estimates of CO and NOx for the year 1997. The agreement between concentrations calculated by the global 3-dimensional CTM IMAGES and the observations is optimized using the adjoint modelling technique, which allows to invert for CO and NOx fluxes simultaneously, taking their chemical interactions into account. Our analysis quantifies a total of 39 flux parameters, comprising anthropogenic and biomass burning sources over large continental regions, soil and lightning emissions of NOx, biogenic emissions of CO and non-methane hydrocarbons, as well as the deposition velocities of both CO and NOx. Comparison between observed, prior and optimized CO mixing ratios at NOAA/CMDL sites shows that the inversion performs well at the northern mid- and high latitudes, and that it is less efficient in the Southern Hemisphere, as expected due to the scarsity of measurements over this part of the globe. The inversion, moreover, brings the model much closer to the measured NO2 columns over all regions. Sensitivity tests show that anthropogenic sources exhibit weak sensitivity to changes of the a priori errors associated to the bottom-up inventory, whereas biomass burning sources are subject to a strong variability. Our best estimate for the 1997 global top-down CO source amounts to 2760 Tg CO. Anthropogenic emissions increase by 28%, in agreement with previous inverse modelling studies, suggesting that the present bottom-up inventories underestimate the anthropogenic CO emissions in the Northern Hemisphere. The magnitude of the optimized NOx global source decreases by 14% with respect to the prior, and amounts to 42.1 Tg N, out of which 22.8 Tg N are due to anthropogenic sources. The NOx emissions increase over Tropical regions, whereas they decrease
Isomorphs in the phase diagram of a model liquid without inverse power law repulsion
DEFF Research Database (Denmark)
Veldhorst, Arnold Adriaan; Bøhling, Lasse; Dyre, J. C.
2012-01-01
scattering function are calculated. The results are shown to reflect a hidden scale invariance; despite its exponential repulsion the Buckingham potential is well approximated by an inverse power-law plus a linear term in the region of the first peak of the radial distribution function. As a consequence...... the dynamics of the viscous Buckingham liquid is mimicked by a corresponding model with purely repulsive inverse-power-law interactions. The results presented here closely resemble earlier results for Lennard-Jones type liquids, demonstrating that the existence of strong correlations and isomorphs does...... not depend critically on the mathematical form of the repulsion being an inverse power law....
Inverse problem for the mean-field monomer-dimer model with attractive interaction
International Nuclear Information System (INIS)
Contucci, Pierluigi; Luzi, Rachele; Vernia, Cecilia
2017-01-01
The inverse problem method is tested for a class of monomer-dimer statistical mechanics models that contain also an attractive potential and display a mean-field critical point at a boundary of a coexistence line. The inversion is obtained by analytically identifying the parameters in terms of the correlation functions and via the maximum-likelihood method. The precision is tested in the whole phase space and, when close to the coexistence line, the algorithm is used together with a clustering method to take care of the underlying possible ambiguity of the inversion. (paper)
Energy Technology Data Exchange (ETDEWEB)
Aguilo Valentin, Miguel Alejandro [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2016-07-01
This study presents a new nonlinear programming formulation for the solution of inverse problems. First, a general inverse problem formulation based on the compliance error functional is presented. The proposed error functional enables the computation of the Lagrange multipliers, and thus the first order derivative information, at the expense of just one model evaluation. Therefore, the calculation of the Lagrange multipliers does not require the solution of the computationally intensive adjoint problem. This leads to significant speedups for large-scale, gradient-based inverse problems.
Inverse problem for the mean-field monomer-dimer model with attractive interaction
Contucci, Pierluigi; Luzi, Rachele; Vernia, Cecilia
2017-05-01
The inverse problem method is tested for a class of monomer-dimer statistical mechanics models that contain also an attractive potential and display a mean-field critical point at a boundary of a coexistence line. The inversion is obtained by analytically identifying the parameters in terms of the correlation functions and via the maximum-likelihood method. The precision is tested in the whole phase space and, when close to the coexistence line, the algorithm is used together with a clustering method to take care of the underlying possible ambiguity of the inversion.
Modeling and inverse feedforward control for conducting polymer actuators with hysteresis
International Nuclear Information System (INIS)
Wang, Xiangjiang; Alici, Gursel; Tan, Xiaobo
2014-01-01
Conducting polymer actuators are biocompatible with a small footprint, and operate in air or liquid media under low actuation voltages. This makes them excellent actuators for macro- and micro-manipulation devices, however, their positioning ability or accuracy is adversely affected by their hysteresis non-linearity under open-loop control strategies. In this paper, we establish a hysteresis model for conducting polymer actuators, based on a rate-independent hysteresis model known as the Duhem model. The hysteresis model is experimentally identified and integrated with the linear dynamics of the actuator. This combined model is inverted to control the displacement of the tri-layer actuators considered in this study, without using any external feedback. The inversion requires an inverse hysteresis model which was experimentally identified using an inverse neural network model. Experimental results show that the position tracking errors are reduced by more than 50% when the hysteresis inverse model is incorporated into an inversion-based feedforward controller, indicating the potential of the proposed method in enabling wider use of such smart actuators. (paper)
Directory of Open Access Journals (Sweden)
Pablo D. Mininni
2012-01-01
Full Text Available In the context of tackling the ill-posed inverse problem of motion estimation from image sequences, we propose to introduce prior knowledge on flow regularity given by turbulence statistical models. Prior regularity is formalised using turbulence power laws describing statistically self-similar structure of motion increments across scales. The motion estimation method minimises the error of an image observation model while constraining second-order structure function to behave as a power law within a prescribed range. Thanks to a Bayesian modelling framework, the motion estimation method is able to jointly infer the most likely power law directly from image data. The method is assessed on velocity fields of 2-D or quasi-2-D flows. Estimation accuracy is first evaluated on a synthetic image sequence of homogeneous and isotropic 2-D turbulence. Results obtained with the approach based on physics of fluids outperform state-of-the-art. Then, the method analyses atmospheric turbulence using a real meteorological image sequence. Selecting the most likely power law model enables the recovery of physical quantities, which are of major interest for turbulence atmospheric characterisation. In particular, from meteorological images we are able to estimate energy and enstrophy fluxes of turbulent cascades, which are in agreement with previous in situ measurements.
Inverse modelling of national and European CH4 emissions using the atmospheric zoom model TM5
Directory of Open Access Journals (Sweden)
P. Bergamaschi
2005-01-01
Full Text Available A synthesis inversion based on the atmospheric zoom model TM5 is used to derive top-down estimates of CH4 emissions from individual European countries for the year 2001. We employ a model zoom over Europe with 1° × 1° resolution that is two-way nested into the global model domain (with resolution of 6° × 4°. This approach ensures consistent boundary conditions for the zoom domain and thus European top-down estimates consistent with global CH4 observations. The TM5 model, driven by ECMWF analyses, simulates synoptic scale events at most European and global sites fairly well, and the use of high-frequency observations allows exploiting the information content of individual synoptic events. A detailed source attribution is presented for a comprehensive set of 56 monitoring sites, assigning the atmospheric signal to the emissions of individual European countries and larger global regions. The available observational data put significant constraints on emissions from different regions. Within Europe, in particular several Western European countries are well constrained. The inversion results suggest up to 50-90% higher anthropogenic CH4 emissions in 2001 for Germany, France and UK compared to reported UNFCCC values (EEA, 2003. A recent revision of the German inventory, however, resulted in an increase of reported CH4 emissions by 68.5% (EEA, 2004, being now in very good agreement with our top-down estimate. The top-down estimate for Finland is distinctly smaller than the a priori estimate, suggesting much smaller CH4 emissions from Finnish wetlands than derived from the bottom-up inventory. The EU-15 totals are relatively close to UNFCCC values (within 4-30% and appear very robust for different inversion scenarios.
Global Monthly CO2 Flux Inversion Based on Results of Terrestrial Ecosystem Modeling
Deng, F.; Chen, J.; Peters, W.; Krol, M.
2008-12-01
Most of our understanding of the sources and sinks of atmospheric CO2 has come from inverse studies of atmospheric CO2 concentration measurements. However, the number of currently available observation stations and our ability to simulate the diurnal planetary boundary layer evolution over continental regions essentially limit the number of regions that can be reliably inverted globally, especially over continental areas. In order to overcome these restrictions, a nested inverse modeling system was developed based on the Bayesian principle for estimating carbon fluxes of 30 regions in North America and 20 regions for the rest of the globe. Inverse modeling was conducted in monthly steps using CO2 concentration measurements of 5 years (2000 - 2005) with the following two models: (a) An atmospheric transport model (TM5) is used to generate the transport matrix where the diurnal variation n of atmospheric CO2 concentration is considered to enhance the use of the afternoon-hour average CO2 concentration measurements over the continental sites. (b) A process-based terrestrial ecosystem model (BEPS) is used to produce hourly step carbon fluxes, which could minimize the limitation due to our inability to solve the inverse problem in a high resolution, as the background of our inversion. We will present our recent results achieved through a combination of the bottom-up modeling with BEPS and the top-down modeling based on TM5 driven by offline meteorological fields generated by the European Centre for Medium Range Weather Forecast (ECMFW).
DEFF Research Database (Denmark)
Tordrup, Karl Woldum; Poulsen, Uffe Vestergaard; Nielsen, Carsten
2017-01-01
We use a modular approach to develop a TRNSYS model for a district heating facility by applying inverse modelling to one year of operational data for individual components. We assemble the components into a single TRNSYS model for the full system using the accumulation tanks as a central hub...
Inverse modeling and animation of growing single-stemmed trees at interactive rates
S. Rudnick; L. Linsen; E.G. McPherson
2007-01-01
For city planning purposes, animations of growing trees of several species can be used to deduce which species may best fit a particular environment. The models used for the animation must conform to real measured data. We present an approach for inverse modeling to fit global growth parameters. The model comprises local production rules, which are iteratively and...
DEFF Research Database (Denmark)
Tordrup, Karl Woldum; Poulsen, Uffe Vestergaard; Nielsen, Carsten
2017-01-01
We use a modular approach to develop a TRNSYS model for a district heating facility by applying inverse modelling to one year of operational data for individual components. We assemble the components into a single TRNSYS model for the full system using the accumulation tanks as a central hub conn...
International Nuclear Information System (INIS)
McMurray, J. S.; Williams, C. C.
1998-01-01
Scanning Capacitance Microscopy (SCM) is capable of providing two-dimensional information about dopant and carrier concentrations in semiconducting devices. This information can be used to calibrate models used in the simulation of these devices prior to manufacturing and to develop and optimize the manufacturing processes. To provide information for future generations of devices, ultra-high spatial accuracy (<10 nm) will be required. One method, which potentially provides a means to obtain these goals, is inverse modeling of SCM data. Current semiconducting devices have large dopant gradients. As a consequence, the capacitance probe signal represents an average over the local dopant gradient. Conversion of the SCM signal to dopant density has previously been accomplished with a physical model which assumes that no dopant gradient exists in the sampling area of the tip. The conversion of data using this model produces results for abrupt profiles which do not have adequate resolution and accuracy. A new inverse model and iterative method has been developed to obtain higher resolution and accuracy from the same SCM data. This model has been used to simulate the capacitance signal obtained from one and two-dimensional ideal abrupt profiles. This simulated data has been input to a new iterative conversion algorithm, which has recovered the original profiles in both one and two dimensions. In addition, it is found that the shape of the tip can significantly impact resolution. Currently SCM tips are found to degrade very rapidly. Initially the apex of the tip is approximately hemispherical, but quickly becomes flat. This flat region often has a radius of about the original hemispherical radius. This change in geometry causes the silicon directly under the disk to be sampled with approximately equal weight. In contrast, a hemispherical geometry samples most strongly the silicon centered under the SCM tip and falls off quickly with distance from the tip's apex. Simulation
Gu, Xiaoyu; Yu, Yang; Li, Jianchun; Li, Yancheng
2017-10-01
Magnetorheological elastomer (MRE) base isolations have attracted considerable attention over the last two decades thanks to its self-adaptability and high-authority controllability in semi-active control realm. Due to the inherent nonlinearity and hysteresis of the devices, it is challenging to obtain a reasonably complicated mathematical model to describe the inverse dynamics of MRE base isolators and hence to realise control synthesis of the MRE base isolation system. Two aims have been achieved in this paper: i) development of an inverse model for MRE base isolator based on optimal general regression neural network (GRNN); ii) numerical and experimental validation of a real-time semi-active controlled MRE base isolation system utilising LQR controller and GRNN inverse model. The superiority of GRNN inverse model lays in fewer input variables requirement, faster training process and prompt calculation response, which makes it suitable for online training and real-time control. The control system is integrated with a three-storey shear building model and control performance of the MRE base isolation system is compared with bare building, passive-on isolation system and passive-off isolation system. Testing results show that the proposed GRNN inverse model is able to reproduce desired control force accurately and the MRE base isolation system can effectively suppress the structural responses when compared to the passive isolation system.
Heeding the waveform inversion nonlinearity by unwrapping the model and data
Alkhalifah, Tariq Ali
2012-01-01
Unlike traveltime inversion, waveform inversion provides relatively higher-resolution inverted models. This feature, however, comes at the cost of introducing complex nonlinearity to the inversion operator complicating the convergence process. We use unwrapped-phase-based objective functions to reduce such nonlinearity in a domain in which the high-frequency component is given by the traveltime inversion. Such information is packaged in a frequency-dependent attribute (or traveltime) that can be easily manipulated at different frequencies. It unwraps the phase of the wavefield yielding far less nonlinearity in the objective function than those experienced with the conventional misfit objective function, and yet it still holds most of the critical waveform information in its frequency dependency. However, it suffers from nonlinearity introduced by the model (or reflectivity), as events interact with each other (something like cross talk). This stems from the sinusoidal nature of the band-limited reflectivity model. Unwrapping the phase for such a model can mitigate this nonlinearity as well. Specifically, a simple modification to the inverted domain (or model), can reduce the effect of the model-induced nonlinearity and, thus, make the inversion more convergent. Simple examples are used to highlight such features.
An iterative inverse method to estimate basal topography and initialize ice flow models
Directory of Open Access Journals (Sweden)
W. J. J. van Pelt
2013-06-01
Full Text Available We evaluate an inverse approach to reconstruct distributed bedrock topography and simultaneously initialize an ice flow model. The inverse method involves an iterative procedure in which an ice dynamical model (PISM is run multiple times over a prescribed period, while being forced with space- and time-dependent climate input. After every iteration bed heights are adjusted using information of the remaining misfit between observed and modeled surface topography. The inverse method is first applied in synthetic experiments with a constant climate forcing to verify convergence and robustness of the approach in three dimensions. In a next step, the inverse approach is applied to Nordenskiöldbreen, Svalbard, forced with height- and time-dependent climate input since 1300 AD. An L-curve stopping criterion is used to prevent overfitting. Validation against radar data reveals a high correlation (up to R = 0.89 between modeled and observed thicknesses. Remaining uncertainties can mainly be ascribed to inaccurate model physics, in particular, uncertainty in the description of sliding. Results demonstrate the applicability of this inverse method to reconstruct the ice thickness distribution of glaciers and ice caps. In addition to reconstructing bedrock topography, the method provides a direct tool to initialize ice flow models for forecasting experiments.
Inverse modeling of the terrestrial carbon flux in China with flux covariance among inverted regions
Wang, H.; Jiang, F.; Chen, J. M.; Ju, W.; Wang, H.
2011-12-01
Quantitative understanding of the role of ocean and terrestrial biosphere in the global carbon cycle, their response and feedback to climate change is required for the future projection of the global climate. China has the largest amount of anthropogenic CO2 emission, diverse terrestrial ecosystems and an unprecedented rate of urbanization. Thus information on spatial and temporal distributions of the terrestrial carbon flux in China is of great importance in understanding the global carbon cycle. We developed a nested inversion with focus in China. Based on Transcom 22 regions for the globe, we divide China and its neighboring countries into 17 regions, making 39 regions in total for the globe. A Bayesian synthesis inversion is made to estimate the terrestrial carbon flux based on GlobalView CO2 data. In the inversion, GEOS-Chem is used as the transport model to develop the transport matrix. A terrestrial ecosystem model named BEPS is used to produce the prior surface flux to constrain the inversion. However, the sparseness of available observation stations in Asia poses a challenge to the inversion for the 17 small regions. To obtain additional constraint on the inversion, a prior flux covariance matrix is constructed using the BEPS model through analyzing the correlation in the net carbon flux among regions under variable climate conditions. The use of the covariance among different regions in the inversion effectively extends the information content of CO2 observations to more regions. The carbon flux over the 39 land and ocean regions are inverted for the period from 2004 to 2009. In order to investigate the impact of introducing the covariance matrix with non-zero off-diagonal values to the inversion, the inverted terrestrial carbon flux over China is evaluated against ChinaFlux eddy-covariance observations after applying an upscaling methodology.
Modelling of Data Stream Time Characteristics for Use of Inverse Multiplexer
Directory of Open Access Journals (Sweden)
Petr Jares
2014-01-01
Full Text Available Today, increasing of the transmission rate in a telecommunications network is possible in various ways. One of them is inverse multiplexing. The inverse multiplexer divides a data stream to multiple parallel channels. This principle not only allows to increase the total available transmission rate, but also allows to reduce the error rate and interruption in data stream. The digital subscriber line may be used for the implementation of the inverse multiplex. Accurate knowledge of the transmission parameters of a digital subscriber line and the entire infrastructure of the network provider is necessary for the effective functioning of the terminal device with inverse multiplexing. It is necessary to know not only the parameters related to the transmission rate, but above all the parameters relevant to the time characteristics of data transmission. This paper describes how to obtain the transmission parameters of real digital subscriber lines and their modelling.
Three-dimensional electromagnetic modeling and inversion on massively parallel computers
Energy Technology Data Exchange (ETDEWEB)
Newman, G.A.; Alumbaugh, D.L. [Sandia National Labs., Albuquerque, NM (United States). Geophysics Dept.
1996-03-01
This report has demonstrated techniques that can be used to construct solutions to the 3-D electromagnetic inverse problem using full wave equation modeling. To this point great progress has been made in developing an inverse solution using the method of conjugate gradients which employs a 3-D finite difference solver to construct model sensitivities and predicted data. The forward modeling code has been developed to incorporate absorbing boundary conditions for high frequency solutions (radar), as well as complex electrical properties, including electrical conductivity, dielectric permittivity and magnetic permeability. In addition both forward and inverse codes have been ported to a massively parallel computer architecture which allows for more realistic solutions that can be achieved with serial machines. While the inversion code has been demonstrated on field data collected at the Richmond field site, techniques for appraising the quality of the reconstructions still need to be developed. Here it is suggested that rather than employing direct matrix inversion to construct the model covariance matrix which would be impossible because of the size of the problem, one can linearize about the 3-D model achieved in the inverse and use Monte-Carlo simulations to construct it. Using these appraisal and construction tools, it is now necessary to demonstrate 3-D inversion for a variety of EM data sets that span the frequency range from induction sounding to radar: below 100 kHz to 100 MHz. Appraised 3-D images of the earth`s electrical properties can provide researchers opportunities to infer the flow paths, flow rates and perhaps the chemistry of fluids in geologic mediums. It also offers a means to study the frequency dependence behavior of the properties in situ. This is of significant relevance to the Department of Energy, paramount to characterizing and monitoring of environmental waste sites and oil and gas exploration.
DEFF Research Database (Denmark)
Yoon, Daeung; Zhdanov, Michael; Cai, Hongzhu
2015-01-01
One of the major problems in the modeling and inversion of marine controlled source electromagnetic (MCSEM) data is related to the need for accurate representation of very complex geoelectrical models typical for marine environment. At the same time, the corresponding forward modeling algorithms...... should be powerful and fast enough to be suitable for repeated use in hundreds of iterations of the inversion and for multiple transmitter/receiver positions. To this end, we have developed a novel 3D modeling and inversion approach, which combines the advantages of the finite difference (FD......) and integral equation (IE) methods. In the framework of this approach, we solve the Maxwell's equations for anomalous electric fields using the FD approximation on a staggered grid. Once the unknown electric fields in the computation domain of the FD method are computed, the electric and magnetic fields...
Neural Network modeling of forward and inverse behavior of rotary MR damper
DEFF Research Database (Denmark)
Bhowmik, Subrata; Høgsberg, Jan Becker; Weber, Felix
2010-01-01
produced is transformed into a translational force through the crank shaft mechanism. A feed-forward back propagation neural network is used to model both the forward and the inverse dynamics of the MR damper. The forward model output is the estimated force and therefore can be used later as observer....... The inverse model is needed to solve the force tracking task when the MR damper is used for structural damping. The training and validation data are obtained from tests of the MR damper on a hydraulic test set-up for sinusoidal and triangular displacement at different frequencies and amplitudes...... velocity and current and the force as the goal. The velocity is derived from the measured displacement by numerical differentiation which requires additional low pass filtering besides the nominal filtering of the measured states to remove measurement noise and offsets. The inverse model is trained...
Applications of Markov random field models for inversion problems in geosciences
Kuwatani, T.; Nagata, K.; Okada, M.; Toriumi, M.
2012-12-01
Recently, a variety of spatial and temporal data sets can be obtained thanks to technological advances of measurement and observation in geosciences. It is very important to inverse spatial or temporal physical variables from these imaging data sets. The Markov random field (MRF) model is a stochastic model using Markov chains that is often applied for image restoration and pattern recognition in information science. In the MRF model, the spatial or temporal variations of physical properties are assumed to be relatively small compared to the observational noise and analytical uncertainty. By the Bayesian approach, the MRF model appropriately filters out high-frequency noise, and we can obtain accurate spatial distributions or time series of physical properties. Furthermore, it has the potential advantage of the incorporation of prior geophysical and geological information through the evaluation function. The purpose of this study is to develop the MRF model in order to apply it to various inversion problems in geosciences. Based on the Bayesian inference, we incorporated the nonlinear generation process of observational data sets into the MRF model. The Markov chain Monte Carlo (MCMC) algorithm was implemented to estimate hyperparameters and optimize target variables. Furthermore, it's important for inversion problems in geosciences to understand discontinuous behavior of physical variables, for example, detection of fault planes and lithospheric boundaries in the earth's interior. By introducing Potts spins as latent variables to the MRF model, we can simultaneously estimate the distributions of continuous and discontinuous variables. For examples of applications, we will introduce two inversion problems: one is a pressure-temperature inversion from compositional data of zoned minerals, and the other is an inversion of fluid distributions from observed seismic velocity structure. Based on these examples, we will discuss effectiveness and broad applicability of the
The Importance of Business Model Factors for Cloud Computing Adoption: Role of Previous Experiences
Directory of Open Access Journals (Sweden)
Bogataj Habjan Kristina
2017-08-01
Full Text Available Background and Purpose: Bringing several opportunities for more effective and efficient IT governance and service exploitation, cloud computing is expected to impact the European and global economies significantly. Market data show that despite many advantages and promised benefits the adoption of cloud computing is not as fast and widespread as foreseen. This situation shows the need for further exploration of the potentials of cloud computing and its implementation on the market. The purpose of this research was to identify individual business model factors with the highest impact on cloud computing adoption. In addition, the aim was to identify the differences in opinion regarding the importance of business model factors on cloud computing adoption according to companies’ previous experiences with cloud computing services.
pyGIMLi: An open-source library for modelling and inversion in geophysics
Rücker, Carsten; Günther, Thomas; Wagner, Florian M.
2017-12-01
Many tasks in applied geosciences cannot be solved by single measurements, but require the integration of geophysical, geotechnical and hydrological methods. Numerical simulation techniques are essential both for planning and interpretation, as well as for the process understanding of modern geophysical methods. These trends encourage open, simple, and modern software architectures aiming at a uniform interface for interdisciplinary and flexible modelling and inversion approaches. We present pyGIMLi (Python Library for Inversion and Modelling in Geophysics), an open-source framework that provides tools for modelling and inversion of various geophysical but also hydrological methods. The modelling component supplies discretization management and the numerical basis for finite-element and finite-volume solvers in 1D, 2D and 3D on arbitrarily structured meshes. The generalized inversion framework solves the minimization problem with a Gauss-Newton algorithm for any physical forward operator and provides opportunities for uncertainty and resolution analyses. More general requirements, such as flexible regularization strategies, time-lapse processing and different sorts of coupling individual methods are provided independently of the actual methods used. The usage of pyGIMLi is first demonstrated by solving the steady-state heat equation, followed by a demonstration of more complex capabilities for the combination of different geophysical data sets. A fully coupled hydrogeophysical inversion of electrical resistivity tomography (ERT) data of a simulated tracer experiment is presented that allows to directly reconstruct the underlying hydraulic conductivity distribution of the aquifer. Another example demonstrates the improvement of jointly inverting ERT and ultrasonic data with respect to saturation by a new approach that incorporates petrophysical relations in the inversion. Potential applications of the presented framework are manifold and include time
Inverse modelling of European N2O emissions: assimilating observations from different networks
Corazza, M.; Bergamaschi, P.; Vermeulen, A.T.; Krol, M.C.
2011-01-01
We describe the setup and first results of an inverse modelling system for atmospheric N2O, based on a four-dimensional variational (4DVAR) technique and the atmospheric transport zoom model TM5. We focus in this study on the European domain, utilizing a comprehensive set of quasi-continuous
Data and modelling requirements for CO2 inversions using high-frequency data
International Nuclear Information System (INIS)
Law, R.M.; Rayner, P.J.; Steele, L.P.; Enting, I.G.
2003-01-01
We explore the future possibilities for CO 2 source estimation from atmospheric concentration data by performing synthetic data experiments. Synthetic data are used to test seasonal CO 2 inversions using high-frequency data. Monthly CO 2 sources over the Australian region are calculated for inversions with data at 4-hourly frequency and averaged over 1 d, 2.5 d, 5 d, 12.17 d and 1 month. The inversion quality, as determined by bias and uncertainty, is degraded when averaging over longer periods. This shows the value of the strong but relatively short-lived signals present in high-frequency records that are removed in averaged and particularly filtered records. Sensitivity tests are performed in which the synthetic data are 'corrupted' to simulate systematic measurement errors such as intercalibration differences or to simulate transport modelling errors. The inversion is also used to estimate the effect of calibration offsets between sites. We find that at short data-averaging periods the inversion is reasonably robust to measurement-type errors. For transport-type errors, the best results are achieved for synoptic (2-5 d) timescales. Overall the tests indicate that improved source estimates should be possible by incorporating continuous measurements into CO 2 inversions
Micromechanical modeling and inverse identification of damage using cohesive approaches
International Nuclear Information System (INIS)
Blal, Nawfal
2013-01-01
In this study a micromechanical model is proposed for a collection of cohesive zone models embedded between two each elements of a standard cohesive-volumetric finite element method. An equivalent 'matrix-inclusions' composite is proposed as a representation of the cohesive-volumetric discretization. The overall behaviour is obtained using homogenization approaches (Hashin Shtrikman scheme and the P. Ponte Castaneda approach). The derived model deals with elastic, brittle and ductile materials. It is available whatever the triaxiality loading rate and the shape of the cohesive law, and leads to direct relationships between the overall material properties and the local cohesive parameters and the mesh density. First, rigorous bounds on the normal and tangential cohesive stiffnesses are obtained leading to a suitable control of the inherent artificial elastic loss induced by intrinsic cohesive models. Second, theoretical criteria on damageable and ductile cohesive parameters are established (cohesive peak stress, critical separation, cohesive failure energy,... ). These criteria allow a practical calibration of the cohesive zone parameters as function of the overall material properties and the mesh length. The main interest of such calibration is its promising capacity to lead to a mesh-insensitive overall response in surface damage. (author) [fr
Directory of Open Access Journals (Sweden)
A.A. Fahmy
2013-12-01
Full Text Available This paper presents a new neuro-fuzzy controller for robot manipulators. First, an inductive learning technique is applied to generate the required inverse modeling rules from input/output data recorded in the off-line structure learning phase. Second, a fully differentiable fuzzy neural network is developed to construct the inverse dynamics part of the controller for the online parameter learning phase. Finally, a fuzzy-PID-like incremental controller was employed as Feedback servo controller. The proposed control system was tested using dynamic model of a six-axis industrial robot. The control system showed good results compared to the conventional PID individual joint controller.
Modeling and inversion Matlab algorithms for resistivity, induced polarization and seismic data
Karaoulis, M.; Revil, A.; Minsley, B. J.; Werkema, D. D.
2011-12-01
M. Karaoulis (1), D.D. Werkema (3), A. Revil (1,2), A., B. Minsley (4), (1) Colorado School of Mines, Dept. of Geophysics, Golden, CO, USA. (2) ISTerre, CNRS, UMR 5559, Université de Savoie, Equipe Volcan, Le Bourget du Lac, France. (3) U.S. EPA, ORD, NERL, ESD, CMB, Las Vegas, Nevada, USA . (4) USGS, Federal Center, Lakewood, 10, 80225-0046, CO. Abstract We propose 2D and 3D forward modeling and inversion package for DC resistivity, time domain induced polarization (IP), frequency-domain IP, and seismic refraction data. For the resistivity and IP case, discretization is based on rectangular cells, where each cell has as unknown resistivity in the case of DC modelling, resistivity and chargeability in the time domain IP modelling, and complex resistivity in the spectral IP modelling. The governing partial-differential equations are solved with the finite element method, which can be applied to both real and complex variables that are solved for. For the seismic case, forward modeling is based on solving the eikonal equation using a second-order fast marching method. The wavepaths are materialized by Fresnel volumes rather than by conventional rays. This approach accounts for complicated velocity models and is advantageous because it considers frequency effects on the velocity resolution. The inversion can accommodate data at a single time step, or as a time-lapse dataset if the geophysical data are gathered for monitoring purposes. The aim of time-lapse inversion is to find the change in the velocities or resistivities of each model cell as a function of time. Different time-lapse algorithms can be applied such as independent inversion, difference inversion, 4D inversion, and 4D active time constraint inversion. The forward algorithms are benchmarked against analytical solutions and inversion results are compared with existing ones. The algorithms are packaged as Matlab codes with a simple Graphical User Interface. Although the code is parallelized for multi
Locatelli, Robin; Bousquet, Philippe; Chevallier, Frédéric
2013-04-01
Since the nineties, inverse modelling by assimilating atmospheric measurements into a chemical transport model (CTM) has been used to derive sources and sinks of atmospheric trace gases. More recently, the high global warming potential of methane (CH4) and unexplained variations of its atmospheric mixing ratio caught the attention of several research groups. Indeed, the diversity and the variability of methane sources induce high uncertainty on the present and the future evolution of CH4 budget. With the increase of available measurement data to constrain inversions (satellite data, high frequency surface and tall tower observations, FTIR spectrometry,...), the main limiting factor is about to become the representation of atmospheric transport in CTMs. Indeed, errors in transport modelling directly converts into flux changes when assuming perfect transport in atmospheric inversions. Hence, we propose an inter-model comparison in order to quantify the impact of transport and modelling errors on the CH4 fluxes estimated into a variational inversion framework. Several inversion experiments are conducted using the same set-up (prior emissions, measurement and prior errors, OH field, initial conditions) of the variational system PYVAR, developed at LSCE (Laboratoire des Sciences du Climat et de l'Environnement, France). Nine different models (ACTM, IFS, IMPACT, IMPACT1x1, MOZART, PCTM, TM5, TM51x1 and TOMCAT) used in TRANSCOM-CH4 experiment (Patra el al, 2011) provide synthetic measurements data at up to 280 surface sites to constrain the inversions performed using the PYVAR system. Only the CTM (and the meteorological drivers which drive them) used to create the pseudo-observations vary among inversions. Consequently, the comparisons of the nine inverted methane fluxes obtained for 2005 give a good order of magnitude of the impact of transport and modelling errors on the estimated fluxes with current and future networks. It is shown that transport and modelling errors
Model study of the compact gravity reconstruction; Juryoku inversion `CGR` no model kento
Energy Technology Data Exchange (ETDEWEB)
Ishii, Y.; Muraoka, A. [Sogo Geophysical Exploration Co. Ltd., Tokyo (Japan)
1996-05-01
An examination was made on gravity inversion using a compact gravity reconstruction (CGR) method in gravity tomography analysis. In a model analysis, an analytical region of 100m{times}50m was divided into cells of 10m{times}10m, on the assumption that two density anomalous bodies with a density difference of 1.0g/cm{sup 3} existed with one shallow and the other deep density distribution. The result of the analysis revealed that, in a linear analysis by a general inverse matrix, blurs and blotting were plenty with a tendency of making gravity anomaly attributable to an anomalous distribution of shallow density; that CGR provided a large effect in making a clear contrast of an anomalous part; that, where structures of shallow and deep density anomalies existed, the analysis by CGR was inferior in the restoration of a deep structure with errors enlarged; that, if a gravity traverse was taken long compared with the distribution depth of density anomalies, the analytical precision of a deep part was improved; that an analytical convergence was better with the restriction of density difference given on the large side than on the small side; and so on. 3 refs., 10 figs.
Inverse geothermal modelling applied to Danish sedimentary basins
DEFF Research Database (Denmark)
Poulsen, Soren E.; Balling, Niels; Bording, Thue S.
2017-01-01
. The numerical model was utilized for predicting and contouring temperatures at 2000 and 3000 m depths and for two main geothermal reservoir units, the Gassum (Lower Jurassic-Upper Triassic) and Bunter/Skagerrak (Triassic) reservoirs, both currently utilized for geothermal energy production. Temperature...... gradients to depths of 2000-3000 m are generally around 25-30. degrees C km(-1), locally up to about 35. degrees C km(-1). Large regions have geothermal reservoirs with characteristic temperatures ranging from ca. 40-50. degrees C, at 1000-1500 m depth, to ca. 80-110. degrees C, at 2500-3500 m, however...
Acoustic model order reduction for the lowest condition number in inverse method
Madoliat, Reza; Nouri, Nowrouz Mohammad; Rahrovi, Ali
2017-06-01
Acoustic sources with wide surfaces can be broken down in a fluid environment into smaller acoustic sources. In this study, a general model is presented, indicating the type, number, direction, position and strength of these sources in such a way that the main sound and the sound of the equivalent sources match each other acceptably. When the position and direction of the source is determined, the strength of the source can be found using the inverse method. However, since the solution is not unique in the inverse method, a different acoustic strength is obtained for the sources if different positions are selected. By selecting an arrangement of general sources and using an optimization algorithm, the least possible mismatch between the main sound and the sound of equivalent sources can be achieved. In the inverse method, it is important to reduce the effects of measurement errors. The sensor placement and acoustic model order reduction (AMOR) are studied for reducing these effects.
Sabbagh, Harold A; Sabbagh, Elias H; Aldrin, John C; Knopp, Jeremy S
2013-01-01
Computational Electromagnetics and Model-Based Inversion: A Modern Paradigm for Eddy Current Nondestructive Evaluation describes the natural marriage of the computer to eddy-current NDE. Three distinct topics are emphasized in the book: (a) fundamental mathematical principles of volume-integral equations as a subset of computational electromagnetics, (b) mathematical algorithms applied to signal-processing and inverse scattering problems, and (c) applications of these two topics to problems in which real and model data are used. By showing how mathematics and the computer can solve problems more effectively than current analog practices, this book defines the modern technology of eddy-current NDE. This book will be useful to advanced students and practitioners in the fields of computational electromagnetics, electromagnetic inverse-scattering theory, nondestructive evaluation, materials evaluation and biomedical imaging. Users of eddy-current NDE technology in industries as varied as nuclear power, aerospace,...
American Option Pricing using GARCH models and the Normal Inverse Gaussian distribution
DEFF Research Database (Denmark)
Stentoft, Lars Peter
In this paper we propose a feasible way to price American options in a model with time varying volatility and conditional skewness and leptokurtosis using GARCH processes and the Normal Inverse Gaussian distribution. We show how the risk neutral dynamics can be obtained in this model, we interpre....... In particular, improvements are found when considering the smile in implied standard deviations.......In this paper we propose a feasible way to price American options in a model with time varying volatility and conditional skewness and leptokurtosis using GARCH processes and the Normal Inverse Gaussian distribution. We show how the risk neutral dynamics can be obtained in this model, we interpret...... the effect of the riskneutralization, and we derive approximation procedures which allow for a computationally efficient implementation of the model. When the model is estimated on financial returns data the results indicate that compared to the Gaussian case the extension is important. A study of the model...
Theoretical study on the inverse modeling of deep body temperature measurement
International Nuclear Information System (INIS)
Huang, Ming; Chen, Wenxi
2012-01-01
We evaluated the theoretical aspects of monitoring the deep body temperature distribution with the inverse modeling method. A two-dimensional model was built based on anatomical structure to simulate the human abdomen. By integrating biophysical and physiological information, the deep body temperature distribution was estimated from cutaneous surface temperature measurements using an inverse quasilinear method. Simulations were conducted with and without the heat effect of blood perfusion in the muscle and skin layers. The results of the simulations showed consistently that the noise characteristics and arrangement of the temperature sensors were the major factors affecting the accuracy of the inverse solution. With temperature sensors of 0.05 °C systematic error and an optimized 16-sensor arrangement, the inverse method could estimate the deep body temperature distribution with an average absolute error of less than 0.20 °C. The results of this theoretical study suggest that it is possible to reconstruct the deep body temperature distribution with the inverse method and that this approach merits further investigation. (paper)
Application of random seismic inversion method based on tectonic model in thin sand body research
Dianju, W.; Jianghai, L.; Qingkai, F.
2017-12-01
The oil and gas exploitation at Songliao Basin, Northeast China have already progressed to the period with high water production. The previous detailed reservoir description that based on seismic image, sediment core, borehole logging has great limitations in small scale structural interpretation and thin sand body characterization. Thus, precise guidance for petroleum exploration is badly in need of a more advanced method. To do so, we derived the method of random seismic inversion constrained by tectonic model.It can effectively improve the depicting ability of thin sand bodies, combining numerical simulation techniques, which can credibly reducing the blindness of reservoir analysis from the whole to the local and from the macroscopic to the microscopic. At the same time, this can reduce the limitations of the study under the constraints of different geological conditions of the reservoir, accomplish probably the exact estimation for the effective reservoir. Based on the research, this paper has optimized the regional effective reservoir evaluation and the productive location adjustment of applicability, combined with the practical exploration and development in Aonan oil field.
Energy Technology Data Exchange (ETDEWEB)
Bledsoe, Keith C. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2015-04-01
The DiffeRential Evolution Adaptive Metropolis (DREAM) method is a powerful optimization/uncertainty quantification tool used to solve inverse transport problems in Los Alamos National Laboratory’s INVERSE code system. The DREAM method has been shown to be adept at accurate uncertainty quantification, but it can be very computationally demanding. Previously, the DREAM method in INVERSE performed a user-defined number of particle transport calculations. This placed a burden on the user to guess the number of calculations that would be required to accurately solve any given problem. This report discusses a new approach that has been implemented into INVERSE, the Gelman-Rubin convergence metric. This metric automatically detects when an appropriate number of transport calculations have been completed and the uncertainty in the inverse problem has been accurately calculated. In a test problem with a spherical geometry, this method was found to decrease the number of transport calculations (and thus time required) to solve a problem by an average of over 90%. In a cylindrical test geometry, a 75% decrease was obtained.
Inverse modeling of cloud-aerosol interactions -- Part 1: Detailed response surface analysis
Partridge, D.G.; Vrugt, J.A.; Tunved, P.; Ekman, A.M.L.; Gorea, D.; Sooroshian, A.
2011-01-01
New methodologies are required to probe the sensitivity of parameters describing cloud droplet activation. This paper presents an inverse modeling-based method for exploring cloud-aerosol interactions via response surfaces. The objective function, containing the difference between the measured and
The LXR inverse agonist SR9238 suppresses fibrosis in a model of non-alcoholic steatohepatitis
Directory of Open Access Journals (Sweden)
Kristine Griffett
2015-04-01
Conclusions: Here, we demonstrate that an LXR inverse agonist, SR9238, is effective in reduction of hepatic steatosis, inflammation and fibrosis in an animal model of NASH. These results have important implications for the development of therapeutics for treatment NASH in humans.
Estimation of root water uptake parameters by inverse modeling with soil water content data
Hupet, F.; Lambot, S.; Feddes, R.A.; Dam, van J.C.; Vanclooster, M.
2003-01-01
In this paper we have tested the feasibility of the inverse modeling approach to derive root water uptake parameters (RWUP) from soil water content data using numerical experiments for three differently textured soils and for an optimal drying period. The RWUP of interest are the rooting depth and
Bergamaschi, P.; Frankenberg, C.; Meirink, J.F.; Krol, M.C.; Dentener, F.; Wagner, T.; Platt, U.; Kaplan, J.O.; Körner, S.; Heimann, M.; Dlugokencky, E.J.; Goede, A.
2007-01-01
We extend the analysis of a global CH4 data set retrieved from SCIAMACHY (Frankenberg et al., 2006) by making a detailed comparison with inverse TM5 model simulations for 2003 that are optimized versus high accuracy CH4 surface measurements from the NOAA ESRL network. The comparison of column
An inverse dynamics model for the analysis, reconstruction and prediction of bipedal walking
Koopman, Hubertus F.J.M.; Grootenboer, H.J.; de Jongh, Henk J.; Huijing, P.A.J.B.M.; de Vries, J.
1995-01-01
Walking is a constrained movement which may best be observed during the double stance phase when both feet contact the floor. When analyzing a measured movement with an inverse dynamics model, a violation of these constrains will always occur due to measuring errors and deviations of the segments
Bayesian Uncertainty Quantification for Subsurface Inversion Using a Multiscale Hierarchical Model
Mondal, Anirban
2014-07-03
We consider a Bayesian approach to nonlinear inverse problems in which the unknown quantity is a random field (spatial or temporal). The Bayesian approach contains a natural mechanism for regularization in the form of prior information, can incorporate information from heterogeneous sources and provide a quantitative assessment of uncertainty in the inverse solution. The Bayesian setting casts the inverse solution as a posterior probability distribution over the model parameters. The Karhunen-Loeve expansion is used for dimension reduction of the random field. Furthermore, we use a hierarchical Bayes model to inject multiscale data in the modeling framework. In this Bayesian framework, we show that this inverse problem is well-posed by proving that the posterior measure is Lipschitz continuous with respect to the data in total variation norm. Computational challenges in this construction arise from the need for repeated evaluations of the forward model (e.g., in the context of MCMC) and are compounded by high dimensionality of the posterior. We develop two-stage reversible jump MCMC that has the ability to screen the bad proposals in the first inexpensive stage. Numerical results are presented by analyzing simulated as well as real data from hydrocarbon reservoir. This article has supplementary material available online. © 2014 American Statistical Association and the American Society for Quality.
Numerical Modeling of Inverse Problems under Uncertainty for Damage Detection in Aircraft Structures
2013-08-01
the nature of the physics involved, from the point of view of the inverse problem, the direct model is only a “black box ” to obtain numerical...Campos, Brasil . As results directly related to this research, several publications and monographs were published and /or are being prepared, as
Aldiss, Don; Haslam, Richard
2013-04-01
In parts of London, faulting introduces lateral heterogeneity to the local ground conditions, especially where construction works intercept the Palaeogene Lambeth Group. This brings difficulties to the compilation of a ground model that is fully consistent with the ground investigation data, and so to the design and construction of engineering works. However, because bedrock in the London area is rather uniform at outcrop, and is widely covered by Quaternary deposits, few faults are shown on the geological maps of the area. This paper discusses a successful resolution of this problem at a site in east central London, where tunnels for a new underground railway station are planned. A 3D geological model was used to provide an understanding of the local geological structure, in faulted Lambeth Group strata, that had not been possible by other commonly-used methods. This model includes seven previously unrecognised faults, with downthrows ranging from about 1 m to about 12 m. The model was constructed in the GSI3D geological modelling software using about 145 borehole records, including many legacy records, in an area of 850 m by 500 m. The basis of a GSI3D 3D geological model is a network of 2D cross-sections drawn by a geologist, generally connecting borehole positions (where the borehole records define the level of the geological units that are present), and outcrop and subcrop lines for those units (where shown by a geological map). When the lines tracing the base of each geological unit within the intersecting cross-sections are complete and mutually consistent, the software is used to generate TIN surfaces between those lines, so creating a 3D geological model. Even where a geological model is constructed as if no faults were present, changes in apparent dip between two data points within a single cross-section can indicate that a fault is present in that segment of the cross-section. If displacements of similar size with the same polarity are found in a series
Ojo, A. O.; Xie, Jun; Olorunfemi, M. O.
2018-01-01
To reduce ambiguity related to nonlinearities in the resistivity model-data relationships, an efficient direct-search scheme employing the Neighbourhood Algorithm (NA) was implemented to solve the 1-D resistivity problem. In addition to finding a range of best-fit models which are more likely to be global minimums, this method investigates the entire multi-dimensional model space and provides additional information about the posterior model covariance matrix, marginal probability density function and an ensemble of acceptable models. This provides new insights into how well the model parameters are constrained and make assessing trade-offs between them possible, thus avoiding some common interpretation pitfalls. The efficacy of the newly developed program is tested by inverting both synthetic (noisy and noise-free) data and field data from other authors employing different inversion methods so as to provide a good base for comparative performance. In all cases, the inverted model parameters were in good agreement with the true and recovered model parameters from other methods and remarkably correlate with the available borehole litho-log and known geology for the field dataset. The NA method has proven to be useful whilst a good starting model is not available and the reduced number of unknowns in the 1-D resistivity inverse problem makes it an attractive alternative to the linearized methods. Hence, it is concluded that the newly developed program offers an excellent complementary tool for the global inversion of the layered resistivity structure.
DEFF Research Database (Denmark)
Herckenrath, Daan; Fiandaca, G.; Auken, Esben
2013-01-01
hydrogeophysical inversion approaches to inform a field-scale groundwater model with time domain electromagnetic (TDEM) and electrical resistivity tomography (ERT) data. In a sequential hydrogeophysical inversion (SHI) a groundwater model is calibrated with geophysical data by coupling groundwater model parameters...
GEMSFITS: Code package for optimization of geochemical model parameters and inverse modeling
International Nuclear Information System (INIS)
Miron, George D.; Kulik, Dmitrii A.; Dmytrieva, Svitlana V.; Wagner, Thomas
2015-01-01
Highlights: • Tool for generating consistent parameters against various types of experiments. • Handles a large number of experimental data and parameters (is parallelized). • Has a graphical interface and can perform statistical analysis on the parameters. • Tested on fitting the standard state Gibbs free energies of aqueous Al species. • Example on fitting interaction parameters of mixing models and thermobarometry. - Abstract: GEMSFITS is a new code package for fitting internally consistent input parameters of GEM (Gibbs Energy Minimization) geochemical–thermodynamic models against various types of experimental or geochemical data, and for performing inverse modeling tasks. It consists of the gemsfit2 (parameter optimizer) and gfshell2 (graphical user interface) programs both accessing a NoSQL database, all developed with flexibility, generality, efficiency, and user friendliness in mind. The parameter optimizer gemsfit2 includes the GEMS3K chemical speciation solver ( (http://gems.web.psi.ch/GEMS3K)), which features a comprehensive suite of non-ideal activity- and equation-of-state models of solution phases (aqueous electrolyte, gas and fluid mixtures, solid solutions, (ad)sorption. The gemsfit2 code uses the robust open-source NLopt library for parameter fitting, which provides a selection between several nonlinear optimization algorithms (global, local, gradient-based), and supports large-scale parallelization. The gemsfit2 code can also perform comprehensive statistical analysis of the fitted parameters (basic statistics, sensitivity, Monte Carlo confidence intervals), thus supporting the user with powerful tools for evaluating the quality of the fits and the physical significance of the model parameters. The gfshell2 code provides menu-driven setup of optimization options (data selection, properties to fit and their constraints, measured properties to compare with computed counterparts, and statistics). The practical utility, efficiency, and
Identification of Constitutive Parameters Using Inverse Strategy Coupled to an ANN Model
International Nuclear Information System (INIS)
Aguir, H.; Chamekh, A.; BelHadjSalah, H.; Hambli, R.
2007-01-01
This paper deals with the identification of material parameters using an inverse strategy. In the classical methods, the inverse technique is generally coupled with a finite element code which leads to a long computing time. In this work an inverse strategy coupled with an ANN procedure is proposed. This method has the advantage of being faster than the classical one. To validate this approach an experimental plane tensile and bulge tests are used in order to identify material behavior. The ANN model is trained from finite element simulations of the two tests. In order to reduce the gap between the experimental responses and the numerical ones, the proposed method is coupled with an optimization procedure to identify material parameters for the AISI304. The identified material parameters are the hardening curve and the anisotropic coefficients
Evaluation of an Inverse Molecular Design Algorithm in a Model Binding Site
Huggins, David J.; Altman, Michael D.; Tidor, Bruce
2008-01-01
Computational molecular design is a useful tool in modern drug discovery. Virtual screening is an approach that docks and then scores individual members of compound libraries. In contrast to this forward approach, inverse approaches construct compounds from fragments, such that the computed affinity, or a combination of relevant properties, is optimized. We have recently developed a new inverse approach to drug design based on the dead-end elimination and A* algorithms employing a physical potential function. This approach has been applied to combinatorially constructed libraries of small-molecule ligands to design high-affinity HIV-1 protease inhibitors [M. D. Altman et al. J. Am. Chem. Soc. 130: 6099–6013, 2008]. Here we have evaluated the new method using the well studied W191G mutant of cytochrome c peroxidase. This mutant possesses a charged binding pocket and has been used to evaluate other design approaches. The results show that overall the new inverse approach does an excellent job of separating binders from non-binders. For a few individual cases, scoring inaccuracies led to false positives. The majority of these involve erroneous solvation energy estimation for charged amines, anilinium ions and phenols, which has been observed previously for a variety of scoring algorithms. Interestingly, although inverse approaches are generally expected to identify some but not all binders in a library, due to limited conformational searching, these results show excellent coverage of the known binders while still showing strong discrimination of the non-binders. PMID:18831031
Effect of recent observations on Asian CO2 flux estimates by transport model inversions
International Nuclear Information System (INIS)
Maksyutov, Shamil; Patra, Prabir K.; Machida, Toshinobu; Mukai, Hitoshi; Nakazawa, Takakiyo; Inoue, Gen
2003-01-01
We use an inverse model to evaluate the effects of the recent CO 2 observations over Asia on estimates of regional CO 2 sources and sinks. Global CO 2 flux distribution is evaluated using several atmospheric transport models, atmospheric CO 2 observations and a 'time-independent' inversion procedure adopted in the basic synthesis inversion by the Transcom-3 inverse model intercomparison project. In our analysis we include airborne and tower observations in Siberia, continuous monitoring and airborne observations over Japan, and airborne monitoring on regular flights on Tokyo-Sydney route. The inclusion of the new data reduces the uncertainty of the estimated regional CO 2 fluxes for Boreal Asia (Siberia), Temperate Asia and South-East Asia. The largest effect is observed for the emission/sink estimate for the Boreal Asia region, where introducing the observations in Siberia reduces the source uncertainty by almost half. It also produces an uncertainty reduction for Boreal North America. Addition of the Siberian airborne observations leads to projecting extra sinks in Boreal Asia of 0.2 Pg C/yr, and a smaller change for Europe. The Tokyo-Sydney observations reduce and constrain the Southeast Asian source
Validation of the Swiss methane emission inventory by atmospheric observations and inverse modelling
Directory of Open Access Journals (Sweden)
S. Henne
2016-03-01
Full Text Available Atmospheric inverse modelling has the potential to provide observation-based estimates of greenhouse gas emissions at the country scale, thereby allowing for an independent validation of national emission inventories. Here, we present a regional-scale inverse modelling study to quantify the emissions of methane (CH4 from Switzerland, making use of the newly established CarboCount-CH measurement network and a high-resolution Lagrangian transport model. In our reference inversion, prior emissions were taken from the "bottom-up" Swiss Greenhouse Gas Inventory (SGHGI as published by the Swiss Federal Office for the Environment in 2014 for the year 2012. Overall we estimate national CH4 emissions to be 196 ± 18 Gg yr−1 for the year 2013 (1σ uncertainty. This result is in close agreement with the recently revised SGHGI estimate of 206 ± 33 Gg yr−1 as reported in 2015 for the year 2012. Results from sensitivity inversions using alternative prior emissions, uncertainty covariance settings, large-scale background mole fractions, two different inverse algorithms (Bayesian and extended Kalman filter, and two different transport models confirm the robustness and independent character of our estimate. According to the latest SGHGI estimate the main CH4 source categories in Switzerland are agriculture (78 %, waste handling (15 % and natural gas distribution and combustion (6 %. The spatial distribution and seasonal variability of our posterior emissions suggest an overestimation of agricultural CH4 emissions by 10 to 20 % in the most recent SGHGI, which is likely due to an overestimation of emissions from manure handling. Urban areas do not appear as emission hotspots in our posterior results, suggesting that leakages from natural gas distribution are only a minor source of CH4 in Switzerland. This is consistent with rather low emissions of 8.4 Gg yr−1 reported by the SGHGI but inconsistent with the much higher value of 32 Gg yr−1 implied by the
Verstraete, Michel M.; Pinty, Bernard; Dickinson, Robert E.
1990-01-01
A new physically based analytical model of the bidirectional reflectance of vegetation canopies is derived. The model expresses the bidirectional reflectance field of a semiinfinite canopy as a combination of functions describing (1) the optical properties of the leaves through their single-scattering albedo and their phase function, (2) the average distribution of leaf orientations, and (3) the architecture of the canopy. The model is validated against laboratory and ground-based measurements in the visible and IR spectral regions, taken over two vegetation covers. The intrinsic optical properties of leaves and the information on the geometrical canopy arrangements in space were obtained using an inversion procedure based on a nonlinear optimization technique. Model predictions of bidirectional reflectances obtained using the inversion procedure compare well with actual observations.
Wada, Daichi; Sugimoto, Yohei
2017-04-01
Aerodynamic loads on aircraft wings are one of the key parameters to be monitored for reliable and effective aircraft operations and management. Flight data of the aerodynamic loads would be used onboard to control the aircraft and accumulated data would be used for the condition-based maintenance and the feedback for the fatigue and critical load modeling. The effective sensing techniques such as fiber optic distributed sensing have been developed and demonstrated promising capability of monitoring structural responses, i.e., strains on the surface of the aircraft wings. By using the developed techniques, load identification methods for structural health monitoring are expected to be established. The typical inverse analysis for load identification using strains calculates the loads in a discrete form of concentrated forces, however, the distributed form of the loads is essential for the accurate and reliable estimation of the critical stress at structural parts. In this study, we demonstrate an inverse analysis to identify the distributed loads from measured strain information. The introduced inverse analysis technique calculates aerodynamic loads not in a discrete but in a distributed manner based on a finite element model. In order to verify the technique through numerical simulations, we apply static aerodynamic loads on a flat panel model, and conduct the inverse identification of the load distributions. We take two approaches to build the inverse system between loads and strains. The first one uses structural models and the second one uses neural networks. We compare the performance of the two approaches, and discuss the effect of the amount of the strain sensing information.
Inversion of the Jacobi-Porstendörfer Room Model for the Radon Progeny
Czech Academy of Sciences Publication Activity Database
Thomas, J.; Jílek, K.; Brabec, Marek
2010-01-01
Roč. 55, č. 4 (2010), s. 433-437 ISSN 0029-5922 Institutional research plan: CEZ:AV0Z10300504 Keywords : Jacobi room model * inversion and invariants of the model * unattached radon daughters * attachment rate * deposition rate Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.321, year: 2010 http://www.nukleonika.pl/www/back/full/vol55_2010/v55n4p433f.pdf
Modelling Inverse Gaussian Data with Censored Response Values: EM versus MCMC
Directory of Open Access Journals (Sweden)
R. S. Sparks
2011-01-01
Full Text Available Low detection limits are common in measure environmental variables. Building models using data containing low or high detection limits without adjusting for the censoring produces biased models. This paper offers approaches to estimate an inverse Gaussian distribution when some of the data used are censored because of low or high detection limits. Adjustments for the censoring can be made if there is between 2% and 20% censoring using either the EM algorithm or MCMC. This paper compares these approaches.
Directory of Open Access Journals (Sweden)
Warsa
2014-07-01
Full Text Available Groundwater is an important economic source of water supply for drinking water and irrigation water for agriculture. Surface nuclear magnetic resonance (SNMR sounding is a relatively new geophysical method that can be used to determine the presence of culturally and economically important substances, such as subsurface water or hydrocarbon distribution. SNMR sounding allows the determination of water content and pore size distribution directly from the surface. The SNMR method is performed by stimulating an alternating current pulse through an antenna at the surface in order to confirm the existence of water in the subsurface. This paper reports the development of a 3-D forward modeling code for SNMR amplitudes and decay times, after which an improved 2-D and 3-D inversion algorithm is investigated, consisting of schemes for regularizing model parameterization. After briefly reviewing inversion schemes generally used in geophysics, the special properties of SNMR or magnetic resonance sounding (MRS inversion are evaluated. We present an extension of MRS to magnetic resonance tomography (MRT, i.e. an extension for 2-D and 3-D investigation, and the appropriate inversions.
Efficient non-negative constrained model-based inversion in optoacoustic tomography
Ding, Lu; Luís Deán-Ben, X.; Lutzweiler, Christian; Razansky, Daniel; Ntziachristos, Vasilis
2015-09-01
The inversion accuracy in optoacoustic tomography depends on a number of parameters, including the number of detectors employed, discrete sampling issues or imperfectness of the forward model. These parameters result in ambiguities on the reconstructed image. A common ambiguity is the appearance of negative values, which have no physical meaning since optical absorption can only be higher or equal than zero. We investigate herein algorithms that impose non-negative constraints in model-based optoacoustic inversion. Several state-of-the-art non-negative constrained algorithms are analyzed. Furthermore, an algorithm based on the conjugate gradient method is introduced in this work. We are particularly interested in investigating whether positive restrictions lead to accurate solutions or drive the appearance of errors and artifacts. It is shown that the computational performance of non-negative constrained inversion is higher for the introduced algorithm than for the other algorithms, while yielding equivalent results. The experimental performance of this inversion procedure is then tested in phantoms and small animals, showing an improvement in image quality and quantitativeness with respect to the unconstrained approach. The study performed validates the use of non-negative constraints for improving image accuracy compared to unconstrained methods, while maintaining computational efficiency.
Energy Technology Data Exchange (ETDEWEB)
Garreta, Vincent; Guiot, Joel; Hely, Christelle [CEREGE, UMR 6635, CNRS, Universite Aix-Marseille, Europole de l' Arbois, Aix-en-Provence (France); Miller, Paul A.; Sykes, Martin T. [Lund University, Department of Physical Geography and Ecosystems Analysis, Geobiosphere Science Centre, Lund (Sweden); Brewer, Simon [Universite de Liege, Institut d' Astrophysique et de Geophysique, Liege (Belgium); Litt, Thomas [University of Bonn, Paleontological Institute, Bonn (Germany)
2010-08-15
Climate reconstructions from data sensitive to past climates provide estimates of what these climates were like. Comparing these reconstructions with simulations from climate models allows to validate the models used for future climate prediction. It has been shown that for fossil pollen data, gaining estimates by inverting a vegetation model allows inclusion of past changes in carbon dioxide values. As a new generation of dynamic vegetation model is available we have developed an inversion method for one model, LPJ-GUESS. When this novel method is used with high-resolution sediment it allows us to bypass the classic assumptions of (1) climate and pollen independence between samples and (2) equilibrium between the vegetation, represented as pollen, and climate. Our dynamic inversion method is based on a statistical model to describe the links among climate, simulated vegetation and pollen samples. The inversion is realised thanks to a particle filter algorithm. We perform a validation on 30 modern European sites and then apply the method to the sediment core of Meerfelder Maar (Germany), which covers the Holocene at a temporal resolution of approximately one sample per 30 years. We demonstrate that reconstructed temperatures are constrained. The reconstructed precipitation is less well constrained, due to the dimension considered (one precipitation by season), and the low sensitivity of LPJ-GUESS to precipitation changes. (orig.)
Full-model wavenumber inversion: An emphasis on the appropriate wavenumber continuation
Alkhalifah, Tariq Ali
2016-04-06
A model of the earth can be described using a Fourier basis represented by its wavenumber content. In full-waveform inversion (FWI), the wavenumber description of the model is natural because our Born-approximation-based velocity updates are made up of wavefields. Our objective in FWI is to access all the model wavenumbers available in our limited aperture and bandwidth recorded data that are not yet accurately present in the initial velocity model. To invert for those model wavenumbers, we need to locate their imprint in the data. Thus, I review the relation between the model wavenumber buildup and the inversion process. Specifically, I emphasize a focus on the model wavenumber components and identified their individual influence on the data. Missing the energy for a single vertical low-model wavenumber from the residual between the true Marmousi model and some initial linearly increasing velocity model produced a worse least-squares fit to the data than the initial model itself, in which all the residual model wavenumbers were missing. This stern realization validated the importance of wavenumber continuation, specifically starting from the low-model wavenumbers, to higher (resolution) wavenumbers, especially those attained in an order dictated by the scattering angle filter. A numerical Marmousi example determined the important role that the scattering angle filter played in managing the wavenumber continuation from low to high. An application on the SEG2014 blind test data set with frequencies lower than 7 Hz muted out further validated the versatility of the scattering angle filtering.
Inverse modelling in estimating soil hydraulic functions: a Genetic Algorithm approach
Ines, A. V. M.; Droogers, P.
The practical application of simulation models in the field is sometimes hindered by the difficulty of deriving the soil hydraulic properties of the study area. The procedure so-called inverse modelling has been investigated in many studies to address the problem where most of the studies were limited to hypothetical soil profile and soil core samples in the laboratory. Often, the numerical approach called forward-backward simulation is employed to generate synthetic data then added with random errors to mimic the real-world condition. Inverse modelling is used to backtrack the expected values of the parameters. This study explored the potential of a Genetic Algorithm (GA) to estimate inversely the soil hydraulic functions in the unsaturated zone. Lysimeter data from a wheat experiment in India were used in the analysis. Two cases were considered: (1) a numerical case where the forward-backward approach was employed and (2) the experimental case where the real data from the lysimeter experiment were used. Concurrently, the use of soil water, evapotranspiration (ET) and the combination of both were investigated as criteria in the inverse modelling. Results showed that using soil water as a criterion provides more accurate parameter estimates than using ET. However, from a practical point of view, ET is more attractive as it can be obtained with reasonable accuracy on a regional scale from remote sensing observations. The experimental study proved that the forward-backward approach does not take into account the effects of model errors. The formulation of the problem is found to be critical for a successful parameter estimation. The sensitivity of parameters to the objective function and their zone of influence in the soil column are major determinants in the solution. Generally, their effects sometimes lead to non-uniqueness in the solution but to some extent are partly handled by GA. Overall, it was concluded that the GA approach is promising to the inverse problem
Inverse modelling in estimating soil hydraulic functions: a Genetic Algorithm approach
Directory of Open Access Journals (Sweden)
A. V. M. Ines
2002-01-01
Full Text Available The practical application of simulation models in the field is sometimes hindered by the difficulty of deriving the soil hydraulic properties of the study area. The procedure so-called inverse modelling has been investigated in many studies to address the problem where most of the studies were limited to hypothetical soil profile and soil core samples in the laboratory. Often, the numerical approach called forward-backward simulation is employed to generate synthetic data then added with random errors to mimic the real-world condition. Inverse modelling is used to backtrack the expected values of the parameters. This study explored the potential of a Genetic Algorithm (GA to estimate inversely the soil hydraulic functions in the unsaturated zone. Lysimeter data from a wheat experiment in India were used in the analysis. Two cases were considered: (1 a numerical case where the forward-backward approach was employed and (2 the experimental case where the real data from the lysimeter experiment were used. Concurrently, the use of soil water, evapotranspiration (ET and the combination of both were investigated as criteria in the inverse modelling. Results showed that using soil water as a criterion provides more accurate parameter estimates than using ET. However, from a practical point of view, ET is more attractive as it can be obtained with reasonable accuracy on a regional scale from remote sensing observations. The experimental study proved that the forward-backward approach does not take into account the effects of model errors. The formulation of the problem is found to be critical for a successful parameter estimation. The sensitivity of parameters to the objective function and their zone of influence in the soil column are major determinants in the solution. Generally, their effects sometimes lead to non-uniqueness in the solution but to some extent are partly handled by GA. Overall, it was concluded that the GA approach is promising to the
Inverse Optimization: A New Perspective on the Black-Litterman Model
Bertsimas, Dimitris; Gupta, Vishal; Paschalidis, Ioannis Ch.
2014-01-01
The Black-Litterman (BL) model is a widely used asset allocation model in the financial industry. In this paper, we provide a new perspective. The key insight is to replace the statistical framework in the original approach with ideas from inverse optimization. This insight allows us to significantly expand the scope and applicability of the BL model. We provide a richer formulation that, unlike the original model, is flexible enough to incorporate investor information on volatility and market dynamics. Equally importantly, our approach allows us to move beyond the traditional mean-variance paradigm of the original model and construct “BL”-type estimators for more general notions of risk such as coherent risk measures. Computationally, we introduce and study two new “BL”-type estimators and their corresponding portfolios: a Mean Variance Inverse Optimization (MV-IO) portfolio and a Robust Mean Variance Inverse Optimization (RMV-IO) portfolio. These two approaches are motivated by ideas from arbitrage pricing theory and volatility uncertainty. Using numerical simulation and historical backtesting, we show that both methods often demonstrate a better risk-reward tradeoff than their BL counterparts and are more robust to incorrect investor views. PMID:25382873
Inverse Optimization: A New Perspective on the Black-Litterman Model.
Bertsimas, Dimitris; Gupta, Vishal; Paschalidis, Ioannis Ch
2012-12-11
The Black-Litterman (BL) model is a widely used asset allocation model in the financial industry. In this paper, we provide a new perspective. The key insight is to replace the statistical framework in the original approach with ideas from inverse optimization. This insight allows us to significantly expand the scope and applicability of the BL model. We provide a richer formulation that, unlike the original model, is flexible enough to incorporate investor information on volatility and market dynamics. Equally importantly, our approach allows us to move beyond the traditional mean-variance paradigm of the original model and construct "BL"-type estimators for more general notions of risk such as coherent risk measures. Computationally, we introduce and study two new "BL"-type estimators and their corresponding portfolios: a Mean Variance Inverse Optimization (MV-IO) portfolio and a Robust Mean Variance Inverse Optimization (RMV-IO) portfolio. These two approaches are motivated by ideas from arbitrage pricing theory and volatility uncertainty. Using numerical simulation and historical backtesting, we show that both methods often demonstrate a better risk-reward tradeoff than their BL counterparts and are more robust to incorrect investor views.
Chen, Mingjie; Izady, Azizallah; Abdalla, Osman A.; Amerjeed, Mansoor
2018-02-01
Bayesian inference using Markov Chain Monte Carlo (MCMC) provides an explicit framework for stochastic calibration of hydrogeologic models accounting for uncertainties; however, the MCMC sampling entails a large number of model calls, and could easily become computationally unwieldy if the high-fidelity hydrogeologic model simulation is time consuming. This study proposes a surrogate-based Bayesian framework to address this notorious issue, and illustrates the methodology by inverse modeling a regional MODFLOW model. The high-fidelity groundwater model is approximated by a fast statistical model using Bagging Multivariate Adaptive Regression Spline (BMARS) algorithm, and hence the MCMC sampling can be efficiently performed. In this study, the MODFLOW model is developed to simulate the groundwater flow in an arid region of Oman consisting of mountain-coast aquifers, and used to run representative simulations to generate training dataset for BMARS model construction. A BMARS-based Sobol' method is also employed to efficiently calculate input parameter sensitivities, which are used to evaluate and rank their importance for the groundwater flow model system. According to sensitivity analysis, insensitive parameters are screened out of Bayesian inversion of the MODFLOW model, further saving computing efforts. The posterior probability distribution of input parameters is efficiently inferred from the prescribed prior distribution using observed head data, demonstrating that the presented BMARS-based Bayesian framework is an efficient tool to reduce parameter uncertainties of a groundwater system.
Research Note: Full-waveform inversion of the unwrapped phase of a model
Alkhalifah, Tariq Ali
2013-12-06
Reflections in seismic data induce serious non-linearity in the objective function of full- waveform inversion. Thus, without a good initial velocity model that can produce reflections within a half cycle of the frequency used in the inversion, convergence to a solution becomes difficult. As a result, we tend to invert for refracted events and damp reflections in data. Reflection induced non-linearity stems from cycle skipping between the imprint of the true model in observed data and the predicted model in synthesized data. Inverting for the phase of the model allows us to address this problem by avoiding the source of non-linearity, the phase wrapping phenomena. Most of the information related to the location (or depths) of interfaces is embedded in the phase component of a model, mainly influenced by the background model, while the velocity-contrast information (responsible for the reflection energy) is mainly embedded in the amplitude component. In combination with unwrapping the phase of data, which mitigates the non-linearity introduced by the source function, I develop a framework to invert for the unwrapped phase of a model, represented by the instantaneous depth, using the unwrapped phase of the data. The resulting gradient function provides a mechanism to non-linearly update the velocity model by applying mainly phase shifts to the model. In using the instantaneous depth as a model parameter, we keep track of the model properties unfazed by the wrapping phenomena. © 2013 European Association of Geoscientists & Engineers.
Energy Technology Data Exchange (ETDEWEB)
Chen, J.; Hoversten, G.M.
2011-09-15
Joint inversion of seismic AVA and CSEM data requires rock-physics relationships to link seismic attributes to electrical properties. Ideally, we can connect them through reservoir parameters (e.g., porosity and water saturation) by developing physical-based models, such as Gassmann’s equations and Archie’s law, using nearby borehole logs. This could be difficult in the exploration stage because information available is typically insufficient for choosing suitable rock-physics models and for subsequently obtaining reliable estimates of the associated parameters. The use of improper rock-physics models and the inaccuracy of the estimates of model parameters may cause misleading inversion results. Conversely, it is easy to derive statistical relationships among seismic and electrical attributes and reservoir parameters from distant borehole logs. In this study, we develop a Bayesian model to jointly invert seismic AVA and CSEM data for reservoir parameter estimation using statistical rock-physics models; the spatial dependence of geophysical and reservoir parameters are carried out by lithotypes through Markov random fields. We apply the developed model to a synthetic case, which simulates a CO{sub 2} monitoring application. We derive statistical rock-physics relations from borehole logs at one location and estimate seismic P- and S-wave velocity ratio, acoustic impedance, density, electrical resistivity, lithotypes, porosity, and water saturation at three different locations by conditioning to seismic AVA and CSEM data. Comparison of the inversion results with their corresponding true values shows that the correlation-based statistical rock-physics models provide significant information for improving the joint inversion results.
Directory of Open Access Journals (Sweden)
Leszek Majkut
Full Text Available In the work, in order to solve the inverse problem, i.e. the problem of finding values of the additional quantities (mass, elasticity, the beam inverse model was proposed. Analysis of this model allows finding such a value of additional mass (elasticity as a function of its localization so that the free vibration frequency changes to desirable value. The criteria for choice of the “proper” pair (mass - its position, including the criterion allowing changing the position of the vibration node of the second mode of the free vibrations, were given. Analysis of the influence of uncertainties in the determination of the additional quantity value and its position on the desired free vibration frequency was carried out, too. The proposed beam inverse model can be employing to identification of the beam cracks. In such a case, the input quantity is free vibration frequency measured on the damaged object. Each determined free-vibration frequency allows determining the flexibility curve for the spring modeling crack as a function of its position. The searched parameters of the crack (its depth and position are indicated by the common point of two arbitrary curves. Accuracy of crack parameters determination depends on accuracy (uncertainty of frequency measurement. Only some regions containing the searched crack parameters can be obtained in such a situation.
Energy Technology Data Exchange (ETDEWEB)
Watanabe, T.; Sassa, K. [Kyoto University, Kyoto (Japan); Uesaka, S. [Kyoto University, Kyoto (Japan). Faculty of Engineering
1996-10-01
The effect of initial models on full-wave inversion (FWI) analysis based on acoustic wave-equation was studied for elastic wave tomography of underground structures. At present, travel time inversion using initial motion travel time is generally used, and inverse analysis is conducted using the concept `ray,` assuming very high wave frequency. Although this method can derive stable solutions relatively unaffected by initial model, it uses only the data of initial motion travel time. FWI calculates theoretical waveform at each receiver using all of observed waveforms as data by wave equation modeling where 2-D underground structure is calculated by difference calculus under the assumption that wave propagation is described by wave equation of P wave. Although it is a weak point that FWI is easily affected by noises in an initial model and data, it is featured by high resolution of solutions. This method offers very excellent convergence as a proper initial model is used, resulting in sufficient performance, however, it is strongly affected by initial model. 2 refs., 7 figs., 1 tab.
An Inverse Modeling Approach to Investigate Past Lead Atmospheric Deposition in Southern Greenland
Massa, C.; Monna, F.; Bichet, V.; Gauthier, E.; Richard, H.
2013-12-01
The aim of this study is to model atmospheric pollution lead fluxes using two different paleoenvironmental records, covering the last 2000 years, located in southern Greenland. Fifty five sediment samples from the Lake Igaliku sequence (61°00.403'N, 45°26.494'W) were analyzed for their Pb and Al contents, and for lead isotopic compositions. The second archive consists in a previously published dataset (Shotyk et al., 2003), including Zr and Pb concentrations, and lead isotopic compositions, obtained from a minerogenic peat deposit located 16 km northwest of Lake Igaliku (61°08.314'N, 45°33.703'W). As natural background concentrations are high and obliterate most of the airborne anthropogenic lead, it is not possible to isolate this anthropogenic contribution through time with classical methods (i.e. Pb is normalized to a lithogenic and conservative element). Moreover, the background 206Pb/207Pb ratio is rather noisy because of the wide geological heterogeneity of sediment sources, which further complicated unambiguous detection of the lead pollution. To overcome these difficulties, an inverse modeling approach based on assumptions about past lead inputs was applied. This method consists of simulating a range of anthropogenic fluxes to determine the best match between measured and simulated data, both for Pb concentrations and isotopic compositions. The model is validated by the coherence of the results obtained from the two independent datasets that must reflect a similar pollution history. Although notable 206Pb/207Pb ratio shifts suggest that the first signs of anthropogenic inputs may have occurred in the 15th century, the signal-to-noise ratio was too low to significantly influence the sediment composition. Nevertheless we were able to estimate that anthropogenic lead fluxes did not exceed 2700 μg m-2 yr-1, a maximum value recorded during the 1960s. The comparison with other records from the North Atlantic Islands reveals a spatial gradient most likely due
DEFF Research Database (Denmark)
Sonnenborg, Torben Obel; Engesgaard, Peter Knudegaard; Rosbjerg, Dan
1996-01-01
An application of an inverse flow and transport model to a contaminated aquifer is presented. The objective of the study is to identify physical and nonreactive flow and transport parameters through an optimization approach. The approach can be classified as a statistical procedure, where a flow ...... is the first in a two-paper series describing contaminant transport at a waste residue site. III the second paper, reactive transport at the site is investigated.......An application of an inverse flow and transport model to a contaminated aquifer is presented. The objective of the study is to identify physical and nonreactive flow and transport parameters through an optimization approach. The approach can be classified as a statistical procedure, where a flow...
Spin model for nontrivial types of magnetic order in inverse-perovskite antiferromagnets
Mochizuki, Masahito; Kobayashi, Masaya; Okabe, Reoya; Yamamoto, Daisuke
2018-02-01
Nontrivial magnetic orders in the inverse-perovskite manganese nitrides are theoretically studied by constructing a classical spin model describing the magnetic anisotropy and frustrated exchange interactions inherent in specific crystal and electronic structures of these materials. With a replica-exchange Monte Carlo technique, a theoretical analysis of this model reproduces the experimentally observed triangular Γ5 g and Γ4 g spin-ordered patterns and the systematic evolution of magnetic orders. Our Rapid Communication solves a 40-year-old problem of nontrivial magnetism for the inverse-perovskite manganese nitrides and provides a firm basis for clarifying the magnetism-driven negative thermal expansion phenomenon discovered in this class of materials.
Liu, Long; Liu, Wei
2018-04-01
A forward modeling and inversion algorithm is adopted in order to determine the water injection plan in the oilfield water injection network. The main idea of the algorithm is shown as follows: firstly, the oilfield water injection network is inversely calculated. The pumping station demand flow is calculated. Then, forward modeling calculation is carried out for judging whether all water injection wells meet the requirements of injection allocation or not. If all water injection wells meet the requirements of injection allocation, calculation is stopped, otherwise the demand injection allocation flow rate of certain step size is reduced aiming at water injection wells which do not meet requirements, and next iterative operation is started. It is not necessary to list the algorithm into water injection network system algorithm, which can be realized easily. Iterative method is used, which is suitable for computer programming. Experimental result shows that the algorithm is fast and accurate.
Comparison of inverse dynamics calculated by two- and three-dimensional models during walking
DEFF Research Database (Denmark)
Alkjaer, T; Simonsen, E B; Dyhre-Poulsen, P
2001-01-01
recorded the subjects as they walked across two force plates. The subjects were invited to approach a walking speed of 4.5 km/h. The ankle, knee and hip joint moments in the sagittal plane were calculated by 2D and 3D inverse dynamics analysis and compared. Despite the uniform walking speed (4.53 km....../h) and similar footwear, relatively large inter-individual variations were found in the joint moment patterns during the stance phase. The differences between individuals were present in both the 2D and 3D analysis. For the entire sample of subjects the overall time course pattern of the ankle, knee and hip...... the magnitude of the joint moments calculated by 2D and 3D inverse dynamics but the inter-individual variation was not affected by the different models. The simpler 2D model seems therefore appropriate for human gait analysis. However, comparisons of gait data from different studies are problematic...
Inverse magnetic catalysis in Nambu–Jona-Lasinio model beyond mean field
International Nuclear Information System (INIS)
Mao, Shijun
2016-01-01
We study inverse magnetic catalysis in the Nambu–Jona-Lasinio model beyond mean field approximation. The feed-down from mesons to quarks is embedded in an effective coupling constant at finite temperature and magnetic field. While the magnetic catalysis is still the dominant effect at low temperature, the meson dressed quark mass drops down with increasing magnetic field at high temperature due to the dimension reduction of the Goldstone mode in the Pauli–Villars regularization scheme.
An Inverse Neural Controller Based on the Applicability Domain of RBF Network Models
Directory of Open Access Journals (Sweden)
Alex Alexandridis
2018-01-01
Full Text Available This paper presents a novel methodology of generic nature for controlling nonlinear systems, using inverse radial basis function neural network models, which may combine diverse data originating from various sources. The algorithm starts by applying the particle swarm optimization-based non-symmetric variant of the fuzzy means (PSO-NSFM algorithm so that an approximation of the inverse system dynamics is obtained. PSO-NSFM offers models of high accuracy combined with small network structures. Next, the applicability domain concept is suitably tailored and embedded into the proposed control structure in order to ensure that extrapolation is avoided in the controller predictions. Finally, an error correction term, estimating the error produced by the unmodeled dynamics and/or unmeasured external disturbances, is included to the control scheme to increase robustness. The resulting controller guarantees bounded input-bounded state (BIBS stability for the closed loop system when the open loop system is BIBS stable. The proposed methodology is evaluated on two different control problems, namely, the control of an experimental armature-controlled direct current (DC motor and the stabilization of a highly nonlinear simulated inverted pendulum. For each one of these problems, appropriate case studies are tested, in which a conventional neural controller employing inverse models and a PID controller are also applied. The results reveal the ability of the proposed control scheme to handle and manipulate diverse data through a data fusion approach and illustrate the superiority of the method in terms of faster and less oscillatory responses.
An Inverse Neural Controller Based on the Applicability Domain of RBF Network Models.
Alexandridis, Alex; Stogiannos, Marios; Papaioannou, Nikolaos; Zois, Elias; Sarimveis, Haralambos
2018-01-22
This paper presents a novel methodology of generic nature for controlling nonlinear systems, using inverse radial basis function neural network models, which may combine diverse data originating from various sources. The algorithm starts by applying the particle swarm optimization-based non-symmetric variant of the fuzzy means (PSO-NSFM) algorithm so that an approximation of the inverse system dynamics is obtained. PSO-NSFM offers models of high accuracy combined with small network structures. Next, the applicability domain concept is suitably tailored and embedded into the proposed control structure in order to ensure that extrapolation is avoided in the controller predictions. Finally, an error correction term, estimating the error produced by the unmodeled dynamics and/or unmeasured external disturbances, is included to the control scheme to increase robustness. The resulting controller guarantees bounded input-bounded state (BIBS) stability for the closed loop system when the open loop system is BIBS stable. The proposed methodology is evaluated on two different control problems, namely, the control of an experimental armature-controlled direct current (DC) motor and the stabilization of a highly nonlinear simulated inverted pendulum. For each one of these problems, appropriate case studies are tested, in which a conventional neural controller employing inverse models and a PID controller are also applied. The results reveal the ability of the proposed control scheme to handle and manipulate diverse data through a data fusion approach and illustrate the superiority of the method in terms of faster and less oscillatory responses.
A wrench and inversion model for structures in the Timor Sea region, northwest Austrialia
Energy Technology Data Exchange (ETDEWEB)
Nelson, A.W.
1996-01-01
A structural model is developed for part of the Timor Sea region, northwest Australia, involving multiple strike-slip episodes, and significant changes in the regional or local stress regimes. It is interpreted that both normal and reverse faults have existed since at least the Permian, and have changed their sense of movement in response to changing stress fields, with latest changes occurring as a result of Tertiary collision of the Australian and Eurasian Plates. Both 2-D and 3-D seismic data sets are used to demonstrate development of conventional simple strike-slip models into complex multi-episode models incorporating through-going and abandoned faults. After only three episodes of fault movement, one of which involves inversion, the fault linkages and structural history can become very difficult to unravel. The Jabiru Oil Field is shown to have developed at the intersection of orthogonal faults, with resultant restraining fault bend geometry. Failure to identify a large part of the field for several years after discovery may be attributed to misinterpretation of reversal of fault throw with depth (resulting from inversion), and to lack of appreciation of the significance of abandoned faults. Factors including fault abandonment, fault dips steepening with depth (including sideways concave faults), fault inversion, and trap seal development on inverted and abandoned faults may have a significant influence on the way petroleum traps are mapped and the trapping capacity of seals.
A wrench and inversion model for structures in the Timor Sea region, northwest Austrialia
Energy Technology Data Exchange (ETDEWEB)
Nelson, A.W.
1996-12-31
A structural model is developed for part of the Timor Sea region, northwest Australia, involving multiple strike-slip episodes, and significant changes in the regional or local stress regimes. It is interpreted that both normal and reverse faults have existed since at least the Permian, and have changed their sense of movement in response to changing stress fields, with latest changes occurring as a result of Tertiary collision of the Australian and Eurasian Plates. Both 2-D and 3-D seismic data sets are used to demonstrate development of conventional simple strike-slip models into complex multi-episode models incorporating through-going and abandoned faults. After only three episodes of fault movement, one of which involves inversion, the fault linkages and structural history can become very difficult to unravel. The Jabiru Oil Field is shown to have developed at the intersection of orthogonal faults, with resultant restraining fault bend geometry. Failure to identify a large part of the field for several years after discovery may be attributed to misinterpretation of reversal of fault throw with depth (resulting from inversion), and to lack of appreciation of the significance of abandoned faults. Factors including fault abandonment, fault dips steepening with depth (including sideways concave faults), fault inversion, and trap seal development on inverted and abandoned faults may have a significant influence on the way petroleum traps are mapped and the trapping capacity of seals.
Modeling and forecasting foreign exchange daily closing prices with normal inverse Gaussian
Teneng, Dean
2013-09-01
We fit the normal inverse Gaussian(NIG) distribution to foreign exchange closing prices using the open software package R and select best models by Käärik and Umbleja (2011) proposed strategy. We observe that daily closing prices (12/04/2008 - 07/08/2012) of CHF/JPY, AUD/JPY, GBP/JPY, NZD/USD, QAR/CHF, QAR/EUR, SAR/CHF, SAR/EUR, TND/CHF and TND/EUR are excellent fits while EGP/EUR and EUR/GBP are good fits with a Kolmogorov-Smirnov test p-value of 0.062 and 0.08 respectively. It was impossible to estimate normal inverse Gaussian parameters (by maximum likelihood; computational problem) for JPY/CHF but CHF/JPY was an excellent fit. Thus, while the stochastic properties of an exchange rate can be completely modeled with a probability distribution in one direction, it may be impossible the other way around. We also demonstrate that foreign exchange closing prices can be forecasted with the normal inverse Gaussian (NIG) Lévy process, both in cases where the daily closing prices can and cannot be modeled by NIG distribution.
Fitz, Hartmut; Chang, Franklin
2017-09-01
Nativist theories have argued that language involves syntactic principles which are unlearnable from the input children receive. A paradigm case of these innate principles is the structure dependence of auxiliary inversion in complex polar questions (Chomsky, 1968, 1975, 1980). Computational approaches have focused on the properties of the input in explaining how children acquire these questions. In contrast, we argue that messages are structured in a way that supports structure dependence in syntax. We demonstrate this approach within a connectionist model of sentence production (Chang, 2009) which learned to generate a range of complex polar questions from a structured message without positive exemplars in the input. The model also generated different types of error in development that were similar in magnitude to those in children (e.g., auxiliary doubling, Ambridge, Rowland, & Pine, 2008; Crain & Nakayama, 1987). Through model comparisons we trace how meaning constraints and linguistic experience interact during the acquisition of auxiliary inversion. Our results suggest that auxiliary inversion rules in English can be acquired without innate syntactic principles, as long as it is assumed that speakers who ask complex questions express messages that are structured into multiple propositions. Copyright © 2017 Elsevier B.V. All rights reserved.
Flatness-based model inverse for feed-forward braking control
de Vries, Edwin; Fehn, Achim; Rixen, Daniel
2010-12-01
For modern cars an increasing number of driver assistance systems have been developed. Some of these systems interfere/assist with the braking of a car. Here, a brake actuation algorithm for each individual wheel that can respond to both driver inputs and artificial vehicle deceleration set points is developed. The algorithm consists of a feed-forward control that ensures, within the modelled system plant, the optimal behaviour of the vehicle. For the quarter-car model with LuGre-tyre behavioural model, an inverse model can be derived using v x as the 'flat output', that is, the input for the inverse model. A number of time derivatives of the flat output are required to calculate the model input, brake torque. Polynomial trajectory planning provides the needed time derivatives of the deceleration request. The transition time of the planning can be adjusted to meet actuator constraints. It is shown that the output of the trajectory planning would ripple and introduce a time delay when a gradual continuous increase of deceleration is requested by the driver. Derivative filters are then considered: the Bessel filter provides the best symmetry in its step response. A filter of same order and with negative real-poles is also used, exhibiting no overshoot nor ringing. For these reasons, the 'real-poles' filter would be preferred over the Bessel filter. The half-car model can be used to predict the change in normal load on the front and rear axle due to the pitching of the vehicle. The anticipated dynamic variation of the wheel load can be included in the inverse model, even though it is based on a quarter-car. Brake force distribution proportional to normal load is established. It provides more natural and simpler equations than a fixed force ratio strategy.
Directory of Open Access Journals (Sweden)
Matthias Locherer
2015-08-01
Full Text Available The upcoming satellite mission EnMAP offers the opportunity to retrieve information on the seasonal development of vegetation parameters on a regional scale based on hyperspectral data. This study aims to investigate whether an analysis method for the retrieval of leaf area index (LAI, developed and validated on the 4 m resolution scale of six airborne datasets covering the 2012 growing period, is transferable to the spaceborne 30 m resolution scale of the future EnMAP mission. The widely used PROSAIL model is applied to generate look-up-table (LUT libraries, by which the model is inverted to derive LAI information. With the goal of defining the impact of different selection criteria in the inversion process, different techniques for the LUT based inversion are tested, such as several cost functions, type and amount of artificial noise, number of considered solutions and type of averaging method. The optimal inversion procedure (Laplace, median, 4% inverse multiplicative noise, 350 out of 100,000 averages is identified by validating the results against corresponding in-situ measurements (n = 330 of LAI. Finally, the best performing LUT inversion (R2 = 0.65, RMSE = 0.64 is adapted to simulated EnMAP data, generated from the airborne acquisitions. The comparison of the retrieval results to upscaled maps of LAI, previously validated on the 4 m scale, shows that the optimized retrieval method can successfully be transferred to spaceborne EnMAP data.
Modeling the density at Merapi volcano area, Indonesia, via the inverse gravimetric problem
Tiede, C.; Camacho, A. G.; Gerstenecker, C.; FernáNdez, J.; Suyanto, I.
2005-09-01
Merapi is a high-risk andesitic volcano in Central Java, Indonesia. Very little information is known about the detailed regional density structure around Merapi and its neighbor volcano Merbabu. We compute a subsurface three-dimensional (3-D) model of anomalous density for the volcanoes Merapi and Merbabu in Central Java, Indonesia, by inversion of the gravity field. As input for the inversion methodology, gravity observations from 443 points, whose 3-D coordinates are determined by GPS, are used. The inversion algorithm is based on an explorative approach to fit a least squares condition, including a balancing factor between the minimization of the residuals and the anomalous mass. A mean density about 2242 kg/m3 for the region of Merapi and Merbabu has been computed by least squares adjustment. Results of the inversion show major low-density contrasts up to -242 kg/m3 and positive structures about +264 kg/m3, referred to the determined mean density. A density anomaly (relative high) with densities up to +264 kg/m3 is connecting the volcanoes in a 152° course from NW to SE and might be built of older basaltic lava. Low-density contrasts about -242 kg/m could be found in agreement with magnetotelluric and electromagnetic results. Generally, the identified high- and low-density bodies are in agreement with the results of other geophysical methods such as electromagnetic and magnetotelluric prospecting or geological formations and structures. A porosity about 21% is derived for the largest negative density bodies about -242 kg/m3. Furthermore, the density model gives some new information about the controversial origin of a hill formation near Merapi and is also used to discuss the possible existence of a shallow magma chamber, which is also a controversial subject. Generally, the density model serves as basic information for the interpretation of geodetic and geophysical observations and confirms existing results from magnetotellurics, electromagnetics, and seismic
TransCom N2O model inter-comparison - Part 2 : Atmospheric inversion estimates of N2O emissions
Thompson, R. L.; Ishijima, K.; Saikawa, E.; Corazza, M.; Karstens, U.; Patra, P. K.; Bergamaschi, P.; Chevallier, F.; Dlugokencky, E.; Prinn, R. G.; Weiss, R. F.; O'Doherty, S.; Fraser, P. J.; Steele, L. P.; Krummel, P. B.; Vermeulen, A.; Tohjima, Y.; Jordan, A.; Haszpra, L.; Steinbacher, M.; Van Der Laan, S.; Aalto, T.; Meinhardt, F.; Popa, Maria Elena|info:eu-repo/dai/nl/375806407; Moncrieff, J.; Bousquet, P.
2014-01-01
This study examines N2O emission estimates from five different atmospheric inversion frameworks based on chemistry transport models (CTMs). The five frameworks differ in the choice of CTM, meteorological data, prior uncertainties and inversion method but use the same prior emissions and observation
TransCom N2O model inter-comparison, Part II : Atmospheric inversion estimates of N2O emissions
Thompson, R. L.; Ishijima, K.; Saikawa, E.; Corazza, M.; Karstens, U.; Patra, P. K.; Bergamaschi, P.; Chevallier, F.; Dlugokencky, E.; Prinn, R. G.; Weiss, R. F.; O'Doherty, S.; Fraser, P. J.; Steele, L. P.; Krummel, P. B.; Vermeulen, A.; Tohjima, Y.; Jordan, A.; Haszpra, L.; Steinbacher, M.; Van Der Laan, S.; Aalto, T.; Meinhardt, F.; Popa, Maria Elena; Moncrieff, J.; Bousquet, P.
2014-01-01
This study examines N2O emission estimates from 5 different atmospheric inversion frameworks. The 5 frameworks differ in the choice of atmospheric transport model, meteorological data, prior uncertainties and inversion method but use the same prior emissions and observation dataset. The mean
TH-A-9A-06: Inverse Planning of Gamma Knife Radiosurgery Using Natural Physical Models
International Nuclear Information System (INIS)
Purpose: Treatment-planning systems rely on computer intensive optimization algorithms in order to provide radiation dose localization. We are investigating a new optimization paradigm based on natural physical modeling and simulations, which tend to evolve in time and find the minimum energy state. In our research, we aim to match physical models with radiation therapy inverse planning problems, where the minimum energy state coincides with the optimal solution. As a prototype study, we have modeled the inverse planning of Gamma Knife radiosurgery using the dynamic interactions between charged particles and demonstrate the potential of the paradigm. Methods: For inverse planning of Gamma Knife radiosurgery: (1) positive charges are uniformly placed on the surface of tumors and critical structures. (2) The Gamma Knife dose kernels of 4mm, 8mm and 16mm radii are modeled as geometric objects with variable charges. (3) The number of shots per each kernel radii is obtained by solving a constrained integer-linear problem. (4) The shots are placed into the tumor volume and move under electrostatic forces. The simulation is performed until internal forces are zero or maximum iterations are reached. (5) Finally, non-negative least squares (NNLS) is used to calculate the beam-on times for each shot. Results: A 3D C-shaped tumor surrounding a spherical critical structure was used for testing the new optimization paradigm. These tests showed that charges spread out evenly covering the tumor while keeping distance from the critical structure, resulting in a high quality plan. Conclusion: We have developed a new paradigm for dose optimization based on the simulation of physical models. As prototype studies, we applied electrostatic models to Gamma Knife radiosurgery and demonstrated the potential of the new paradigm. Further research and fine-tuning of the model are underway. NSF CBET-0853157
Improving Atomic Force Microscopy Imaging by a Direct Inverse Asymmetric PI Hysteresis Model
Directory of Open Access Journals (Sweden)
Dong Wang
2015-02-01
Full Text Available A modified Prandtl–Ishlinskii (PI model, referred to as a direct inverse asymmetric PI (DIAPI model in this paper, was implemented to reduce the displacement error between a predicted model and the actual trajectory of a piezoelectric actuator which is commonly found in AFM systems. Due to the nonlinearity of the piezoelectric actuator, the standard symmetric PI model cannot precisely describe the asymmetric motion of the actuator. In order to improve the accuracy of AFM scans, two series of slope parameters were introduced in the PI model to describe both the voltage-increase-loop (trace and voltage-decrease-loop (retrace. A feedforward controller based on the DIAPI model was implemented to compensate hysteresis. Performance of the DIAPI model and the feedforward controller were validated by scanning micro-lenses and standard silicon grating using a custom-built AFM.
Improving atomic force microscopy imaging by a direct inverse asymmetric PI hysteresis model.
Wang, Dong; Yu, Peng; Wang, Feifei; Chan, Ho-Yin; Zhou, Lei; Dong, Zaili; Liu, Lianqing; Li, Wen Jung
2015-02-03
A modified Prandtl-Ishlinskii (PI) model, referred to as a direct inverse asymmetric PI (DIAPI) model in this paper, was implemented to reduce the displacement error between a predicted model and the actual trajectory of a piezoelectric actuator which is commonly found in AFM systems. Due to the nonlinearity of the piezoelectric actuator, the standard symmetric PI model cannot precisely describe the asymmetric motion of the actuator. In order to improve the accuracy of AFM scans, two series of slope parameters were introduced in the PI model to describe both the voltage-increase-loop (trace) and voltage-decrease-loop (retrace). A feedforward controller based on the DIAPI model was implemented to compensate hysteresis. Performance of the DIAPI model and the feedforward controller were validated by scanning micro-lenses and standard silicon grating using a custom-built AFM.
Modelling of MOSFET inversion layer conductivity using the resistor network method
International Nuclear Information System (INIS)
Kingdon, R.D.
1988-10-01
The subthreshold conductance of a MOSFET is smaller than predicted by elementary theory because of inhomogeneity in the inversion layer channel. Prediction of the true conductance thus requires specification of the degree and type of inhomogeneity plus a knowledge of how this suppresses the conductance. To investigate the latter effect the quasi two dimensional MOSFET channel has been simulated by a two dimensional resistor network. This method is computationally efficient and very versatile regarding the choice of inhomogeneity. The starting point for the research is that radiation directly affects the homogeneity of the MOSFET inversion layer. Therefore, a study of radiation damage requires an understanding of the effects of inhomogeneities and an ability to model them. The effects can be used to measure the radiation dose as in radiation dosemeters, or they may need to be suppressed for devices used in space. (author)
The effect of error models in the multiscale inversion of binary permeability fields
Ray, J.; Bloemenwaanders, B. V.; McKenna, S. A.; Marzouk, Y. M.
2010-12-01
We present results from a recently developed multiscale inversion technique for binary media, with emphasis on the effect of subgrid model errors on the inversion. Binary media are a useful fine-scale representation of heterogeneous porous media. Averaged properties of the binary field representations can be used to characterize flow through the porous medium at the macroscale. Both direct measurements of the averaged properties and upscaling are complicated and may not provide accurate results. However, it may be possible to infer upscaled properties of the binary medium from indirect measurements at the coarse scale. Multiscale inversion, performed with a subgrid model to connect disparate scales together, can also yield information on the fine-scale properties. We model the binary medium using truncated Gaussian fields, and develop a subgrid model for the upscaled permeability based on excursion sets of those fields. The subgrid model requires an estimate of the proportion of inclusions at the block scale as well as some geometrical parameters of the inclusions as inputs, and predicts the effective permeability. The inclusion proportion is assumed to be spatially varying, modeled using Gaussian processes and represented using a truncated Karhunen-Louve (KL) expansion. This expansion is used, along with the subgrid model, to pose as a Bayesian inverse problem for the KL weights and the geometrical parameters of the inclusions. The model error is represented in two different ways: (1) as a homoscedastic error and (2) as a heteroscedastic error, dependent on inclusion proportionality and geometry. The error models impact the form of the likelihood function in the expression for the posterior density of the objects of inference. The problem is solved using an adaptive Markov Chain Monte Carlo method, and joint posterior distributions are developed for the KL weights and inclusion geometry. Effective permeabilities and tracer breakthrough times at a few
Inverse analysis and regularisation in conditional source-term estimation modelling
Labahn, Jeffrey W.; Devaud, Cecile B.; Sipkens, Timothy A.; Daun, Kyle J.
2014-05-01
Conditional Source-term Estimation (CSE) obtains the conditional species mass fractions by inverting a Fredholm integral equation of the first kind. In the present work, a Bayesian framework is used to compare two different regularisation methods: zeroth-order temporal Tikhonov regulatisation and first-order spatial Tikhonov regularisation. The objectives of the current study are: (i) to elucidate the ill-posedness of the inverse problem; (ii) to understand the origin of the perturbations in the data and quantify their magnitude; (iii) to quantify the uncertainty in the solution using different priors; and (iv) to determine the regularisation method best suited to this problem. A singular value decomposition shows that the current inverse problem is ill-posed. Perturbations to the data may be caused by the use of a discrete mixture fraction grid for calculating the mixture fraction PDF. The magnitude of the perturbations is estimated using a box filter and the uncertainty in the solution is determined based on the width of the credible intervals. The width of the credible intervals is significantly reduced with the inclusion of a smoothing prior and the recovered solution is in better agreement with the exact solution. The credible intervals for temporal and spatial smoothing are shown to be similar. Credible intervals for temporal smoothing depend on the solution from the previous time step and a smooth solution is not guaranteed. For spatial smoothing, the credible intervals are not dependent upon a previous solution and better predict characteristics for higher mixture fraction values. These characteristics make spatial smoothing a promising alternative method for recovering a solution from the CSE inversion process.
Quintero-Chavarria, E.; Ochoa Gutierrez, L. H.
2016-12-01
Applications of the Self-potential Method in the fields of Hydrogeology and Environmental Sciences have had significant developments during the last two decades with a strong use on groundwater flows identification. Although only few authors deal with the forward problem's solution -especially in geophysics literature- different inversion procedures are currently being developed but in most cases they are compared with unconventional groundwater velocity fields and restricted to structured meshes. This research solves the forward problem based on the finite element method using the St. Venant's Principle to transform a point dipole, which is the field generated by a single vector, into a distribution of electrical monopoles. Then, two simple aquifer models were generated with specific boundary conditions and head potentials, velocity fields and electric potentials in the medium were computed. With the model's surface electric potential, the inverse problem is solved to retrieve the source of electric potential (vector field associated to groundwater flow) using deterministic and stochastic approaches. The first approach was carried out by implementing a Tikhonov regularization with a stabilized operator adapted to the finite element mesh while for the second a hierarchical Bayesian model based on Markov chain Monte Carlo (McMC) and Markov Random Fields (MRF) was constructed. For all implemented methods, the result between the direct and inverse models was contrasted in two ways: 1) shape and distribution of the vector field, and 2) magnitude's histogram. Finally, it was concluded that inversion procedures are improved when the velocity field's behavior is considered, thus, the deterministic method is more suitable for unconfined aquifers than confined ones. McMC has restricted applications and requires a lot of information (particularly in potentials fields) while MRF has a remarkable response especially when dealing with confined aquifers.
A stochastic approach for model reduction and memory function design in hydrogeophysical inversion
Hou, Z.; Kellogg, A.; Terry, N.
2009-12-01
Geophysical (e.g., seismic, electromagnetic, radar) techniques and statistical methods are essential for research related to subsurface characterization, including monitoring subsurface flow and transport processes, oil/gas reservoir identification, etc. For deep subsurface characterization such as reservoir petroleum exploration, seismic methods have been widely used. Recently, electromagnetic (EM) methods have drawn great attention in the area of reservoir characterization. However, considering the enormous computational demand corresponding to seismic and EM forward modeling, it is usually a big problem to have too many unknown parameters in the modeling domain. For shallow subsurface applications, the characterization can be very complicated considering the complexity and nonlinearity of flow and transport processes in the unsaturated zone. It is warranted to reduce the dimension of parameter space to a reasonable level. Another common concern is how to make the best use of time-lapse data with spatial-temporal correlations. This is even more critical when we try to monitor subsurface processes using geophysical data collected at different times. The normal practice is to get the inverse images individually. These images are not necessarily continuous or even reasonably related, because of the non-uniqueness of hydrogeophysical inversion. We propose to use a stochastic framework by integrating minimum-relative-entropy concept, quasi Monto Carlo sampling techniques, and statistical tests. The approach allows efficient and sufficient exploration of all possibilities of model parameters and evaluation of their significances to geophysical responses. The analyses enable us to reduce the parameter space significantly. The approach can be combined with Bayesian updating, allowing us to treat the updated ‘posterior’ pdf as a memory function, which stores all the information up to date about the distributions of soil/field attributes/properties, then consider the
Tiampo, K. F.; Fernández, J.; Jentzsch, G.; Charco, M.; Rundle, J. B.
2004-11-01
Here we present an inversion methodology using the combination of a genetic algorithm (GA) inversion program, and an elastic-gravitational earth model to determine the parameters of a volcanic intrusion. Results from the integration of the elastic-gravitational model, a suite of FORTRAN 77 programs developed to compute the displacements due to volcanic loading, with the GA inversion code, written in the C programming language, are presented. These codes allow for the calculation of displacements (horizontal and vertical), tilt, vertical strain and potential and gravity changes on the surface of an elastic-gravitational layered Earth model due to the magmatic intrusion. We detail the appropriate methodology for examining the sensitivity of the model to variation in the constituent parameters using the GA, and present, for the first time, a Monte Carlo technique for evaluating the propagation of error through the GA inversion process. One application example is given at Mayon volcano, Philippines, for the inversion program, the sensitivity analysis, and the error evaluation. The integration of the GA with the complex elastic-gravitational model is a blueprint for an efficient nonlinear inversion methodology and its implementation into an effective tool for the evaluation of parameter sensitivity. Finally, the extension of this inversion algorithm and the error assessment methodology has important implications to the modeling and data assimilation of a number of other nonlinear applications in the field of geosciences.
DEFF Research Database (Denmark)
Ren, Tao; Modest, Michael F.; Fateev, Alexander
2015-01-01
In this study, we present an inverse calculation model based on the Levenberg-Marquardt optimization method to reconstruct temperature and species concentration from measured line-of-sight spectral transmissivity data for homogeneous gaseous media. The high temperature gas property database HITEMP...... 2010 (Rothman et al. (2010) [1]), which contains line-by-line (LBL) information for several combustion gas species, such as CO2 and H2O, was used to predict gas spectral transmissivities. The model was validated by retrieving temperatures and species concentrations from experimental CO2 and H2O...
The research on method of interlayer modeling based on seismic inversion and petrophysical facies
Directory of Open Access Journals (Sweden)
Chao Cheng
2016-03-01
Full Text Available Currently, the three-dimensional distribution of interlayer is realized by stochastic modeling. Traditionally, the three-dimensional geological modeling controlled by sedimentary facies models is built on the basis of logging interpretation parameters and geophysical information. Because of shallow gas-cap, the quality of three-dimensional seismic data vertical resolution in research area cannot meet the interlayer research that is below ten meters. Moreover, sedimentary facies cannot commendably reveal interlayer distribution and the well density is very sparse in research area. So, it is difficult for conventional technology to finely describe interlayers. In this document, it uses L1-L2 combined norm constrained inversion to enhance the recognition capability of interlayer in seismic profile and improve the signal to noise ratio, the wave group characteristics and the vertical resolution of three-dimensional data and classifies petrophysical facies of interlayer based on core, sedimentary facies and logging interpretation. The interlayer model which is based on seismic inversion model and petrophysical facies can precisely simulate the distribution of reservoir and interlayer. The results show that the simulation results of this new methodology are consistent with the dynamic production perfectly which provide a better basis for producing and mining remaining oil and a new interlayer modeling method for sparse well density.
Inverse modeling of test SB4-VM2/216.7 at Wellenberg
International Nuclear Information System (INIS)
Finsterle, S.
1994-03-01
Pressure and flow rate data from a water sampling test, which also produced gas, at the Wellenberg site are analyzed using inverse modeling techniques. Two conceptual models are developed and used for parameter estimation. The first model assumes that the gas observed at the surface is dissolved in the pore water under natural pressure and temperature conditions and comes out of solution due to the pressure reduction during pumping. The second model considers a mobile gas phase originally present in the formation. While both models are able to explain the observed pressure response as well as the gas seen at the surface, large uncertainties in the data and in the model assumptions inhibit the determination of two-phase flow parameters. The analysis indicates, however, that the formation has a very low permeability and that formation head is far below hydrostatic
Rafique, Rashad; Fienen, Michael N.; Parkin, Timothy B.; Anex, Robert P.
2013-01-01
DayCent is a biogeochemical model of intermediate complexity widely used to simulate greenhouse gases (GHG), soil organic carbon and nutrients in crop, grassland, forest and savannah ecosystems. Although this model has been applied to a wide range of ecosystems, it is still typically parameterized through a traditional “trial and error” approach and has not been calibrated using statistical inverse modelling (i.e. algorithmic parameter estimation). The aim of this study is to establish and demonstrate a procedure for calibration of DayCent to improve estimation of GHG emissions. We coupled DayCent with the parameter estimation (PEST) software for inverse modelling. The PEST software can be used for calibration through regularized inversion as well as model sensitivity and uncertainty analysis. The DayCent model was analysed and calibrated using N2O flux data collected over 2 years at the Iowa State University Agronomy and Agricultural Engineering Research Farms, Boone, IA. Crop year 2003 data were used for model calibration and 2004 data were used for validation. The optimization of DayCent model parameters using PEST significantly reduced model residuals relative to the default DayCent parameter values. Parameter estimation improved the model performance by reducing the sum of weighted squared residual difference between measured and modelled outputs by up to 67 %. For the calibration period, simulation with the default model parameter values underestimated mean daily N2O flux by 98 %. After parameter estimation, the model underestimated the mean daily fluxes by 35 %. During the validation period, the calibrated model reduced sum of weighted squared residuals by 20 % relative to the default simulation. Sensitivity analysis performed provides important insights into the model structure providing guidance for model improvement.
The Impact of Forest Density on Forest Height Inversion Modeling from Polarimetric InSAR Data
Directory of Open Access Journals (Sweden)
Changcheng Wang
2016-03-01
Full Text Available Forest height is of great significance in analyzing the carbon cycle on a global or a local scale and in reconstructing the accurate forest underlying terrain. Major algorithms for estimating forest height, such as the three-stage inversion process, are depending on the random-volume-over-ground (RVoG model. However, the RVoG model is characterized by a lot of parameters, which influence its applicability in forest height retrieval. Forest density, as an important biophysical parameter, is one of those main influencing factors. However, its influence to the RVoG model has been ignored in relating researches. For this paper, we study the applicability of the RVoG model in forest height retrieval with different forest densities, using the simulated and real Polarimetric Interferometric SAR data. P-band ESAR datasets of the European Space Agency (ESA BioSAR 2008 campaign were selected for experiments. The test site was located in Krycklan River catchment in Northern Sweden. The experimental results show that the forest density clearly affects the inversion accuracy of forest height and ground phase. For the four selected forest stands, with the density increasing from 633 to 1827 stems/Ha, the RMSEs of inversion decrease from 4.6 m to 3.1 m. The RVoG model is not quite applicable for forest height retrieval especially in sparsely vegetated areas. We conclude that the forest stand density is positively related to the estimation accuracy of the ground phase, but negatively correlates to the ground-to-volume scattering ratio.
International Nuclear Information System (INIS)
Winiarek, Victor
2014-01-01
Uncontrolled releases of pollutant in the atmosphere may be the consequence of various situations: accidents, for instance leaks or explosions in an industrial plant, or terrorist attacks such as biological bombs, especially in urban areas. In the event of such situations, authorities' objectives are various: predict the contaminated zones to apply first countermeasures such as evacuation of concerned population; determine the source location; assess the long-term polluted areas, for instance by deposition of persistent pollutants in the soil. To achieve these objectives, numerical models can be used to model the atmospheric dispersion of pollutants. We will first present the different processes that govern the transport of pollutants in the atmosphere, then the different numerical models that are commonly used in this context. The choice between these models mainly depends of the scale and the details one seeks to take into account. We will then present several inverse modeling methods to estimate the emission as well as statistical methods to estimate prior errors, to which the inversion is very sensitive. Several case studies are presented, using synthetic data as well as real data such as the estimation of source terms from the Fukushima accident in March 2011. From our results, we estimate the Cesium-137 emission to be between 12 and 19 PBq with a standard deviation between 15 and 65% and the Iodine-131 emission to be between 190 and 380 PBq with a standard deviation between 5 and 10%. Concerning the localization of an unknown source of pollutant, two strategies can be considered. On one hand parametric methods use a limited number of parameters to characterize the source term to be reconstructed. To do so, strong assumptions are made on the nature of the source. The inverse problem is hence to estimate these parameters. On the other hand nonparametric methods attempt to reconstruct a full emission field. Several parametric and nonparametric methods are
Energy Technology Data Exchange (ETDEWEB)
Xie, G.; Li, J.; Majer, E.; Zuo, D.
1998-07-01
This paper describes a new 3D parallel GILD electromagnetic (EM) modeling and nonlinear inversion algorithm. The algorithm consists of: (a) a new magnetic integral equation instead of the electric integral equation to solve the electromagnetic forward modeling and inverse problem; (b) a collocation finite element method for solving the magnetic integral and a Galerkin finite element method for the magnetic differential equations; (c) a nonlinear regularizing optimization method to make the inversion stable and of high resolution; and (d) a new parallel 3D modeling and inversion using a global integral and local differential domain decomposition technique (GILD). The new 3D nonlinear electromagnetic inversion has been tested with synthetic data and field data. The authors obtained very good imaging for the synthetic data and reasonable subsurface EM imaging for the field data. The parallel algorithm has high parallel efficiency over 90% and can be a parallel solver for elliptic, parabolic, and hyperbolic modeling and inversion. The parallel GILD algorithm can be extended to develop a high resolution and large scale seismic and hydrology modeling and inversion in the massively parallel computer.
Directory of Open Access Journals (Sweden)
S. L. Heck
2012-02-01
Full Text Available There is a widely recognized need to improve our understanding of biosphere-atmosphere carbon exchanges in areas of complex terrain including the United States Mountain West. CO2 fluxes over mountainous terrain are often difficult to measure due to unusual and complicated influences associated with atmospheric transport. Consequently, deriving regional fluxes in mountain regions with carbon cycle inversion of atmospheric CO2 mole fraction is sensitive to filtering of observations to those that can be represented at the transport model resolution. Using five years of CO2 mole fraction observations from the Regional Atmospheric Continuous CO2 Network in the Rocky Mountains (Rocky RACCOON, five statistical filters are used to investigate a range of approaches for identifying regionally representative CO2 mole fractions. Test results from three filters indicate that subsets based on short-term variance and local CO2 gradients across tower inlet heights retain nine-tenths of the total observations and are able to define representative diel variability and seasonal cycles even for difficult-to-model sites where the influence of local fluxes is much larger than regional mole fraction variations. Test results from two other filters that consider measurements from previous and following days using spline fitting or sliding windows are overly selective. Case study examples showed that these windowing-filters rejected measurements representing synoptic changes in CO2, which suggests that they are not well suited to filtering continental CO2 measurements. We present a novel CO2 lapse rate filter that uses CO2 differences between levels in the model atmosphere to select subsets of site measurements that are representative on model scales. Our new filtering techniques provide guidance for novel approaches to assimilating mountain-top CO2 mole fractions in carbon cycle inverse models.
Al-Ma'shumah, Fathimah; Permana, Dony; Sidarto, Kuntjoro Adji
2015-12-01
Customer Lifetime Value is an important and useful concept in marketing. One of its benefits is to help a company for budgeting marketing expenditure for customer acquisition and customer retention. Many mathematical models have been introduced to calculate CLV considering the customer retention/migration classification scheme. A fairly new class of these models which will be described in this paper uses Markov Chain Models (MCM). This class of models has the major advantage for its flexibility to be modified to several different cases/classification schemes. In this model, the probabilities of customer retention and acquisition play an important role. From Pfeifer and Carraway, 2000, the final formula of CLV obtained from MCM usually contains nonlinear form of the transition probability matrix. This nonlinearity makes the inverse problem of CLV difficult to solve. This paper aims to solve this inverse problem, yielding the approximate transition probabilities for the customers, by applying metaheuristic optimization algorithm developed by Yang, 2013, Flower Pollination Algorithm. The major interpretation of obtaining the transition probabilities are to set goals for marketing teams in keeping the relative frequencies of customer acquisition and customer retention.
Directory of Open Access Journals (Sweden)
A. Stanley Raj
2015-01-01
Full Text Available Soft computing based geoelectrical data inversion differs from conventional computing in fixing the uncertainty problems. It is tractable, robust, efficient, and inexpensive. In this paper, fuzzy logic clustering methods are used in the inversion of geoelectrical resistivity data. In order to characterize the subsurface features of the earth one should rely on the true field oriented data validation. This paper supports the field data obtained from the published results and also plays a crucial role in making an interdisciplinary approach to solve complex problems. Three clustering algorithms of fuzzy logic, namely, fuzzy C-means clustering, fuzzy K-means clustering, and fuzzy subtractive clustering, were analyzed with the help of fuzzy inference system (FIS training on synthetic data. Here in this approach, graphical user interface (GUI was developed with the integration of three algorithms and the input data (AB/2 and apparent resistivity, while importing will process each algorithm and interpret the layer model parameters (true resistivity and depth. A complete overview on the three above said algorithms is presented in the text. It is understood from the results that fuzzy logic subtractive clustering algorithm gives more reliable results and shows efficacy of soft computing tools in the inversion of geoelectrical resistivity data.
Estimation of semolina dough rheological parameters by inversion of a finite elements model
Directory of Open Access Journals (Sweden)
Angelo Fabbri
2015-10-01
Full Text Available The description of the rheological properties of food material plays an important role in food engineering. Particularly for the optimisation of pasta manufacturing process (extrusion is needful to know the rheological properties of semolina dough. Unfortunately characterisation of non-Newtonian fluids, such as food doughs, requires a notable time effort, especially in terms of number of tests to be carried out. The present work proposes an alternative method, based on the combination of laboratory measurement, made with a simplified tool, with the inversion of a finite elements numerical model. To determine the rheological parameters, an objective function, defined as the distance between simulation and experimental data, was considered and the well-known Levenberg-Marqard optimisation algorithm was used. In order to verify the feasibility of the method, the rheological characterisation of the dough was carried also by a traditional procedure. Results shown that the difference between measurements of rheological parameters of the semolina dough made with traditional procedure and inverse methods are very small (maximum percentage error equal to 3.6%. This agreement supports the coherence of the inverse method that, in general, may be used to characterise many non-Newtonian materials.
Thermal-hydraulic modeling of flow inversion in a research reactor
International Nuclear Information System (INIS)
Kazeminejad, H.
2008-01-01
The course of loss of flow accident and flow inversion in a pool type research reactor, with scram enabled under natural circulation condition is numerically investigated. The analyses were performed by a lumped parameters approach for the coupled kinetic-thermal-hydraulics, with continuous feedback due to coolant and fuel temperature effects. A modified Runge-Kutta method was adopted for a better solution to the set of stiff differential equations. Transient thermal-hydraulics during the process of flow inversion and establishment of natural circulation were considered for a 10-MW IAEA research reactor. Some important parameters such as the peak temperatures for the hot channel were obtained for both high-enriched and low enriched fuel. The model prediction is also verified through comparison with other computer code results reported in the literature for detailed simulations of loss of flow accidents (LOFA) and the agreement between the results for the peak clad temperatures and key parameters has been satisfactory. It was found that the flow inversion and subsequent establishment of natural circulation keep the peak cladding surface temperature below the saturation temperature to avoid the escalation of clad temperature to the level of onset of nucleate boiling and sub-cooled void formation to ensure the safe operation of the reactor
Agata, R.; Ichimura, T.; Hirahara, K.; Hori, T.; Hyodo, M.; Hori, M.
2013-12-01
Many studies have focused on geodetic inversion analysis method of coseismic slip distribution with combination of observation data of coseismic crustal deformation on the ground and simplified crustal models such like analytical solution in elastic half-space (Okada, 1985). On the other hand, displacements on the seafloor or near trench axes due to actual earthquakes has been observed by seafloor observatories (e.g. the 2011 Tohoku-oki Earthquake (Tohoku Earthquake) (Sato et. al. 2011) (Kido et. al. 2011)). Also, some studies on tsunamis due to the Tohoku Earthquake indicate that large fault slips near the trench axis may have occurred. Those facts suggest that crustal models considering complex geometry and heterogeneity of the material property near the trench axis should be used for geodetic inversion analysis. Therefore, our group has developed a mesh generation method for finite element models of the Japanese Islands of higher fidelity and a fast crustal deformation analysis method for the models. Degree-of-freedom of the models generated by this method is about 150 million. In this research, the method is extended for inversion analyses of coseismic slip distribution. Since inversion analyses need computation of hundreds of slip response functions due to a unit fault slip assigned for respective divided cells on the fault, parallel computing environment is used. Plural crustal deformation analyses are simultaneously run in a Message Passing Interface (MPI) job. In the job, dynamic load balancing is implemented so that a better parallel efficiency is obtained. Submitting the necessary number of serial job of our previous method is also possible, but the proposed method needs less computation time, places less stress on file systems, and allows simpler job management. A method for considering the fault slip right near the trench axis is also developed. As the displacement distribution of unit fault slip for computing response function, 3rd order B
Gholami, A.; Siahkoohi, H. R.
2009-01-01
In this paper, a new approach is introduced to solve ill-posed linear inverse problems in geophysics. Our method combines classical quadratic regularization and data smoothing by imposing constraints on model and data smoothness simultaneously. When imposing a quadratic penalty term in the data space to control smoothness of the data predicted by classical zero-order regularization, the method leads to a direct regularization in standard form, which is simple to be implemented and ensures that the estimated model is smooth. In addition, by enforcing Tikhonov's predicted data to be sparse in a wavelet domain, the idea leads to an efficient regularization algorithm with two superior properties. First, the algorithm ensures the smoothness of the estimated model while substantially preserving the edges of it, so, it is well suited for recovering piecewise smooth/constant models. Second, parsimony of wavelets on the columns of the forward operator and existence of a fast wavelet transform algorithm provide an efficient sparse representation of the forward operator matrix. The reduced size of the forward operator makes the solution of large-scale problems straightforward, because during the inversion process, only sparse matrices need to be stored, which reduces the memory required. Additionally, all matrix-vector multiplications are carried out in sparse form, reducing CPU time. Applications on both synthetic and real 1-D seismic-velocity estimation experiments illustrate the idea. The performance of the method is compared with that of classical quadratic regularization, total-variation regularization and a two-step, wavelet-based, inversion method.
Werdell, P. Jeremy; Ooesler, Collin S.
2012-01-01
The daily, synoptic images provided by satellite ocean color instruments provide viable data streams for observing changes in the biogeochemistrY of marine ecosystems. Ocean reflectance inversion models (ORMs) provide a common mechanism for inverting the "color" of the water observed a satellite into marine inherent optical properties (lOPs) through a combination of empiricism and radiative transfer theory. lOPs, namely the spectral absorption and scattering characteristics of ocean water and its dissolved and particulate constituents, describe the contents of the upper ocean, information critical for furthering scientific understanding of biogeochemical oceanic processes. Many recent studies inferred marine particle sizes and discriminated between phytoplankton functional groups using remotely-sensed lOPs. While all demonstrated the viability of their approaches, few described the vertical distributions of the water column constituents under consideration and, thus, failed to report the biophysical conditions under which their model performed (e.g., the depth and thickness of the phytoplankton bloom(s)). We developed an ORM to remotely identifY Noctiluca miliaris and other phytoplankton functional types using satellite ocean color data records collected in the northern Arabian Sea. Here, we present results from analyses designed to evaluate the applicability and sensitivity of the ORM to varied biophysical conditions. Specifically, we: (1) synthesized a series of vertical profiles of spectral inherent optical properties that represent a wide variety of bio-optical conditions for the northern Arabian Sea under aN Miliaris bloom; (2) generated spectral remote-sensing reflectances from these profiles using Hydrolight; and, (3) applied the ORM to the synthesized reflectances to estimate the relative concentrations of diatoms and N Miliaris for each example. By comparing the estimates from the inversion model to those from synthesized vertical profiles, we were able to
Dewaele, Hélène; Munier, Simon; Albergel, Clément; Planque, Carole; Laanaia, Nabil; Carrer, Dominique; Calvet, Jean-Christophe
2017-09-01
Soil maximum available water content (MaxAWC) is a key parameter in land surface models (LSMs). However, being difficult to measure, this parameter is usually uncertain. This study assesses the feasibility of using a 15-year (1999-2013) time series of satellite-derived low-resolution observations of leaf area index (LAI) to estimate MaxAWC for rainfed croplands over France. LAI interannual variability is simulated using the CO2-responsive version of the Interactions between Soil, Biosphere and Atmosphere (ISBA) LSM for various values of MaxAWC. Optimal value is then selected by using (1) a simple inverse modelling technique, comparing simulated and observed LAI and (2) a more complex method consisting in integrating observed LAI in ISBA through a land data assimilation system (LDAS) and minimising LAI analysis increments. The evaluation of the MaxAWC estimates from both methods is done using simulated annual maximum above-ground biomass (Bag) and straw cereal grain yield (GY) values from the Agreste French agricultural statistics portal, for 45 administrative units presenting a high proportion of straw cereals. Significant correlations (p value Bag and GY are found for up to 36 and 53 % of the administrative units for the inverse modelling and LDAS tuning methods, respectively. It is found that the LDAS tuning experiment gives more realistic values of MaxAWC and maximum Bag than the inverse modelling experiment. Using undisaggregated LAI observations leads to an underestimation of MaxAWC and maximum Bag in both experiments. Median annual maximum values of disaggregated LAI observations are found to correlate very well with MaxAWC.
Whitton, R Christopher; Trope, Gareth D; Ghasem-Zadeh, Ali; Anderson, Garry A; Parkin, Timothy D H; Mackie, Eleanor J; Seeman, Ego
2010-10-01
Bone modelling and remodelling reduce the risk of fatigue fractures; the former by adapting bone to its loading circumstances, the latter by replacing fatigued bone. Remodelling transiently increases porosity because of the normal delay in onset of the formation phase of the remodelling sequence. Protracted intense loading suppresses remodelling leaving modelling as the only means of maintaining bone strength. We therefore hypothesized that race horses with fatigue fractures of the distal third metacarpal bone (MC3) will have reduced porosity associated with suppressed remodelling while continued adaptive modelling will result in higher volume fraction (BV/TV) at this site. Using high resolution peripheral quantitative computed tomography (HR-pQCT), we measured the distal aspect of the MC3 obtained at postmortem from 13 thoroughbred race horses with condylar fractures of the MC3 (cases), 8 horses without fractures (training controls), 14 horses with a fracture at another site (fractured controls) and 9 horses resting from training (resting controls). Porosity of the subchondral bone of MC3 was lower in cases than resting controls (12±1.4% vs. 18±1.6%, P=0.017) although areas of focal porosity were observed adjacent to fractures in 6/13 horses. BV/TV of the distal metacarpal epiphysis tended to be higher in horses with condylar fractures (0.79±0.015) than training controls (0.74±0.019, P=0.070), but also higher in controls with a fracture elsewhere (0.79±0.014) than the training controls (0.74±0.019, P=0.040). BV/TV was higher in horses over three years of age than those aged two or three years (0.79±0.01 vs. 0.74±0.01, P=0.016). All metacarpal condylar fractures occurred within focal areas of high BV/TV. We infer that intense training in equine athletes suppresses remodelling of third metacarpal subchondral bone limiting damage repair while modelling increases regional bone volume in an attempt to minimise local stresses but may fail to offset bone
Inverse modeling of the biodegradation of emerging organic contaminants in the soil-plant system.
Hurtado, Carlos; Trapp, Stefan; Bayona, Josep M
2016-08-01
Understanding the processes involved in the uptake and accumulation of organic contaminants into plants is very important to assess the possible human risk associated with. Biodegradation of emerging contaminants in plants has been observed, but kinetical studies are rare. In this study, we analyse experimental data on the uptake of emerging organic contaminants into lettuce derived in a greenhouse experiment. Measured soil, root and leaf concentrations from four contaminants were selected within the applicability domain of a steady-state two-compartment standard plant uptake model: bisphenol A (BPA), carbamazepine (CBZ), triclosan (TCS) and caffeine (CAF). The model overestimated concentrations in most cases, when no degradation rates in plants were entered. Subsequently, biodegradation rates were fitted so that the measured concentrations were met. Obtained degradation kinetics are in the order, BPA < CAF ≈ TCS < CBZ in roots, and BPA ≈ TCS < CBZ < CAF in leaves. Kinetics determined by inverse modeling are, despite the inherent uncertainty, indicative of the dissipation rates. The advantage of the procedure that is additional knowledge can be gained from existing experimental data. Dissipation kinetics found via inverse modeling is not a conclusive proof for biodegradation and confirmation by experimental studies is needed. Copyright © 2016. Published by Elsevier Ltd.
Inversion of the Jacobi-Porstendorfer room model for the radon progeny
International Nuclear Information System (INIS)
Thomas, J.; Jilek, K.; Brabec, M.
2010-01-01
The Jacobi-Porstendoerfer (J-P) room model describes the behaviour of radon progeny in the atmosphere of a room. It distinguishes between free and attached radon progeny in air. It has been successfully used without substantial changes for nearly 40 years. There have been several attempts to invert the model approximately to determine the parameters describing the physical processes. Here, an exact solution is aimed at as an algebraic inversion of the system of six linear equations for the five unknown physical parameters k, X, R, q f , q a of the room model. Two strong linear dependencies in this system, unfortunately do not allow to obtain a general solution (especially not for the ventilation coefficient k), but only a parameterized one or for reduced sets of unknown parameters. More, the impossibility to eliminate one of the two linear dependencies and the departures of the measured concentrations forces to solve a set of allowed combinations of equations of the algebraic system and to accept its mean values (therefore with variances) as a result of the algebraic inversion. These results are in agreement with results of the least squares method as well as of a sophisticated modern statistical approach. The algebraic approach provides, of course, a lot of analytical relations to study the mutual dependencies between the model parameters and the measurable quantities. (authors)
Directory of Open Access Journals (Sweden)
Martina Sobotkova
2011-08-01
Full Text Available A method for determining soil hydraulic properties of a weathered tropical soil (Oxisol using a medium-sized column with undisturbed soil is presented. The method was used to determine fitting parameters of the water retention curve and hydraulic conductivity functions of a soil column in support of a pesticide leaching study. The soil column was extracted from a continuously-used research plot in Central Oahu (Hawaii, USA and its internal structure was examined by computed tomography. The experiment was based on tension infiltration into the soil column with free outflow at the lower end. Water flow through the soil core was mathematically modeled using a computer code that numerically solves the one-dimensional Richards equation. Measured soil hydraulic parameters were used for direct simulation, and the retention and soil hydraulic parameters were estimated by inverse modeling. The inverse modeling produced very good agreement between model outputs and measured flux and pressure head data for the relatively homogeneous column. The moisture content at a given pressure from the retention curve measured directly in small soil samples was lower than that obtained through parameter optimization based on experiments using a medium-sized undisturbed soil column.
Global inverse modeling of CH4 sources and sinks: an overview of methods
Houweling, Sander; Bergamaschi, Peter; Chevallier, Frederic; Heimann, Martin; Kaminski, Thomas; Krol, Maarten; Michalak, Anna M.; Patra, Prabir
2017-01-01
The aim of this paper is to present an overview of inverse modeling methods that have been developed over the years for estimating the global sources and sinks of CH4. It provides insight into how techniques and estimates have evolved over time and what the remaining shortcomings are. As such, it serves a didactical purpose of introducing apprentices to the field, but it also takes stock of developments so far and reflects on promising new directions. The main focus is on methodological aspects that are particularly relevant for CH4, such as its atmospheric oxidation, the use of methane isotopologues, and specific challenges in atmospheric transport modeling of CH4. The use of satellite retrievals receives special attention as it is an active field of methodological development, with special requirements on the sampling of the model and the treatment of data uncertainty. Regional scale flux estimation and attribution is still a grand challenge, which calls for new methods capable of combining information from multiple data streams of different measured parameters. A process model representation of sources and sinks in atmospheric transport inversion schemes allows the integrated use of such data. These new developments are needed not only to improve our understanding of the main processes driving the observed global trend but also to support international efforts to reduce greenhouse gas emissions.
Energy Technology Data Exchange (ETDEWEB)
Thomas, Edward V. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Stork, Christopher L. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Mattingly, John K. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2015-07-01
Inverse radiation transport focuses on identifying the configuration of an unknown radiation source given its observed radiation signatures. The inverse problem is traditionally solved by finding the set of transport model parameter values that minimizes a weighted sum of the squared differences by channel between the observed signature and the signature pre dicted by the hypothesized model parameters. The weights are inversely proportional to the sum of the variances of the measurement and model errors at a given channel. The traditional implicit (often inaccurate) assumption is that the errors (differences between the modeled and observed radiation signatures) are independent across channels. Here, an alternative method that accounts for correlated errors between channels is described and illustrated using an inverse problem based on the combination of gam ma and neutron multiplicity counting measurements.
Brain functional BOLD perturbation modelling for forward fMRI and inverse mapping
Robinson, Jennifer; Calhoun, Vince
2018-01-01
Purpose To computationally separate dynamic brain functional BOLD responses from static background in a brain functional activity for forward fMRI signal analysis and inverse mapping. Methods A brain functional activity is represented in terms of magnetic source by a perturbation model: χ = χ0 +δχ, with δχ for BOLD magnetic perturbations and χ0 for background. A brain fMRI experiment produces a timeseries of complex-valued images (T2* images), whereby we extract the BOLD phase signals (denoted by δP) by a complex division. By solving an inverse problem, we reconstruct the BOLD δχ dataset from the δP dataset, and the brain χ distribution from a (unwrapped) T2* phase image. Given a 4D dataset of task BOLD fMRI, we implement brain functional mapping by temporal correlation analysis. Results Through a high-field (7T) and high-resolution (0.5mm in plane) task fMRI experiment, we demonstrated in detail the BOLD perturbation model for fMRI phase signal separation (P + δP) and reconstructing intrinsic brain magnetic source (χ and δχ). We also provided to a low-field (3T) and low-resolution (2mm) task fMRI experiment in support of single-subject fMRI study. Our experiments show that the δχ-depicted functional map reveals bidirectional BOLD χ perturbations during the task performance. Conclusions The BOLD perturbation model allows us to separate fMRI phase signal (by complex division) and to perform inverse mapping for pure BOLD δχ reconstruction for intrinsic functional χ mapping. The full brain χ reconstruction (from unwrapped fMRI phase) provides a new brain tissue image that allows to scrutinize the brain tissue idiosyncrasy for the pure BOLD δχ response through an automatic function/structure co-localization. PMID:29351339
Brain functional BOLD perturbation modelling for forward fMRI and inverse mapping.
Chen, Zikuan; Robinson, Jennifer; Calhoun, Vince
2018-01-01
To computationally separate dynamic brain functional BOLD responses from static background in a brain functional activity for forward fMRI signal analysis and inverse mapping. A brain functional activity is represented in terms of magnetic source by a perturbation model: χ = χ0 +δχ, with δχ for BOLD magnetic perturbations and χ0 for background. A brain fMRI experiment produces a timeseries of complex-valued images (T2* images), whereby we extract the BOLD phase signals (denoted by δP) by a complex division. By solving an inverse problem, we reconstruct the BOLD δχ dataset from the δP dataset, and the brain χ distribution from a (unwrapped) T2* phase image. Given a 4D dataset of task BOLD fMRI, we implement brain functional mapping by temporal correlation analysis. Through a high-field (7T) and high-resolution (0.5mm in plane) task fMRI experiment, we demonstrated in detail the BOLD perturbation model for fMRI phase signal separation (P + δP) and reconstructing intrinsic brain magnetic source (χ and δχ). We also provided to a low-field (3T) and low-resolution (2mm) task fMRI experiment in support of single-subject fMRI study. Our experiments show that the δχ-depicted functional map reveals bidirectional BOLD χ perturbations during the task performance. The BOLD perturbation model allows us to separate fMRI phase signal (by complex division) and to perform inverse mapping for pure BOLD δχ reconstruction for intrinsic functional χ mapping. The full brain χ reconstruction (from unwrapped fMRI phase) provides a new brain tissue image that allows to scrutinize the brain tissue idiosyncrasy for the pure BOLD δχ response through an automatic function/structure co-localization.
Salvus: A scalable software suite for full-waveform modelling & inversion
Afanasiev, M.; Boehm, C.; van Driel, M.; Krischer, L.; Fichtner, A.
2017-12-01
Full-waveform inversion (FWI), whether at the lab, exploration, or planetary scale, requires the cooperation of five principal components. (1) The geometry of the domain needs to be properly discretized and an initial guess of the model parameters must be projected onto it; (2) Large volumes of recorded waveform data must be collected, organized, and processed; (3) Synthetic waveform data must be efficiently and accurately computed through complex domains; (4) Suitable misfit functions and optimization techniques must be used to relate discrepancies in data space to perturbations in the model; and (5) Some form of workflow management must be employed to schedule and run (1) - (4) in the correct order. Each one of these components can represent a formidable technical challenge which redirects energy from the true task at hand: using FWI to extract new information about some underlying continuum.In this presentation we give an overview of the current status of the Salvus software suite, which was introduced to address the challenges listed above. Specifically, we touch on (1) salvus_mesher, which eases the discretization of complex Earth models into hexahedral meshes; (2) salvus_seismo, which integrates with LASIF and ObsPy to streamline the processing and preparation of seismic data; (3) salvus_wave, a high-performance and scalable spectral-element solver capable of simulating waveforms through general unstructured 2- and 3-D domains, and (4) salvus_opt, an optimization toolbox specifically designed for full-waveform inverse problems. Tying everything together, we also discuss (5) salvus_flow: a workflow package designed to orchestrate and manage the rest of the suite. It is our hope that these developments represent a step towards the automation of large-scale seismic waveform inversion, while also lowering the barrier of entry for new applications. We include several examples of Salvus' use in (extra-) planetary seismology, non-destructive testing, and medical
Kahl, Gunnar M; Sidorenko, Yury; Gottesbüren, Bernhard
2015-04-01
As an option for higher-tier leaching assessment of pesticides in Europe according to FOCUS, pesticide properties can be estimated from lysimeter studies by inversely fitting parameter values (substance half-life DT50 and sorption coefficient to organic matter kom ). The aim of the study was to identify adequate methods for inverse modelling. Model parameters for the PEARL (Pesticide Emission Assessment at Regional and Local scales) model were estimated with different inverse optimisation algorithms - the Levenberg-Marquardt (LM), PD_MS2 (PEST Driver Multiple Starting Points 2) and SCEM (Shuffled Complex Evolution Metropolis) algorithms. Optimisation of crop factors and hydraulic properties was found to be an ill-posed problem, and all algorithms failed to identify reliable global minima for the hydrological parameters. All algorithms performed equally well in estimating pesticide sorption and degradation parameters. SCEM was in most cases the only algorithm that reliably calculated uncertainties. The most reliable approach for finding the best parameter set in the stepwise approach of optimising evapotranspiration, soil hydrology and pesticide parameters was to run only SCEM or a combined approach with more than one algorithm using the best fit of each step for further processing. PD_MS2 was well suited to a quick parameter search. The linear parameter uncertainty intervals estimated by LM and PD_MS2 were usually larger than by the non-linear method used by SCEM. With the suggested methods, parameter optimisation, together with reliable estimation of uncertainties, is possible also for relatively complex systems. © 2014 Society of Chemical Industry.
Laterally constrained inversion for CSAMT data interpretation
Wang, Ruo; Yin, Changchun; Wang, Miaoyue; Di, Qingyun
2015-10-01
Laterally constrained inversion (LCI) has been successfully applied to the inversion of dc resistivity, TEM and airborne EM data. However, it hasn't been yet applied to the interpretation of controlled-source audio-frequency magnetotelluric (CSAMT) data. In this paper, we apply the LCI method for CSAMT data inversion by preconditioning the Jacobian matrix. We apply a weighting matrix to Jacobian to balance the sensitivity of model parameters, so that the resolution with respect to different model parameters becomes more uniform. Numerical experiments confirm that this can improve the convergence of the inversion. We first invert a synthetic dataset with and without noise to investigate the effect of LCI applications to CSAMT data, for the noise free data, the results show that the LCI method can recover the true model better compared to the traditional single-station inversion; and for the noisy data, the true model is recovered even with a noise level of 8%, indicating that LCI inversions are to some extent noise insensitive. Then, we re-invert two CSAMT datasets collected respectively in a watershed and a coal mine area in Northern China and compare our results with those from previous inversions. The comparison with the previous inversion in a coal mine shows that LCI method delivers smoother layer interfaces that well correlate to seismic data, while comparison with a global searching algorithm of simulated annealing (SA) in a watershed shows that though both methods deliver very similar good results, however, LCI algorithm presented in this paper runs much faster. The inversion results for the coal mine CSAMT survey show that a conductive water-bearing zone that was not revealed by the previous inversions has been identified by the LCI. This further demonstrates that the method presented in this paper works for CSAMT data inversion.
ANFIS based modeling and inverse control of a thin SMA wire
Kilicarslan, Atilla; Song, Gangbing; Grigoriadis, Karolos
2008-03-01
In this work, we propose an Adaptive Neuro Fuzzy Inference System (ANFIS) based hysteresis modeling and control strategy for a thin Shape Memory Alloy (SMA) wire. Controlling the SMA wire is a challenging problem because of its dynamic hysteretic behavior. By using a hybrid learning procedure ANFIS architectures are powerful tools for many applications, such as identifying nonlinear parameters in a controlled system, predicting chaotic time series and modeling nonlinear functions. We tested our ANFIS model by making it predict major and minor hysteresis loops in different driving frequencies and compared them with the experimental data. To compensate the hysteretic effect, we used an inverse ANFIS model and used it directly as a controller. After dramatically reducing the hysteretic effect, we implemented a PI control to fine tune the response.
Inverse modelling of European N2O emissions. Assimilating observations from different networks
Energy Technology Data Exchange (ETDEWEB)
Corazza, M.; Bergamaschi, P.; Dentener, F. [European Commission Joint Research Centre, Institute for Environment and Sustainability, 21027 Ispra (Italy); Vermeulen, A.T.; Popa, E. [Energy research Centre of the Netherlands ECN, Petten (Netherlands); Aalto, T. [Finnish Meteorological Institute FMI, Helsinki (Finland); Haszpra, L. [Hungarian Meteorological Service, Budapest (Hungary); Meinhardt, F. [Umweltbundesamt UBA, Messstelle Schauinsland, Kirchzarten (Germany); O' Doherty, S. [School of Chemistry, University of Bristol, Bristol (United Kingdom); Thompson, R. [Laboratoire des Sciences du Climat et de l' Environment LSCE, Gif sur Yvette (France); Moncrieff, J. [Edinburgh University, Edinburgh (United Kingdom); Steinbacher, M. [Swiss Federal Laboratories for Materials Science and Technology Empa, Duebendorf (Switzerland); Jordan, A. [Max Planck Institute for Biogeochemistry, Jena (Germany); Dlugokencky, E. [NOAA Earth System Research Laboratory, Global Monitoring Division, Boulder, CO (United States); Bruehl, C. [Max Planck Institute for Chemistry, Mainz (Germany); Krol, M. [Wageningen University and Research Centre WUR, Wageningen (Netherlands)
2010-07-01
We describe the setup and first results of an inverse modelling system for atmospheric N2O, based on a four-dimensional variational (4DVAR) technique and the atmospheric transport zoom model TM5. We focus in this study on the European domain, utilizing a comprehensive set of quasi-continuous measurements over Europe, complemented by N2O measurements from the Earth System Research Laboratory of the National Oceanic and Atmospheric Administration (NOAA/ESRL) cooperative global air sampling network. Despite ongoing measurement comparisons among networks parallel measurements at a limited number of stations show that significant offsets exist among the different laboratories. Since the spatial gradients of N2O mixing ratios are of the same order of magnitude as these biases, the direct use of these biased datasets would lead to significant errors in the derived emissions. Therefore, in order to also use measurements with unknown offsets, a new bias correction scheme has been implemented within the TM5-4DVAR inverse modelling system, thus allowing the simultaneous assimilation of observations from different networks. The N2O bias corrections determined in the TM5-4DVAR system agree within 0.1 ppb (dry-air mole fraction) with the bias derived from the measurements at monitoring stations where parallel NOAA discrete air samples are available. The N2O emissions derived for the northwest European countries for 2006 show good agreement with the bottom-up emission inventories reported to the United Nations Framework Convention on Climate Change (UNFCCC). Moreover, the inverse model can significantly narrow the uncertainty range reported in N2O emission inventories, while the lack of measurements does not allow for better emission estimates in southern Europe. Several sensitivity experiments were performed to test the robustness of the results. It is shown that also inversions without detailed a priori spatio-temporal emission distributions are capable to reproduce major
An inverse voter model for co-evolutionary networks: Stationary efficiency and phase transitions
International Nuclear Information System (INIS)
Zhu Chenping; Kong Hui; Li Li; Gu Zhiming; Xiong Shijie
2011-01-01
In some co-evolutionary networks, the nodes always flip their states between two opposite ones, changing the types of the links to others correspondingly. Meanwhile, the link-rewiring and state-flipping processes feed back each other, and only the links between the nodes in the opposite states are productive in generating flow for the network. We propose an inverse voter model to depict the basic features of them. New phase transitions from full efficiency to deficiency state are found by both the analysis and simulations starting from the random graphs and small-world networks. We suggest a new way to measure the efficiency of networks.
A model for the inverse 1-median problem on trees under uncertain costs
Directory of Open Access Journals (Sweden)
Kien Trung Nguyen
2016-01-01
Full Text Available We consider the problem of justifying vertex weights of a tree under uncertain costs so that a prespecified vertex become optimal and the total cost should be optimal in the uncertainty scenario. We propose a model which delivers the information about the optimal cost which respect to each confidence level \\(\\alpha \\in [0,1]\\. To obtain this goal, we first define an uncertain variable with respect to the minimum cost in each confidence level. If all costs are independently linear distributed, we present the inverse distribution function of this uncertain variable in \\(O(n^{2}\\log n\\ time, where \\(n\\ is the number of vertices in the tree.
Inverse Force Determination on a Small Scale Launch Vehicle Model Using a Dynamic Balance
Ngo, Christina L.; Powell, Jessica M.; Ross, James C.
2017-01-01
A launch vehicle can experience large unsteady aerodynamic forces in the transonic regime that, while usually only lasting for tens of seconds during launch, could be devastating if structural components and electronic hardware are not designed to account for them. These aerodynamic loads are difficult to experimentally measure and even harder to computationally estimate. The current method for estimating buffet loads is through the use of a few hundred unsteady pressure transducers and wind tunnel test. Even with a large number of point measurements, the computed integrated load is not an accurate enough representation of the total load caused by buffeting. This paper discusses an attempt at using a dynamic balance to experimentally determine buffet loads on a generic scale hammer head launch vehicle model tested at NASA Ames Research Center's 11' x 11' transonic wind tunnel. To use a dynamic balance, the structural characteristics of the model needed to be identified so that the natural modal response could be and removed from the aerodynamic forces. A finite element model was created on a simplified version of the model to evaluate the natural modes of the balance flexures, assist in model design, and to compare to experimental data. Several modal tests were conducted on the model in two different configurations to check for non-linearity, and to estimate the dynamic characteristics of the model. The experimental results were used in an inverse force determination technique with a psuedo inverse frequency response function. Due to the non linearity, the model not being axisymmetric, and inconsistent data between the two shake tests from different mounting configuration, it was difficult to create a frequency response matrix that satisfied all input and output conditions for wind tunnel configuration to accurately predict unsteady aerodynamic loads.
Fang, X.; Thompson, R.; Saito, T.; Yokouchi, Y.; Li, S.; Kim, J.; Kim, K.; Park, S.; Graziosi, F.; Stohl, A.
2013-12-01
With a global warming potential of around 22800 over a 100-year time horizon, sulfur hexafluoride (SF6) is one of the greenhouse gases regulated under the Kyoto Protocol. Global SF6 emissions have been increasing since circa the year 2000. The reason for this increase has been inferred to be due to rapidly increasing emissions in developing countries that are not obligated to report their annual emissions to the United Nations Framework Convention on Climate Change, notably China. In this study, SF6 emissions during the period 2006-2012 for China and other East Asian countries were determined using in-situ atmospheric measurements and inverse modeling. We performed various inversion sensitivity tests, which show the largest uncertainties in the a posteriori Chinese emissions are associated with the a priori emissions used and their uncertainty, the station network, as well as the meteorological input data. The overall relative uncertainty of the a posteriori emissions in China is estimated to be 17% in 2008. Based on sensitivity tests, we employed the optimal parameters in our inversion setup and performed yearly inversions for the study period. Inversion results show that the total a posteriori SF6 emissions from China increased from 1420 × 245 Mg/yr in 2006 to 2741 × 472 Mg/yr in 2009 and stabilized thereafter. The rapid increase in emissions reflected a fast increase in SF6 consumption in China, a result also found in bottom-up estimates. The a posteriori emission map shows high emissions concentrated in populated parts of China. During the period 2006-2012, emissions in northwestern and northern China peaked around the year 2009, while emissions in eastern, central and northeastern China grew gradually during almost the whole period. Fluctuating emissions are observed for southwestern China. These regional differences should be caused by changes of provincial SF6 usage and by shifts of usage among different sectors. Fig. 1. Footprint emission sensitivity
Regional-scale geostatistical inverse modeling of North American CO2 fluxes: a synthetic data study
Directory of Open Access Journals (Sweden)
A. M. Michalak
2010-07-01
Full Text Available A series of synthetic data experiments is performed to investigate the ability of a regional atmospheric inversion to estimate grid-scale CO2 fluxes during the growing season over North America. The inversions are performed within a geostatistical framework without the use of any prior flux estimates or auxiliary variables, in order to focus on the atmospheric constraint provided by the nine towers collecting continuous, calibrated CO2 measurements in 2004. Using synthetic measurements and their associated concentration footprints, flux and model-data mismatch covariance parameters are first optimized, and then fluxes and their uncertainties are estimated at three different temporal resolutions. These temporal resolutions, which include a four-day average, a four-day-average diurnal cycle with 3-hourly increments, and 3-hourly fluxes, are chosen to help assess the impact of temporal aggregation errors on the estimated fluxes and covariance parameters. Estimating fluxes at a temporal resolution that can adjust the diurnal variability is found to be critical both for recovering covariance parameters directly from the atmospheric data, and for inferring accurate ecoregion-scale fluxes. Accounting for both spatial and temporal a priori covariance in the flux distribution is also found to be necessary for recovering accurate a posteriori uncertainty bounds on the estimated fluxes. Overall, the results suggest that even a fairly sparse network of 9 towers collecting continuous CO2 measurements across the continent, used with no auxiliary information or prior estimates of the flux distribution in time or space, can be used to infer relatively accurate monthly ecoregion scale CO2 surface fluxes over North America within estimated uncertainty bounds. Simulated random transport error is shown to decrease the quality of flux estimates in under-constrained areas at the ecoregion scale, although the uncertainty bounds remain realistic. While these synthetic
Marine Controlled-Source Electromagnetic 2D Inversion for synthetic models.
Liu, Y.; Li, Y.
2016-12-01
We present a 2D inverse algorithm for frequency domain marine controlled-source electromagnetic (CSEM) data, which is based on the regularized Gauss-Newton approach. As a forward solver, our parallel adaptive finite element forward modeling program is employed. It is a self-adaptive, goal-oriented grid refinement algorithm in which a finite element analysis is performed on a sequence of refined meshes. The mesh refinement process is guided by a dual error estimate weighting to bias refinement towards elements that affect the solution at the EM receiver locations. With the use of the direct solver (MUMPS), we can effectively compute the electromagnetic fields for multi-sources and parametric sensitivities. We also implement the parallel data domain decomposition approach of Key and Ovall (2011), with the goal of being able to compute accurate responses in parallel for complicated models and a full suite of data parameters typical of offshore CSEM surveys. All minimizations are carried out by using the Gauss-Newton algorithm and model perturbations at each iteration step are obtained by using the Inexact Conjugate Gradient iteration method. Synthetic test inversions are presented.
Roemer, R B; Booth, D; Bhavsar, A A; Walter, G H; Terry, L I
2012-12-21
A mathematical model based on conservation of energy has been developed and used to simulate the temperature responses of cones of the Australian cycads Macrozamia lucida and Macrozamia. macleayi during their daily thermogenic cycle. These cones generate diel midday thermogenic temperature increases as large as 12 °C above ambient during their approximately two week pollination period. The cone temperature response model is shown to accurately predict the cones' temperatures over multiple days as based on simulations of experimental results from 28 thermogenic events from 3 different cones, each simulated for either 9 or 10 sequential days. The verified model is then used as the foundation of a new, parameter estimation based technique (termed inverse calorimetry) that estimates the cones' daily metabolic heating rates from temperature measurements alone. The inverse calorimetry technique's predictions of the major features of the cones' thermogenic metabolism compare favorably with the estimates from conventional respirometry (indirect calorimetry). Because the new technique uses only temperature measurements, and does not require measurements of oxygen consumption, it provides a simple, inexpensive and portable complement to conventional respirometry for estimating metabolic heating rates. It thus provides an additional tool to facilitate field and laboratory investigations of the bio-physics of thermogenic plants. Copyright © 2012 Elsevier Ltd. All rights reserved.
A Low-Cost Maximum Power Point Tracking System Based on Neural Network Inverse Model Controller
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Carlos Robles Algarín
2018-01-01
Full Text Available This work presents the design, modeling, and implementation of a neural network inverse model controller for tracking the maximum power point of a photovoltaic (PV module. A nonlinear autoregressive network with exogenous inputs (NARX was implemented in a serial-parallel architecture. The PV module mathematical modeling was developed, a buck converter was designed to operate in the continuous conduction mode with a switching frequency of 20 KHz, and the dynamic neural controller was designed using the Neural Network Toolbox from Matlab/Simulink (MathWorks, Natick, MA, USA, and it was implemented on an open-hardware Arduino Mega board. To obtain the reference signals for the NARX and determine the 65 W PV module behavior, a system made of a 0.8 W PV cell, a temperature sensor, a voltage sensor and a static neural network, was used. To evaluate performance a comparison with the P&O traditional algorithm was done in terms of response time and oscillations around the operating point. Simulation results demonstrated the superiority of neural controller over the P&O. Implementation results showed that approximately the same power is obtained with both controllers, but the P&O controller presents oscillations between 7 W and 10 W, in contrast to the inverse controller, which had oscillations between 1 W and 2 W.
Effects of previous severe exercise on two and three parameter critical power modeling
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Eduardo Kokubun
2007-09-01
Full Text Available The purpose of this study was to apply the two and three-parameter critical power model equations after depletion of a fi xed amount of anaerobic work capacity (AWC, followed by a short rest period. Sixteen subjects underwent: (1 two practice trials for ergometer familiarization to severe exercise; (2 4-5 exercise bouts on different days for the estimation of critical power (CP and AWC using the two and three parameter models; (3 the same procedures as described in stage 2 were repeated after 30 s recovery from 180 s of exercise completed at an intensity that would have elicited exhaustion in around 300 s. The CP2parameter (130-174 W versus 131-170 W and CP3parameter (108 versus 100 W estimated after prior severe exercise followed by a short rest period remained stable compared to the fatigue-free tests. The AWC2parameter was reduced in response to prior severe exercise. The AWC3parameter was not significantly reduced. The correlations between CP2parameter derived from the same equation with and without prior AWC2parameter reduction were strong (r = 0.97-0.99, P ABSTRACT O propósito do presente estudo foi aplicar as equações do modelo de potência crítica de dois e três parâmetros após a depleção de uma quantia fi xa de capacidade de trabalho anaeróbio (AWC, seguido de um período curto de repouso. Dezesseis sujeitos realizaram: (1 duas familiarizações ao exercício severo no cicloergômetro; (2 4-5 exercícios máximos em dias diferentes para a estimativa da CP e AWC por meio dos modelos de dois e três parâmetros; (3 os mesmos procedimentos descritos no #2 foram realizados após 30 s de repouso e de um exercício de 180 s a uma intensidade em que a exaustão provavelmente ocorreria em ~300 s. A CP2parameter (130-174 W versus 131-170 W e CP3parameter (108 versus 100 W estimadas após o exercício prévio severo e seguido por um curto período de repouso permaneceu estável quando comparado às estimativas sem o exercício pr
Pérez-Rial, Sandra; Del Puerto-Nevado, Laura; Girón-Martínez, Alvaro; Terrón-Expósito, Raúl; Díaz-Gil, Juan J; González-Mangado, Nicolás; Peces-Barba, Germán
2014-11-01
Chronic obstructive pulmonary disease (COPD) is an inflammatory lung disease largely associated with cigarette smoke exposure (CSE) and characterized by pulmonary and extrapulmonary manifestations, including systemic inflammation. Liver growth factor (LGF) is an albumin-bilirubin complex with demonstrated antifibrotic, antioxidant, and antihypertensive actions even at extrahepatic sites. We aimed to determine whether short LGF treatment (1.7 μg/mouse ip; 2 times, 2 wk), once the lung damage was established through the chronic CSE, contributes to improvement of the regeneration of damaged lung tissue, reducing systemic inflammation. We studied AKR/J mice, divided into three groups: control (air-exposed), CSE (chronic CSE), and CSE + LGF (LGF-treated CSE mice). We assessed pulmonary function, morphometric data, and levels of various systemic inflammatory markers to test the LGF regenerative capacity in this system. Our results revealed that the lungs of the CSE animals showed pulmonary emphysema and inflammation, characterized by increased lung compliance, enlargement of alveolar airspaces, systemic inflammation (circulating leukocytes and serum TNF-α level), and in vivo lung matrix metalloproteinase activity. LGF treatment was able to reverse all these parameters, decreasing total cell count in bronchoalveolar lavage fluid and T-lymphocyte infiltration in peripheral blood observed in emphysematous mice and reversing the decrease in monocytes observed in chronic CSE mice, and tends to reduce the neutrophil population and serum TNF-α level. In conclusion, LGF treatment normalizes the physiological and morphological parameters and levels of various systemic inflammatory biomarkers in a chronic CSE AKR/J model, which may have important pathophysiological and therapeutic implications for subjects with stable COPD. Copyright © 2014 the American Physiological Society.
An inverse problem approach for structural damage detection - Finite element model refinement
Zimmerman, D. C.; Kaouk, M.
1992-01-01
In this work, a methodology for incorporating measured modal data into an existing refined finite element model is examined with the objective of detecting and locating structural damage. This same algorithm is also useful in terms of finite element model refinement. The algorithm is based on the partial inverse problem, in that only partial spectral information is required. The technique utilizes a symmetric eigenstructure assignment algorithm to perform the partial spectral assignment. Algorithms to enhance mode shape assignability and to preserve sparsity in the updated model are developed. The sparsity preservation is of particular importance when considering damage detection in truss-like structures. Several examples are presented which highlight the key points made within the paper.
Muon anomalous magnetic moment in SUSY B−L model with inverse seesaw
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Shaaban Khalil
2016-12-01
Full Text Available Motivated by the tension between the Higgs mass and muon g−2 in minimal supersymmetric standard model (MSSM, we analyze the muon g−2 in supersymmetric B−L extension of the standard model (BLSSM with inverse seesaw mechanism. In this model, the Higgs mass receives extra important radiative corrections proportional to large neutrino Yukawa coupling. We point out that muon g−2 also gets significant contribution, due to the constructive interferences of light neutralino effects. The light neutralinos are typically the MSSM Bino like and the supersymmetric partner of U(1B−L gauge boson (B˜′-ino. We show that with universal soft supersymmetry breaking terms, the muon g−2 resides within 2σ of the measured value, namely ∼20×10−10, with Higgs mass equal to 125 GeV.
Thin-Sheet Inversion Modeling of Geomagnetic Deep Sounding Data Using MCMC Algorithm
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Hendra Grandis
2013-01-01
Full Text Available The geomagnetic deep sounding (GDS method is one of electromagnetic (EM methods in geophysics that allows the estimation of the subsurface electrical conductivity distribution. This paper presents the inversion modeling of GDS data employing Markov Chain Monte Carlo (MCMC algorithm to evaluate the marginal posterior probability of the model parameters. We used thin-sheet model to represent quasi-3D conductivity variations in the heterogeneous subsurface. The algorithm was applied to invert field GDS data from the zone covering an area that spans from eastern margin of the Bohemian Massif to the West Carpathians in Europe. Conductivity anomalies obtained from this study confirm the well-known large-scale tectonic setting of the area.
Feng, R.; Sharma, S.; Luthi, S.M.; Gisolf, A.
2015-01-01
Previously, Tetyukhina et al. (2014) developed a geological and petrophysical model based on the Book Cliffs outcrops that contained eight lithotypes. For reservoir modelling purposes, this model is judged to be too coarse because in the same lithotype it contains reservoir and non-reservoir
Fang, F.; Pain, C. C.; Gaddard, A. J. H.; de Oliveira, C. R. E.; Piggott, M. D.; Umpleby, A. P.; Copeland, G. J. M.
2003-04-01
There are often uncertain factors in ocean numerical models, e.g. the initial and boundary conditions, parameters. With the introduction of advanced observational techniques, more attention has been given to data assimilation to improve the predictive capabilities of ocean models. The question is how and where best to assimilate the observations for reducing the dependence of solutions on the initial and boundary data and getting a better representation of non-stratified water flows around and over coastal topography. In this investigation, we aim to introduce an adjoint model into the Imperial College Ocean Model (ICOM), which is a 3D nonlinear non-hydrostatic model with mesh adaptivity and optimal Domain Decomposition Method (DDM) parallel solver. By using an unstructured mesh, ICOM can automatically conform to the complicated coastal topography and with mesh adaptivity the resolution can be designed to meet physics demands such as flows in region of high shear and flow separation at coastlines. In the initial stage of this investigation, we discuss various adjoint methods and their consistence. To accelerate the convergence of the gradient calculation and reduce the memory requirement, the numerical techniques: Nonlinear Conjugate Gradient and Check Pointing are introduced. We then apply the adjoint method to 1D nonlinear shallow water and 2D coastal flow past a headland with the inversion of both boundary and initial conditions. We give an initial insight to (1) Effect of data information to be inverted; (2) Role of the nonlinear terms in the inversion; (3) Possibility of adopting non-consistent discretization schemes in the forward and backward adjoint models; (4) Effect of various boundary conditions, e.g. uniform flow and wave/tidal flow.
Directory of Open Access Journals (Sweden)
Mac Sisson
2016-11-01
Full Text Available Poquoson River is a tidal coastal embayment located along the Western Shore of the Chesapeake Bay about 4 km south of the York River mouth in the City of Poquoson and in York County, Virginia. Its drainage area has diversified land uses, including high densities of residence, agricultural, salt marsh land uses, as well as a National Wildlife Refuge. This embayment experiences elevated bacterial concentration due to excess bacterial inputs from storm water runoff, nonpoint sources, and wash off from marshes due to tide and wind-induced set-up and set-down. Bacteria can also grow in the marsh and small tributaries. It is difficult to use a traditional watershed model to simulate bacterial loading, especially in this low-lying marsh area with abundant wildlife, while runoff is not solely driven by precipitation. An inverse approach is introduced to estimate loading from unknown sources based on observations in the embayment. The estimated loadings were combined with loadings estimated from different sources (human, wildlife, agriculture, pets, etc. and input to the watershed model. The watershed model simulated long-term flow and bacterial loading and discharged to a three-dimensional transport model driven by tide, wind, and freshwater discharge. The transport model efficiently simulates the transport and fate of the bacterial concentration in the embayment and is capable of determining the loading reduction needed to improve the water quality condition of the embayment. Combining inverse, watershed, and transport models is a sound approach for simulating bacterial transport correctly in the coastal embayment with complex unknown bacterial sources, which are not solely driven by precipitation.
Houweling, S.; Pandey, S.; Segers, A.
2017-12-01
Methane is regarded as a suitable target for short-term climate mitigation, because of its relatively short atmospheric residence time compared to carbon dioxide and other long-lived greenhouse gases. However, to build climate policy on methane is complicated because of the uncertainties in its emission budget, reflected in the difficulty to predict its global growth rate. Several different and conflicting scenarios have been proposed in high profile journals to explain its recent evolution in the atmosphere. Since the early 2000s atmospheric methane is being measured by Earth orbiting satellites. Missions such as SCIAMACHY and GOSAT have largely increased the number of atmospheric methane measurements that are available for the quantification its emissions using inverse modelling techniques. In this presentation, we address the question what has been the role of satellite data in the discussion about the causes of the varying growth rate of methane, and what are the remaining limitations. This is the time when space borne remote sensing of methane is transitioning from exploratory scientific missions to monitoring missions, starting with the preoperational mission S5p TROPOMI to be launched in September 2017. In the meantime, also inverse modelling techniques are prepared for operational use in support of COP21 agreement to reduce greenhouse gas emissions. These developments bring new opportunities and challenges, which will be discussed.
Directory of Open Access Journals (Sweden)
Patrick Piprek
2018-02-01
Full Text Available This paper presents an approach to model a ski jumper as a multi-body system for an optimal control application. The modeling is based on the constrained Newton-Euler-Equations. Within this paper the complete multi-body modeling methodology as well as the musculoskeletal modeling is considered. For the musculoskeletal modeling and its incorporation in the optimization model, we choose a nonlinear dynamic inversion control approach. This approach uses the muscle models as nonlinear reference models and links them to the ski jumper movement by a control law. This strategy yields a linearized input-output behavior, which makes the optimal control problem easier to solve. The resulting model of the ski jumper can then be used for trajectory optimization whose results are compared to literature jumps. Ultimately, this enables the jumper to get a very detailed feedback of the flight. To achieve the maximal jump length, exact positioning of his body with respect to the air can be displayed.
Energy Technology Data Exchange (ETDEWEB)
Akhil Datta-Gupta
2008-03-31
Streamline-based assisted and automatic history matching techniques have shown great potential in reconciling high resolution geologic models to production data. However, a major drawback of these approaches has been incompressibility or slight compressibility assumptions that have limited applications to two-phase water-oil displacements only. We propose an approach to history matching three-phase flow using a novel compressible streamline formulation and streamline-derived analytic sensitivities. First, we utilize a generalized streamline model to account for compressible flow by introducing an 'effective density' of total fluids along streamlines. Second, we analytically compute parameter sensitivities that define the relationship between the reservoir properties and the production response, viz. water-cut and gas/oil ratio (GOR). These sensitivities are an integral part of history matching, and streamline models permit efficient computation of these sensitivities through a single flow simulation. We calibrate geologic models to production data by matching the water-cut and gas/oil ratio using our previously proposed generalized travel time inversion (GTTI) technique. For field applications, however, the highly non-monotonic profile of the gas/oil ratio data often presents a challenge to this technique. In this work we present a transformation of the field production data that makes it more amenable to GTTI. Further, we generalize the approach to incorporate bottom-hole flowing pressure during three-phase history matching. We examine the practical feasibility of the method using a field-scale synthetic example (SPE-9 comparative study) and a field application. Recently Ensemble Kalman Filtering (EnKF) has gained increased attention for history matching and continuous reservoir model updating using data from permanent downhole sensors. It is a sequential Monte-Carlo approach that works with an ensemble of reservoir models. Specifically, the method
Sulfur hexafluoride (SF6) emissions in East Asia determined by inverse modeling
Fang, X.; Thompson, R. L.; Saito, T.; Yokouchi, Y.; Kim, J.; Li, S.; Kim, K. R.; Park, S.; Graziosi, F.; Stohl, A.
2014-05-01
Sulfur hexafluoride (SF6) has a global warming potential of around 22 800 over a 100-year time horizon and is one of the greenhouse gases regulated under the Kyoto Protocol. Around the year 2000 there was a reversal in the global SF6 emission trend, from a decreasing to an increasing trend, which was likely caused by increasing emissions in countries that are not obligated to report their annual emissions to the United Nations Framework Convention on Climate Change. In this study, SF6 emissions during the period 2006-2012 for all East Asian countries - including Mongolia, China, Taiwan, North Korea, South Korea and Japan - were determined by using inverse modeling and in situ atmospheric measurements. We found that the most important sources of uncertainty associated with these inversions are related to the choice of a priori emissions and their assumed uncertainty, the station network as well as the meteorological input data. Much lower uncertainties are due to seasonal variability in the emissions, inversion geometry and resolution, and the measurement calibration scale. Based on the results of these sensitivity tests, we estimate that the total SF6 emission in East Asia increased rapidly from 2404 ± 325 Mg yr-1 in 2006 to 3787 ± 512 Mg yr-1 in 2009 and stabilized thereafter. China contributed 60-72% to the total East Asian emission for the different years, followed by South Korea (8-16%), Japan (5-16%) and Taiwan (4-7%), while the contributions from North Korea and Mongolia together were less than 3% of the total. The per capita SF6 emissions are highest in South Korea and Taiwan, while the per capita emissions for China, North Korea and Japan are close to global average. During the period 2006-2012, emissions from China and from South Korea increased, while emissions from Taiwan and Japan decreased overall.
A model following inverse controller with adaptive compensation for General Aviation aircraft
Bruner, Hugh S.
The theory for an adaptive inverse flight controller, suitable for use on General Aviation aircraft, is developed in this research. The objectives of this controller are to separate the normally coupled modes of the basic aircraft and thereby permit direct control of airspeed and flight-path angle, meet prescribed performance characteristics as defined by damping ratio and natural frequency, adapt to uncertainties in the physical plant, and be computationally efficient. The three basic elements of the controller are a linear prefilter, an inverse transfer function, and an adaptive neural network compensator. The linear prefilter shapes accelerations required of the overall system in order to achieve the desired system performance characteristics. The inverse transfer function is used to compute the aircraft control inputs required to achieve the necessary accelerations. The adaptive neural network compensator is used to compensate for modeling errors during design or real-time changes in the physical plant. This architecture is patterned after the work of Calise, but differs by not requiring dynamic feedback of the state variables. The controller is coded in ANSI C and integrated with a simulation of a typical General Aviation aircraft. Twenty-three cases are simulated to prove that the objectives for the controller are met. Among these cases are simulated stability and controllability failures in the physical plant, as well as several simulated failures of the neural network. With the exception of some bounded speed-tracking error, the controller is capable of continued flight with any foreseeable failure of the neural network. Recommendations are provided for follow-on investigations by other researchers.
Energy Technology Data Exchange (ETDEWEB)
Li, Weixuan; Lin, Guang; Li, Bing
2016-09-01
A well-known challenge in uncertainty quantification (UQ) is the "curse of dimensionality". However, many high-dimensional UQ problems are essentially low-dimensional, because the randomness of the quantity of interest (QoI) is caused only by uncertain parameters varying within a low-dimensional subspace, known as the sufficient dimension reduction (SDR) subspace. Motivated by this observation, we propose and demonstrate in this paper an inverse regression-based UQ approach (IRUQ) for high-dimensional problems. Specifically, we use an inverse regression procedure to estimate the SDR subspace and then convert the original problem to a low-dimensional one, which can be efficiently solved by building a response surface model such as a polynomial chaos expansion. The novelty and advantages of the proposed approach is seen in its computational efficiency and practicality. Comparing with Monte Carlo, the traditionally preferred approach for high-dimensional UQ, IRUQ with a comparable cost generally gives much more accurate solutions even for high-dimensional problems, and even when the dimension reduction is not exactly sufficient. Theoretically, IRUQ is proved to converge twice as fast as the approach it uses seeking the SDR subspace. For example, while a sliced inverse regression method converges to the SDR subspace at the rate of $O(n^{-1/2})$, the corresponding IRUQ converges at $O(n^{-1})$. IRUQ also provides several desired conveniences in practice. It is non-intrusive, requiring only a simulator to generate realizations of the QoI, and there is no need to compute the high-dimensional gradient of the QoI. Finally, error bars can be derived for the estimation results reported by IRUQ.
Moisan, John R.; Moisan, Tiffany A. H.; Linkswiler, Matthew A.
2011-01-01
Phytoplankton absorption spectra and High-Performance Liquid Chromatography (HPLC) pigment observations from the Eastern U.S. and global observations from NASA's SeaBASS archive are used in a linear inverse calculation to extract pigment-specific absorption spectra. Using these pigment-specific absorption spectra to reconstruct the phytoplankton absorption spectra results in high correlations at all visible wavelengths (r(sup 2) from 0.83 to 0.98), and linear regressions (slopes ranging from 0.8 to 1.1). Higher correlations (r(sup 2) from 0.75 to 1.00) are obtained in the visible portion of the spectra when the total phytoplankton absorption spectra are unpackaged by multiplying the entire spectra by a factor that sets the total absorption at 675 nm to that expected from absorption spectra reconstruction using measured pigment concentrations and laboratory-derived pigment-specific absorption spectra. The derived pigment-specific absorption spectra were further used with the total phytoplankton absorption spectra in a second linear inverse calculation to estimate the various phytoplankton HPLC pigments. A comparison between the estimated and measured pigment concentrations for the 18 pigment fields showed good correlations (r(sup 2) greater than 0.5) for 7 pigments and very good correlations (r(sup 2) greater than 0.7) for chlorophyll a and fucoxanthin. Higher correlations result when the analysis is carried out at more local geographic scales. The ability to estimate phytoplankton pigments using pigment-specific absorption spectra is critical for using hyperspectral inverse models to retrieve phytoplankton pigment concentrations and other Inherent Optical Properties (IOPs) from passive remote sensing observations.
Cleator, Sean; Harrison, Sandy P.; Roulstone, Ian; Nichols, Nancy K.; Prentice, Iain Colin
2017-04-01
Site-based pollen records have been used to provide quantitative reconstructions of Last Glacial Maximum (LGM) climates, but there are too few such records to provide continuous climate fields for the evaluation of climate model simulations. Furthermore, many of the reconstructions were made using modern-analogue techniques, which do not account for the direct impact of CO2 on water-use efficiency and therefore reconstruct considerably drier conditions under low CO2 at the LGM than indicated by other sources of information. We have shown that it is possible to correct analogue-based moisture reconstructions for this effect by inverting a simple light-use efficiency model of productivity, based on the principle that the rate of water loss per unit carbon gain of a plant is the same under conditions of the true moisture, palaeotemperature and palaeo CO2 concentration as under reconstructed moisture, modern CO2 concentration and modern temperature (Prentice et al., 2016). In this study, we use data from the Bartlein el al. (2011) dataset, which provides reconstructions of one or more of six climate variables (mean annual temperature, mean temperature of the warmest and coldest months, the length of the growing seasons, mean annual precipitation, and the ratio of actual to potential evapotranspiration) at individual LGM sites. We use the SPLASH water-balance model to derive a moisture index (MI) at each site from mean annual precipitation and monthly values of sunshine fraction and average temperature, and correct this MI using the Prentice et al. (2016) inversion approach. We then use a three-dimensional variational (3D-Var) data assimilation scheme with the SPLASH model and Prentice et al. (2016) inversion approach to derive reconstructions of all six climate variables at each site, using the Bartlein et al. (2011) data set as a target. We use two alternative background climate states (or priors): modern climate derived from the CRU CL v2.0 data set (New et al., 2002
A Variance Distribution Model of Surface EMG Signals Based on Inverse Gamma Distribution.
Hayashi, Hideaki; Furui, Akira; Kurita, Yuichi; Tsuji, Toshio
2017-11-01
Objective: This paper describes the formulation of a surface electromyogram (EMG) model capable of representing the variance distribution of EMG signals. Methods: In the model, EMG signals are handled based on a Gaussian white noise process with a mean of zero for each variance value. EMG signal variance is taken as a random variable that follows inverse gamma distribution, allowing the representation of noise superimposed onto this variance. Variance distribution estimation based on marginal likelihood maximization is also outlined in this paper. The procedure can be approximated using rectified and smoothed EMG signals, thereby allowing the determination of distribution parameters in real time at low computational cost. Results: A simulation experiment was performed to evaluate the accuracy of distribution estimation using artificially generated EMG signals, with results demonstrating that the proposed model's accuracy is higher than that of maximum-likelihood-based estimation. Analysis of variance distribution using real EMG data also suggested a relationship between variance distribution and signal-dependent noise. Conclusion: The study reported here was conducted to examine the performance of a proposed surface EMG model capable of representing variance distribution and a related distribution parameter estimation method. Experiments using artificial and real EMG data demonstrated the validity of the model. Significance: Variance distribution estimated using the proposed model exhibits potential in the estimation of muscle force. Objective: This paper describes the formulation of a surface electromyogram (EMG) model capable of representing the variance distribution of EMG signals. Methods: In the model, EMG signals are handled based on a Gaussian white noise process with a mean of zero for each variance value. EMG signal variance is taken as a random variable that follows inverse gamma distribution, allowing the representation of noise superimposed onto this
Real-time inverse-model analysis and control on data collection
Vesselinov, V. V.; Robinson, B. A.; Vrugt, J. A.; Zyvoloski, G. A.
2005-12-01
Sophisticated numerical models are commonly used to simulate fluid and chemical flow in the subsurface. The science of flow in porous media is composed of general physical principles (transferable knowledge) and site-specific details. All sites are unique, so even if the physics is well understood, we need detailed, site-specific information to develop a model for each site (subsurface heterogeneity, initial and boundary conditions, etc.). In this respect, at each new site, we "start over". The most time- and resource-consuming step in reducing predictive uncertainty bounds in subsurface systems is the process of uncovering the site-specific details. The current paradigm is to perform a lengthy reconnaissance phase to understand the site, followed by additional data collection and modeling to synthesize the information. Model development methods are slow and labor-intensive for complex sites; therefore, model results generally lag behind the data collection by a considerable length of time. This delay limits the usefulness of the model as a tool to guide data collection: any given iteration of the model is out of date by the time it is completed. The whole process is unacceptably protracted in an era in which, for example, in the U.S. alone we may ultimately need hundreds of sites to implement CO2 geologic sequestration. Our technical capabilities for efficiently collecting and organizing subsurface data have progressed recently with the advent of modern data collection and transmission systems. However, our ability to process this information in the form of numerical models has lagged behind. We propose a new paradigm for the development of complex subsurface flow and transport models in which the inverse analysis is performed in real time, simultaneously with the data collection. Furthermore, we propose to use the inverse model to control the data collection or the operating conditions of an extraction system in real time. This is extremely important because post
Fréour , Sylvain; Gloaguen , David; François , Marc; Guillén , Ronald
2006-01-01
International audience; The scope of this work is the determination of the coefficients of thermal expansion of the Ti-17 beta-phase. A rigorous inverse thermo-elastic self-consistent scale transition inicro-mechanical model extended to multi-phase materials was used. The experimental data required for the application of the inverse method were obtained from both the available literature and especially dedicated X-ray diffraction lattice strain measurements performed on the studied (alpha + b...
Directory of Open Access Journals (Sweden)
M. A. Hussain
2014-01-01
Full Text Available This paper discusses the discrete-time stability analysis of a neural network inverse model control strategy for a relative order two nonlinear system. The analysis is done by representing the closed loop system in state space format and then analyzing the time derivative of the state trajectory using Lyapunov’s direct method. The analysis shows that the tracking output error of the states is confined to a ball in the neighborhood of the equilibrium point where the size of the ball is partly dependent on the accuracy of the neural network model acting as the controller. Simulation studies on the two-tank-in-series system were done to complement the stability analysis and to demonstrate some salient results of the study.
Paluchowski, Lukasz A.; Bjorgan, Asgeir; Nordgaard, Hâvard B.; Randeberg, Lise L.
2016-02-01
Hyperspectral imagery opens a new perspective for biomedical diagnostics and tissue characterization. High spectral resolution can give insight into optical properties of the skin tissue. However, at the same time the amount of collected data represents a challenge when it comes to decomposition into clusters and extraction of useful diagnostic information. In this study spectral-spatial classification and inverse diffusion modeling were employed to hyperspectral images obtained from a porcine burn model using a hyperspectral push-broom camera. The implemented method takes advantage of spatial and spectral information simultaneously, and provides information about the average optical properties within each cluster. The implemented algorithm allows mapping spectral and spatial heterogeneity of the burn injury as well as dynamic changes of spectral properties within the burn area. The combination of statistical and physics informed tools allowed for initial separation of different burn wounds and further detailed characterization of the injuries in short post-injury time.
Naturalness and lepton number/flavor violation in inverse seesaw models
Energy Technology Data Exchange (ETDEWEB)
Haba, Naoyuki [Graduate School of Science and Engineering, Shimane University,1060, Nishikawatsu, Matsue, Shimane (Japan); Ishida, Hiroyuki [Graduate School of Science and Engineering, Shimane University,1060, Nishikawatsu, Matsue, Shimane (Japan); Physics Division, National Center for Theoretical Sciences,101, Section 2 Kuang Fu Road, Hsinchu, 300 Taiwan (China); Yamaguchi, Yuya [Graduate School of Science and Engineering, Shimane University,1060, Nishikawatsu, Matsue, Shimane (Japan); Department of Physics, Faculty of Science, Hokkaido University,Kita 9 Nishi 8, Kita-ku, Sapporo, Hokkaido (Japan)
2016-11-02
We introduce three right-handed neutrinos and three sterile neutrinos, and consider an inverse seesaw mechanism for neutrino mass generation. From naturalness point of view, their Majorana masses should be small, while it induces a large neutrino Yukawa coupling. Then, a neutrinoless double beta decay rate can be enhanced, and a sizable Higgs mass correction is inevitable. We find that the enhancement rate can be more than ten times compared with a standard prediction from light neutrino contribution alone, and an analytic form of heavy neutrino contributions to the Higgs mass correction. In addition, we numerically analyze the model, and find almost all parameter space of the model can be complementarily searched by future experiments of neutrinoless double beta decay and μ→e conversion.
On the critical behavior of the inverse susceptibility of a model of structural phase transitions
International Nuclear Information System (INIS)
Pisanova, E.S.; Ivanov, S.I.
2013-01-01
An exactly solvable lattice model describing structural phase transitions in an anharmonic crystal with long-range interaction is considered in the neighborhoods of the quantum and classical critical points at the corresponding upper critical dimensions. In a broader neighborhood of the critical region the inverse susceptibility of the model is exactly calculated in terms of the Lambert W-function and graphically presented as a function of the deviation from the critical point and the upper critical dimension. For quantum and classical systems with real physical dimensions (chains, thin layers and three-dimensional systems) the exact results are compared with the asymptotic ones on the basis of some numerical data for their ratio. Relative errors are also provided
Smith, James A.
1992-01-01
The inversion of the leaf area index (LAI) canopy parameter from optical spectral reflectance measurements is obtained using a backpropagation artificial neural network trained using input-output pairs generated by a multiple scattering reflectance model. The problem of LAI estimation over sparse canopies (LAI 1000 percent for low LAI. Minimization methods applied to merit functions constructed from differences between measured reflectances and predicted reflectances using multiple-scattering models are unacceptably sensitive to a good initial guess for the desired parameter. In contrast, the neural network reported generally yields absolute percentage errors of <30 percent when weighting coefficients trained on one soil type were applied to predicted canopy reflectance at a different soil background.
Optimization-Based Inverse Identification of the Parameters of a Concrete Cap Material Model
Král, Petr; Hokeš, Filip; Hušek, Martin; Kala, Jiří; Hradil, Petr
2017-10-01
Issues concerning the advanced numerical analysis of concrete building structures in sophisticated computing systems currently require the involvement of nonlinear mechanics tools. The efforts to design safer, more durable and mainly more economically efficient concrete structures are supported via the use of advanced nonlinear concrete material models and the geometrically nonlinear approach. The application of nonlinear mechanics tools undoubtedly presents another step towards the approximation of the real behaviour of concrete building structures within the framework of computer numerical simulations. However, the success rate of this application depends on having a perfect understanding of the behaviour of the concrete material models used and having a perfect understanding of the used material model parameters meaning. The effective application of nonlinear concrete material models within computer simulations often becomes very problematic because these material models very often contain parameters (material constants) whose values are difficult to obtain. However, getting of the correct values of material parameters is very important to ensure proper function of a concrete material model used. Today, one possibility, which permits successful solution of the mentioned problem, is the use of optimization algorithms for the purpose of the optimization-based inverse material parameter identification. Parameter identification goes hand in hand with experimental investigation while it trying to find parameter values of the used material model so that the resulting data obtained from the computer simulation will best approximate the experimental data. This paper is focused on the optimization-based inverse identification of the parameters of a concrete cap material model which is known under the name the Continuous Surface Cap Model. Within this paper, material parameters of the model are identified on the basis of interaction between nonlinear computer simulations
Inverse photoemission of uranium oxides
International Nuclear Information System (INIS)
Roussel, P.; Morrall, P.; Tull, S.J.
2009-01-01
Understanding the itinerant-localised bonding role of the 5f electrons in the light actinides will afford an insight into their unusual physical and chemical properties. In recent years, the combination of core and valance band electron spectroscopies with theoretic modelling have already made significant progress in this area. However, information of the unoccupied density of states is still scarce. When compared to the forward photoemission techniques, measurements of the unoccupied states suffer from significantly less sensitivity and lower resolution. In this paper, we report on our experimental apparatus, which is designed to measure the inverse photoemission spectra of the light actinides. Inverse photoemission spectra of UO 2 and UO 2.2 along with the corresponding core and valance electron spectra are presented in this paper. UO 2 has been reported previously, although through its inclusion here it allows us to compare and contrast results from our experimental apparatus to the previous Bremsstrahlung Isochromat Spectroscopy and Inverse Photoemission Spectroscopy investigations
Lv, Jie; Yan, Zhenguo; Wei, Jingyi
2014-11-01
Accurate retrieval of crop chlorophyll content is of great importance for crop growth monitoring, crop stress situations, and the crop yield estimation. This study focused on retrieval of rice chlorophyll content from data through radiative transfer model inversion. A field campaign was carried out in September 2009 in the farmland of ChangChun, Jinlin province, China. A different set of 10 sites of the same species were used in 2009 for validation of methodologies. Reflectance of rice was collected using ASD field spectrometer for the solar reflective wavelengths (350-2500 nm), chlorophyll content of rice was measured by SPAD-502 chlorophyll meter. Each sample sites was recorded with a Global Position System (GPS).Firstly, the PROSPECT radiative transfer model was inverted using support vector machine in order to link rice spectrum and the corresponding chlorophyll content. Secondly, genetic algorithms were adopted to select parameters of support vector machine, then support vector machine was trained the training data set, in order to establish leaf chlorophyll content estimation model. Thirdly, a validation data set was established based on hyperspectral data, and the leaf chlorophyll content estimation model was applied to the validation data set to estimate leaf chlorophyll content of rice in the research area. Finally, the outcome of the inversion was evaluated using the calculated R2 and RMSE values with the field measurements. The results of the study highlight the significance of support vector machine in estimating leaf chlorophyll content of rice. Future research will concentrated on the view of the definition of satellite images and the selection of the best measurement configuration for accurate estimation of rice characteristics.
Carroll, T. A.; Kopf, M.; Strassmeier, K. G.
2008-09-01
Context: The major challenges for a fully polarized radiative transfer driven approach to Zeeman-Doppler imaging are still the enormous computational requirements. In every cycle of the iterative interplay between the forward process (spectral synthesis) and the inverse process (derivative based optimization) the Stokes profile synthesis requires several thousand evaluations of the polarized radiative transfer equation for a given stellar surface model. Aims: To cope with these computational demands and to allow for the incorporation of a full Stokes profile synthesis into Doppler- and Zeeman-Doppler imaging applications as well as into large scale solar Stokes profile inversions, we present a novel fast and accurate synthesis method for calculating local Stokes profiles. Methods: Our approach is based on artificial neural network models, which we use to approximate the complex non-linear mapping between the most important atmospheric parameters and the corresponding Stokes profiles. A number of specialized artificial neural networks, are used to model the functional relation between the model atmosphere, magnetic field strength, field inclination, and field azimuth, on one hand and the individual components (I,Q,U,V) of the Stokes profiles, on the other hand. Results: We performed an extensive statistical evaluation and show that our new approach yields accurate local as well as disk-integrated Stokes profiles over a wide range of atmospheric conditions. The mean rms errors for the Stokes I and V profiles are well below 0.2% compared to the exact numerical solution. Errors for Stokes Q and U are in the range of 1%. Our approach does not only offer an accurate approximation to the LTE polarized radiative transfer it, moreover, accelerates the synthesis by a factor of more than 1000.
Adaptive forward-inverse modeling of reservoir fluids away from wellbores; TOPICAL
International Nuclear Information System (INIS)
Ziagos, J P; Gelinas, R J; Doss, S K; Nelson, R G
1999-01-01
This Final Report contains the deliverables of the DeepLook Phase I project entitled, ''Adaptive Forward-Inverse Modeling of Reservoir Fluids Away from Wellbores''. The deliverables are: (i) a description of 2-D test problem results, analyses, and technical descriptions of the techniques used, (ii) a listing of program setup commands that construct and execute the codes for selected test problems (these commands are in mathematical terminology, which reinforces technical descriptions in the text), and (iii) an evaluation and recommendation regarding continuance of this project, including considerations of possible extensions to 3-D codes, additional technical scope, and budget for the out-years. The far-market objective in this project is to develop advanced technologies that can help locate and enhance the recovery of oil from heterogeneous rock formations. The specific technical objective in Phase I was to develop proof-of-concept of new forward and inverse (F-I) modeling techniques[Gelinas et al, 1998] that seek to enhance estimates (images) of formation permeability distributions and fluid motion away from wellbore volumes. This goes to the heart of improving industry's ability to jointly image reservoir permeability and flow predictions of trapped and recovered oil versus time. The estimation of formation permeability away from borehole measurements is an ''inverse'' problem. It is an inseparable part of modeling fluid flows throughout the reservoir in efforts to increase the efficiency of oil recovery at minimum cost. Classic issues of non-uniqueness, mathematical instability, noise effects, and inadequate numerical solution techniques have historically impeded progress in reservoir parameter estimations. Because information pertaining to fluid and rock properties is always sampled sparsely by wellbore measurements, a successful method for interpolating permeability and fluid data between the measurements must be: (i) physics-based, (ii) conditioned by signal
Energy Technology Data Exchange (ETDEWEB)
Mukhopadhyay, S.; Tsang, Y.; Finsterle, S.
2009-01-15
A simple conceptual model has been recently developed for analyzing pressure and temperature data from flowing fluid temperature logging (FFTL) in unsaturated fractured rock. Using this conceptual model, we developed an analytical solution for FFTL pressure response, and a semianalytical solution for FFTL temperature response. We also proposed a method for estimating fracture permeability from FFTL temperature data. The conceptual model was based on some simplifying assumptions, particularly that a single-phase airflow model was used. In this paper, we develop a more comprehensive numerical model of multiphase flow and heat transfer associated with FFTL. Using this numerical model, we perform a number of forward simulations to determine the parameters that have the strongest influence on the pressure and temperature response from FFTL. We then use the iTOUGH2 optimization code to estimate these most sensitive parameters through inverse modeling and to quantify the uncertainties associated with these estimated parameters. We conclude that FFTL can be utilized to determine permeability, porosity, and thermal conductivity of the fracture rock. Two other parameters, which are not properties of the fractured rock, have strong influence on FFTL response. These are pressure and temperature in the borehole that were at equilibrium with the fractured rock formation at the beginning of FFTL. We illustrate how these parameters can also be estimated from FFTL data.
International Nuclear Information System (INIS)
Zhang Jin-Peng; Wu Zhen-Sen; Zhao Zhen-Wei; Zhang Yu-Sheng; Wang Bo
2012-01-01
The maritime tropospheric duct is a low-altitude anomalous refractivity structure over the ocean surface, and it can significantly affect the performance of many shore-based/shipboard radar and communication systems. We propose the idea that maritime tropospheric ducts can be retrieved from ocean forward-scattered low-elevation global positioning system (GPS) signals. Retrieval is accomplished by matching the measured power patterns of the signals to those predicted by the forward propagation model as a function of the modified refractivity profile. On the basis of a parabolic equation method and bistatic radar equation, we develop such a forward model for computing the trapped propagation characteristics of an ocean forward-scattered GPS signal within a tropospheric duct. A new GPS scattering initial field is defined for this model to start the propagation modeling. A preliminary test on the performance of this model is conducted using measured data obtained from a 2009-experiment in the South China Sea. Results demonstrate that this model can predict GPS propagation characteristics within maritime tropospheric ducts and serve as a forward model for duct inversion
Lura, Derek; Wernke, Matthew; Alqasemi, Redwan; Carey, Stephanie; Dubey, Rajiv
2012-01-01
This paper presents the probability density based gradient projection (GP) of the null space of the Jacobian for a 25 degree of freedom bilateral robotic human body model (RHBM). This method was used to predict the inverse kinematics of the RHBM and maximize the similarity between predicted inverse kinematic poses and recorded data of 10 subjects performing activities of daily living. The density function was created for discrete increments of the workspace. The number of increments in each direction (x, y, and z) was varied from 1 to 20. Performance of the method was evaluated by finding the root mean squared (RMS) of the difference between the predicted joint angles relative to the joint angles recorded from motion capture. The amount of data included in the creation of the probability density function was varied from 1 to 10 subjects, creating sets of for subjects included and excluded from the density function. The performance of the GP method for subjects included and excluded from the density function was evaluated to test the robustness of the method. Accuracy of the GP method varied with amount of incremental division of the workspace, increasing the number of increments decreased the RMS error of the method, with the error of average RMS error of included subjects ranging from 7.7° to 3.7°. However increasing the number of increments also decreased the robustness of the method.
[Bare Soil Moisture Inversion Model Based on Visible-Shortwave Infrared Reflectance].
Zheng, Xiao-po; Sun, Yue-jun; Qin, Qi-ming; Ren, Hua-zhong; Gao, Zhong-ling; Wu, Ling; Meng, Qing-ye; Wang, Jin-liang; Wang, Jian-hua
2015-08-01
Soil is the loose solum of land surface that can support plants. It consists of minerals, organics, atmosphere, moisture, microbes, et al. Among its complex compositions, soil moisture varies greatly. Therefore, the fast and accurate inversion of soil moisture by using remote sensing is very crucial. In order to reduce the influence of soil type on the retrieval of soil moisture, this paper proposed a normalized spectral slope and absorption index named NSSAI to estimate soil moisture. The modeling of the new index contains several key steps: Firstly, soil samples with different moisture level were artificially prepared, and soil reflectance spectra was consequently measured using spectroradiometer produced by ASD Company. Secondly, the moisture absorption spectral feature located at shortwave wavelengths and the spectral slope of visible wavelengths were calculated after analyzing the regular spectral feature change patterns of different soil at different moisture conditions. Then advantages of the two features at reducing soil types' effects was synthesized to build the NSSAI. Thirdly, a linear relationship between NSSAI and soil moisture was established. The result showed that NSSAI worked better (correlation coefficient is 0.93) than most of other traditional methods in soil moisture extraction. It can weaken the influences caused by soil types at different moisture levels and improve the bare soil moisture inversion accuracy.
Eddy Current Inversion Models for Estimating Dimensions of Defects in Multilayered Structures
Directory of Open Access Journals (Sweden)
Bo Ye
2014-01-01
Full Text Available In eddy current nondestructive evaluation, one of the principal challenges is to determine the dimensions of defects in multilayered structures from the measured signals. It is a typical inverse problem which is generally considered to be nonlinear and ill-posed. In the paper, two effective approaches have been proposed to estimate the defect dimensions. The first one is a partial least squares (PLS regression method. The second one is a kernel partial least squares (KPLS regression method. The experimental research is carried out. In experiments, the eddy current signals responding to magnetic field changes are detected by a giant magnetoresistive (GMR sensor and preprocessed for noise elimination using a wavelet packet analysis (WPA method. Then, the proposed two approaches are used to construct the inversion models of defect dimension estimation. Finally, the estimation results are analyzed. The performance comparison between the proposed two approaches and the artificial neural network (ANN method is presented. The comparison results demonstrate the feasibility and validity of the proposed two methods. Between them, the KPLS regression method gives a better prediction performance than the PLS regression method at present.
Energy Technology Data Exchange (ETDEWEB)
Clarke, R.
1997-10-27
Reflection tomography can determine velocity models containing lateral velocity variations and reflectors of arbitrary shapes; in order to access the kinematic data, a 3D zero offset approach to the SMART (Sequential Migration Aided Reflection Tomography) method, an original method of migration velocity analysis, is adopted. The approach involves interpreting kinematic data in the post-stack depth migrated cube and then de-migrating the horizons by two-point ray-tracing. A fast and robust two-point ray-tracer is developed, which can recover multi-valued kinematic data from complex geological structures. An original formulation for 3D reflection tomography is proposed, which can reliably take into account multivalued travel times
Improving Inverse Dynamics Accuracy in a Planar Walking Model Based on Stable Reference Point
Directory of Open Access Journals (Sweden)
Alaa Abdulrahman
2014-01-01
Full Text Available Physiologically and biomechanically, the human body represents a complicated system with an abundance of degrees of freedom (DOF. When developing mathematical representations of the body, a researcher has to decide on how many of those DOF to include in the model. Though accuracy can be enhanced at the cost of complexity by including more DOF, their necessity must be rigorously examined. In this study a planar seven-segment human body walking model with single DOF joints was developed. A reference point was added to the model to track the body’s global position while moving. Due to the kinematic instability of the pelvis, the top of the head was selected as the reference point, which also assimilates the vestibular sensor position. Inverse dynamics methods were used to formulate and solve the equations of motion based on Newton-Euler formulae. The torques and ground reaction forces generated by the planar model during a regular gait cycle were compared with similar results from a more complex three-dimensional OpenSim model with muscles, which resulted in correlation errors in the range of 0.9–0.98. The close comparison between the two torque outputs supports the use of planar models in gait studies.
Energy Technology Data Exchange (ETDEWEB)
Finsterle, Stefan; Kiryukhin, A.V.; Asaulova, N.P.; Finsterle, S.
2008-04-01
A three-dimensional numerical model of the Pauzhetsky geothermal field has been developed based on a conceptual hydrogeological model of the system. It extends over a 13.6-km2 area and includes three layers: (1) a base layer with inflow; (2) a geothermal reservoir; and (3) an upper layer with discharge and recharge/infiltration areas. Using the computer program iTOUGH2 (Finsterle, 2004), the model is calibrated to a total of 13,675 calibration points, combining natural-state and 1960-2006 exploitation data. The principal model parameters identified and estimated by inverse modeling include the fracture permeability and fracture porosity of the geothermal reservoir, the initial natural upflow rate, the base-layer porosity, and the permeabilities of the infiltration zones. Heat and mass balances derived from the calibrated model helped identify the sources of the geothermal reserves in the field. With the addition of five makeup wells, simulation forecasts for the 2007-2032 period predict a sustainable average steam production of 29 kg/s, which is sufficient to maintain the generation of 6.8 MWe at the Pauzhetsky power plant.
Yang, Guijun; Zhao, Chunjiang; Pu, Ruiliang; Feng, Haikuan; Li, Zhenhai; Li, Heli; Sun, Chenhong
2015-01-01
Through its association with proteins and plant pigments, leaf nitrogen (N) plays an important regulatory role in photosynthesis, leaf respiration, and net primary production. However, the traditional methods of measurement leaf N are rooted in sample-based spectroscopy in laboratory. There is a big challenge of deriving leaf N from the nondestructive field-measured leaf spectra. In this study, the original PROSPECT model was extended by replacing the absorption coefficient of chlorophyll in the original PROSPECT model with an equivalent N absorption coefficient to develop a nitrogen-based PROSPECT model (N-PROSPECT). N-PROSPECT was evaluated by comparing the model-simulated reflectance values with the measured leaf reflectance values. The validated results show that the correlation coefficient (R) was 0.98 for the wavelengths of 400 to 2500 nm. Finally, N-PROSPECT was used to simulate leaf reflectance using different combinations of input parameters, and partial least squares regression (PLSR) was used to establish the relationship between the N-PROSPECT simulated reflectance and the corresponding leaf nitrogen density (LND). The inverse of the PLSR-based N-PROSPECT model was used to retrieve LND from the measured reflectance with a relatively high accuracy (R2=0.77, RMSE=22.15 μg cm-2). This result demonstrates that the N-PROSPECT model established in this study can accurately simulate nitrogen spectral contributions and retrieve LND.
Charco, María; González, Pablo J.; Galán del Sastre, Pedro
2017-04-01
The Kilauea volcano (Hawaii, USA) is one of the most active volcanoes world-wide and therefore one of the better monitored volcanoes around the world. Its complex system provides a unique opportunity to investigate the dynamics of magma transport and supply. Geodetic techniques, as Interferometric Synthetic Aperture Radar (InSAR) are being extensively used to monitor ground deformation at volcanic areas. The quantitative interpretation of such surface ground deformation measurements using geodetic data requires both, physical modelling to simulate the observed signals and inversion approaches to estimate the magmatic source parameters. Here, we use synthetic aperture radar data from Sentinel-1 radar interferometry satellite mission to image volcano deformation sources during the inflation along Kilauea's Southwest Rift Zone in April-May 2015. We propose a Finite Element Model (FEM) for the calculation of Green functions in a mechanically heterogeneous domain. The key aspect of the methodology lies in applying the reciprocity relationship of the Green functions between the station and the source for efficient numerical inversions. The search for the best-fitting magmatic (point) source(s) is generally conducted for an array of 3-D locations extending below a predefined volume region. However, our approach allows to reduce the total number of Green functions to the number of the observation points by using the, above mentioned, reciprocity relationship. This new methodology is able to accurately represent magmatic processes using physical models capable of simulating volcano deformation in non-uniform material properties distribution domains, which eventually will lead to better description of the status of the volcano.
Zhu, Rui; Zander, Thomas; Dreischarf, Marcel; Duda, Georg N; Rohlmann, Antonius; Schmidt, Hendrik
2013-04-26
Mostly simplified loads were used in biomechanical finite element (FE) studies of the spine because of a lack of data on muscular physiological loading. Inverse static (IS) models allow the prediction of muscle forces for predefined postures. A combination of both mechanical approaches - FE and IS - appears to allow a more realistic modeling. However, it is unknown what deviations are to be expected when muscle forces calculated for models with rigid vertebrae and fixed centers of rotation, as generally found in IS models, are applied to a FE model with elastic vertebrae and discs. The aim of this study was to determine the effects of these disagreements. Muscle forces were estimated for 20° flexion and 10° extension in an IS model and transferred to a FE model. The effects of the elasticity of bony structures (rigid vs. elastic) and the definition of the center of rotation (fixed vs. non-fixed) were quantified using the deviation of actual intervertebral rotation (IVR) of the FE model and the targeted IVR from the IS model. For extension, the elasticity of the vertebrae had only a minor effect on IVRs, whereas a non-fixed center of rotation increased the IVR deviation on average by 0.5° per segment. For flexion, a combination of the two parameters increased IVR deviation on average by 1° per segment. When loading FE models with predicted muscle forces from IS analyses, the main limitations in the IS model - rigidity of the segments and the fixed centers of rotation - must be considered. Copyright © 2013 Elsevier Ltd. All rights reserved.
McAlpine, Jerrold D.
In arid regions, mechanical disturbances along the desert floor can result in large fluctuations of dust particles into the atmosphere. Rotorcraft operation near the surface may have the greatest potential for dust entrainment per vehicle. Due to this, there is a need for efficient tools to estimate the risk of air quality and visibility impacts in the neighborhood of rotorcraft operating near the desert surface. In this study, a set of parameterized models were developed to form a multi-component modeling system to simulate the entrainment and dispersion of dust from a rotorcraft wake. A simplified scheme utilizing momentum theory was applied to predict the shear stress at the ground under the rotorcraft. Stochastic dust emission algorithms were used to predict the PM10 emission rate from the wake. The distribution of dust emission from the wake was assigned at the walls of a box-volume that encapsulates the wake. The distribution was determined using the results of an inverse Lagrangian stochastic particle dispersion modeling study, using a dataset from a full-scale experiment. All of the elements were put together into a model that simulates the dispersion of PM10 dust from a rotorcraft wake. Downwind concentrations of PM10 estimated using the multi-component modeling system compared well to a set of experimental measurements.
Inverting reflections using full-waveform inversion with inaccurate starting models
AlTheyab, Abdullah
2015-08-19
We present a method for inverting seismic reflections using full-waveform inversion (FWI) with inaccurate starting models. For a layered medium, near-offset reflections (with zero angle of incidence) are unlikely to be cycle-skipped regardless of the low-wavenumber velocity error in the initial models. Therefore, we use them as a starting point for FWI, and the subsurface velocity model is then updated during the FWI iterations using reflection wavepaths from varying offsets that are not cycle-skipped. To enhance low-wavenumber updates and accelerate the convergence, we take several passes through the non-linear Gauss-Seidel iterations, where we invert traces from a narrow range of near offsets and finally end at the far offsets. Every pass is followed by applying smoothing to the cumulative slowness update. The smoothing is strong at the early stages and relaxed at later iterations to allow for a gradual reconstruction of the subsurface model in a multiscale manner. Applications to synthetic and field data, starting from inaccurate models, show significant low-wavenumber updates and flattening of common-image gathers after many iterations.
Identification of strain-rate and thermal sensitive material model with an inverse method
Peroni, L; Peroni, M
2010-01-01
This paper describes a numerical inverse method to extract material strength parameters from the experimental data obtained via mechanical tests at different strain-rates and temperatures. It will be shown that this procedure is particularly useful to analyse experimental results when the stress-strain fields in the specimen cannot be correctly described via analytical models. This commonly happens in specimens with no regular shape, in specimens with a regular shape when some instability phenomena occur (for example the necking phenomena in tensile tests that create a strongly heterogeneous stress-strain fields) or in dynamic tests (where the strain-rate field is not constant due to wave propagation phenomena). Furthermore the developed procedure is useful to take into account thermal phenomena generally affecting high strain-rate tests due to the adiabatic overheating related to the conversion of plastic work. The method presented requires strong effort both from experimental and numerical point of view, an...
Thermo-mechanical model identification of a strengthened copper with an inverse method
Peroni, M; Dallocchio, A
2009-01-01
This paper describes a numerical inverse method to extract material strength parameters from the experimental data obtained via mechanical tests at different strain-rates. It will be shown that this procedure is particularly useful to analyse experimental results when the stress-strain fields in the specimen cannot be correctly described via analytical models. This commonly happens in specimens with no regular shape, in specimens with a regular shape when some instability phenomena occur (for example the necking phenomena in tensile tests that create a strongly heterogeneous stress-strain fields) or in dynamic tests (where the strain-rate field is not constant due to wave propagation phenomena). Furthermore the developed procedure is useful to take into account thermal phenomena generally affecting high strain-rate tests due to the adiabatic overheating related to the conversion of plastic work.
Virgili-Llop, Josep; Zagaris, Costantinos; Park, Hyeongjun; Zappulla, Richard; Romano, Marcello
2018-03-01
An experimental campaign has been conducted to evaluate the performance of two different guidance and control algorithms on a multi-constrained docking maneuver. The evaluated algorithms are model predictive control (MPC) and inverse dynamics in the virtual domain (IDVD). A linear-quadratic approach with a quadratic programming solver is used for the MPC approach. A nonconvex optimization problem results from the IDVD approach, and a nonlinear programming solver is used. The docking scenario is constrained by the presence of a keep-out zone, an entry cone, and by the chaser's maximum actuation level. The performance metrics for the experiments and numerical simulations include the required control effort and time to dock. The experiments have been conducted in a ground-based air-bearing test bed, using spacecraft simulators that float over a granite table.
Directory of Open Access Journals (Sweden)
Asan Gani
2010-09-01
Full Text Available Active vibration control of the first three modes of a vibrating cantilever beam using collocated piezoelectric sensor and actuator is examined in this paper. To achieve this, a model based on Euler-Bernoulli beam equation is adopted and extended to the case of three bonded piezoelectric patches that act as sensor, actuator and exciter respectively. A compensated inverse PID controller has been designed and developed to damp first three modes of vibration. Controllers have been designed for each mode and these are later combined in parallel to damp any of the three modes. Individual controller gives better reduction in sensor output for the second and third modes while the combined controller performs better for the first mode. Simulation studies are carried out using MATLAB. These results are compared and verified experimentally and the real-time implementation is carried out with xPC-target toolbox in MATLAB
Dorati, Rossella; Genta, Ida; Modena, Tiziana; Conti, Bice
2013-01-01
The goal of the present work was to evaluate and discuss vibration nozzle microencapsulation (VNM) technology combined to lyophilization, for the microencapsulation of a hydrophilic model molecule into a hydrophilic polymer. Fluorescein-loaded alginate microparticles prepared by VNM and emulsion phase inversion microencapsulation (EPIM) were lyophilized. Morphology, particle size distribution, lyophilized microspheres stability upon rehydration, drug loading and in vitro release were evaluated. Well-formed microspheres were obtained by the VNM technique, with higher yields of production (93.3-100%) and smaller particle size (d50138.10-158.00) than the EPIM microspheres. Rehydration upon lyophilization occurred in 30 min maintaining microsphere physical integrity. Fluorescein release was always faster from the microspheres obtained by VNM (364 h) than from those obtained by EPIM (504 h). The results suggest that VNM is a simple, easy to be scaled-up process suitable for the microencapsulation hydrophilic drugs.
Regional inverse modeling for high reactive species with PYVAR-CHIMERE
Fortems-Cheiney, A.; Pison, I.; Dufour, G.; Broquet, G.; Costantino, L.
2017-12-01
The degradation of air quality is a worldwide environmental problem: according to the World Health Organization WHO, 92% of the world's population breathe polluted air in 2016. A number of air pollutants associated with respiratory disease and shortened life expectancy play a particularly important role in global outdoor air pollution. In addition to threatening both human health and ecosystems, these gaseous air pollutants including nitrogen oxides (NOx=NO+NO2), sulfur dioxide (SO2), ammonia (NH3), and volatile organic compounds (VOCs) could be precursors of ozone (O3) and Particulate Matter (PM). Without a strong scientific back-up to determine their different sources, the necessary regulations to improve air quality will not be efficient. To date, only chemistry-transport models (CTM) are able to describe pollutant concentrations at any location in the world and their evolution in the atmosphere. Consequently, they have become essential tools for studying air quality. However, CTM are hampered by incomplete information on gaseous precursors and one of the large shortcoming for simulating the gaseous pollutants budgets is the lack of high spatio-temporal variability for the emission estimations provided as inputs for chemistry-transport models. For all these reasons, an inverse system called PYVAR-CHIMERE has been developed, operating in synergy between a CTM and atmospheric observations, and being adjust for the highly reactive species of interest here, as NO2. We present here the first results of this Bayesian variational inverse method for the quantification of NO2 emissions both over Europe (in March 2011) and over China (in January 2015), with a spatial resolution of 0.5°x0.5° and at a weekly temporal resolution, constrained by surface measurements and OMI NO2 satellite observations.
Direct and inverse modelling for environmental risk assessment and emission control
Penenko, V.; Baklanov, A.; Tsvetova, E.; Mahura, A.
2009-04-01
A concept of environmental modelling and its applications for Siberian regions are presented. The regions are considered both as sources and receptors of pollution as elements of the global climatic system. A methodology has been developed to build the combined methods of forward and inverse modelling for the problems of the air quality, environmental risk assessment and control. It is based on variational principles and methods of adjoint sensitivity theory. This allows obtaining the optimal numerical schemes and universal algorithm of the forward-inverse modelling. Following the concept, the functionals (describing the generalised characteristics of the processes, data, and models) are considered together with the basic model components. To combine all these elements in the frames of forward and inverse relations, we suppose that each of them may contain uncertainty. In this case, it is naturally to formulate a weak-constraint variational principle for the augmented functional which contains the model description in the form of integral identity and the cost functional including the total measure of all uncertainties. The stationary conditions for the augmented functional with respect to the variations its functional arguments define the mutually agreed structure of numerical schemes for forward and adjoint problems, and sensitivity relations. For quantitative risk assessment the following characteristics are useful: (i) values of goal functionals and their variations in a form of sensitivity relations; (ii) risk and sensitivity functions to the variations of the sources. It is convenient to take the risk function multiplied by the source function as a distributed risk measure. The variational technique provides the backward propagation of information, contained in the target functionals, to parameters and sources of the models through the sensitivity and uncertainty functions. This gives a base for realisation of the feedback algorithms and methods of control
Backus, George E.
1999-01-01
The purpose of the grant was to study how prior information about the geomagnetic field can be used to interpret surface and satellite magnetic measurements, to generate quantitative descriptions of prior information that might be so used, and to use this prior information to obtain from satellite data a model of the core field with statistically justifiable error estimates. The need for prior information in geophysical inversion has long been recognized. Data sets are finite, and faithful descriptions of aspects of the earth almost always require infinite-dimensional model spaces. By themselves, the data can confine the correct earth model only to an infinite-dimensional subset of the model space. Earth properties other than direct functions of the observed data cannot be estimated from those data without prior information about the earth. Prior information is based on what the observer already knows before the data become available. Such information can be "hard" or "soft". Hard information is a belief that the real earth must lie in some known region of model space. For example, the total ohmic dissipation in the core is probably less that the total observed geothermal heat flow out of the earth's surface. (In principle, ohmic heat in the core can be recaptured to help drive the dynamo, but this effect is probably small.) "Soft" information is a probability distribution on the model space, a distribution that the observer accepts as a quantitative description of her/his beliefs about the earth. The probability distribution can be a subjective prior in the sense of Bayes or the objective result of a statistical study of previous data or relevant theories.
Marjavaara, B. D.; Ebermark, S.; Lundström, T. S.
2009-09-01
A multiobjective surrogate-based inverse modeling technique to predict the spatial and temporal pressure distribution numerically during the fabrication of sheet moulding compounds (SMCs) is introduced. Specifically, an isotropic temperature-dependent Newtonian viscosity model of a SMC charge is fitted to experimental measurements via numerical simulations in order to mimic the temporal pressure distribution at two spatial locations simultaneously. The simulations are performed by using the commercial computational fluid dynamics (CFD) code ANSYS CFX-10.0, and the multiobjective surrogate-based fitting procedure proposed is carried out with a hybrid formulation of the NSGA-IIa evolutionary algorithm and the response surface methodology in Matlab. The outcome of the analysis shows the ability of the optimization framework to efficiently reduce the total computational load of the problem. Furthermore, the viscosity model assumed seems to be able to re solve the temporal pressure distribution and the advancing flow front accurately, which can not be said of the spatial pressure distribution. Hence, it is recommended to improve the CFD model proposed in order to better capture the true behaviour of the mould flow.
Zhang, Y.; Dalguer, L. A.; Song, S.; Clinton, J. F.
2013-12-01
Detailed source imaging of the spatial and temporal slip distribution of earthquakes is a main research goal for seismology. In this study we investigate how the number and geometrical distribution of seismic stations affect finite kinematic source inversion results by inverting ground motions derived from a known synthetic dynamic earthquake rupture model, which is governed by the slip weakening friction law with heterogeneous stress distribution. Our target dynamic rupture model is a buried strike-slip event (Mw 6.5) in a layered half space (Dalguer & Mai, 2011) with broadband synthetic ground motions created at 168 near-field stations. In the inversion, we modeled low frequency (under 1Hz) waveforms using a genetic algorithm in a Bayesian framework (Moneli et al. 2008) to retrieve peak slip velocity, rupture time, and rise time of the source. The dynamic consistent regularized Yoffe function (Tinti et al. 2005) was applied as a single window slip velocity function. Tikhonov regularization was used to smooth final slip. We tested three station network geometry cases: (a) single station, in which we inverted 3 component waveforms from a single station varying azimuth and epicentral distance; (b) multi-station configurations with similar numbers of stations all at similar distances from, but regularly spaced around the fault; (c) irregular multi-station configurations using different numbers of stations. For analysis, waveform misfits are calculated using all 168 stations. Our results show: 1) single station tests suggest that it may be possible to obtain a relatively good source model even using one station, with a waveform misfit comparable to that obtained with the best source model. The best single station performance occurs with stations in which amplitude ratios between the three components are not large, indicating that P & S waves are all present. We infer that both body wave radiation pattern and distance play an important role in selection of optimal
Top-down estimates of European CH4 and N2O emissions based on four different inverse models
Bergamaschi, P.; Corazza, M.; Karstens, U.; Athanassiadou, M.; Thompson, R.L.; Pison, I.; Manning, A.J.; Bousquet, P.; Segers, A.; Vermeulen, A.T.; Janssens-Maenhout, G.; Schmidt, M.; Ramonet, M.; Meinhardt, F.; Aalto, T.; Haszpra, L.; Moncrieff, J.; Popa, M.E.; Lowry, D.; Steinbacher, M.; Jordan, A.; O'Doherty, S.; Piacentino, S.; Dlugokencky, E.
2015-01-01
European CH4 and N2O emissions are estimated for 2006 and 2007 using four inverse modelling systems, based on different global and regional Eulerian and Lagrangian transport models. This ensemble approach is designed to provide more realistic estimates of the overall uncertainties in the derived
DEFF Research Database (Denmark)
Addassi, Mouadh; Johannesson, Björn; Wadsö, Lars
2018-01-01
Here we present an inverse analyses approach to determining the two-phase moisture transport properties relevant to concrete durability modeling. The purposed moisture transport model was based on a continuum approach with two truly separate equations for the liquid and gas phase being connected...
DEFF Research Database (Denmark)
Cai, Hongzhu; Zhdanov, Michael
2014-01-01
This letter introduces a new method for the modeling and inversion of magnetic anomalies caused by crystalline basements. The method is based on the 3-D Cauchy-type integral representation of the magnetic field. Traditional methods use volume integrals over the domains occupied by anomalous...... is particularly significant in solving problems of the modeling and inversion of magnetic data for the depth to the basement. In this letter, a novel method is proposed, which only requires discretizing the magnetic contrast surface for modeling and inversion. We demonstrate the method using several synthetic...... susceptibility and on the prismatic representation of the volumes with an anomalous susceptibility distribution. Such discretization is computationally expensive, particularly in 3-D cases. The technique of Cauchy-type integrals makes it possible to represent the magnetic field as surface integrals, which...
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S. Lowe
2016-09-01
Carlo Markov Chain (MCMC approach to constraining parametric uncertainties.A complete treatment of bulk–surface partitioning is shown to predict CCN spectra similar to those calculated using classical Köhler theory with the surface tension of a pure water drop, as found in previous studies. In addition, model sensitivity to perturbations in the partitioning parameters was found to be negligible. As a result, this study supports previously held recommendations that complex surfactant effects might be neglected, and the continued use of classical Köhler theory in global climate models (GCMs is recommended to avoid an additional computational burden. The framework developed is suitable for application to many additional composition-dependent processes that might impact CCN activation potential. However, the focus of this study is to demonstrate the efficacy of the applied sensitivity analysis to identify important parameters in those processes and will be extended to facilitate a global sensitivity analysis and inverse aerosol–CCN closure analysis.
Liang, Yingjie; Chen, Wen
2018-04-01
The mean squared displacement (MSD) of the traditional ultraslow diffusion is a logarithmic function of time. Recently, the continuous time random walk model is employed to characterize this ultraslow diffusion dynamics by connecting the heavy-tailed logarithmic function and its variation as the asymptotical waiting time density. In this study we investigate the limiting waiting time density of a general ultraslow diffusion model via the inverse Mittag-Leffler function, whose special case includes the traditional logarithmic ultraslow diffusion model. The MSD of the general ultraslow diffusion model is analytically derived as an inverse Mittag-Leffler function, and is observed to increase even more slowly than that of the logarithmic function model. The occurrence of very long waiting time in the case of the inverse Mittag-Leffler function has the largest probability compared with the power law model and the logarithmic function model. The Monte Carlo simulations of one dimensional sample path of a single particle are also performed. The results show that the inverse Mittag-Leffler waiting time density is effective in depicting the general ultraslow random motion.
Directory of Open Access Journals (Sweden)
Chin-Teng Lin
2010-01-01
Full Text Available In this paper, we develop a vision based obstacle detection system by utilizing our proposed fisheye lens inverse perspective mapping (FLIPM method. The new mapping equations are derived to transform the images captured by the fisheye lens camera into the undistorted remapped ones under practical circumstances. In the obstacle detection, we make use of the features of vertical edges on objects from remapped images to indicate the relative positions of obstacles. The static information of remapped images in the current frame is referred to determining the features of source images in the searching stage from either the profile or temporal IPM difference image. The profile image can be acquired by several processes such as sharpening, edge detection, morphological operation, and modified thinning algorithms on the remapped image. The temporal IPM difference image can be obtained by a spatial shift on the remapped image in the previous frame. Moreover, the polar histogram and its post-processing procedures will be used to indicate the position and length of feature vectors and to remove noises as well. Our obstacle detection can give drivers the warning signals within a limited distance from nearby vehicles while the detected obstacles are even with the quasi-vertical edges.
Fluxes of atmospheric methane using novel instruments, field measurements, and inverse modeling
Santoni, Gregory Winn
The atmospheric concentration of methane (CH4) -- the most significant non-CO2 anthropogenic long-lived greenhouse gas -- stabilized between 1999 and 2006 and then began to rise again. Explanations for this behavior differ but studies agree that more measurements and better modeling are needed to reliably explain the model-data discrepancies and predict future change. This dissertation focuses on measurements of CH4 and inverse modeling of atmospheric CH4 fluxes using field measurements at a variety of spatial scales. We first present a new fast-response instrument to measure the isotopic composition of CH4 in ambient air. The instrument was used to characterize mass fluxes and isofluxes (a isotopically-weighted mass flux) from a well-studied research fen in New Hampshire. Eddycovariance and automatic chamber techniques produced consistent estimates of both the CH4 fluxes and their isotopic composition at sub-hourly resolution. We then characterize fluxes of CH4 from aircraft engines using measurements made with the same instrument during the Alternative Aviation Fuel Experiment (AAFEX), a study that aimed to determine the atmospheric impacts of alternative fuel use in the growing aviation industry. Emissions of CO2, CH4, and N2O from different synthetic fuels were statistically indistinguishable from those of the widely used JP-8 jet fuel. We then present airborne observations of the long-lived greenhouse gas suite -- CO2, CH4, N2O, and CO -- during two aircraft campaigns, HIPPO and CalNex, made using a similar instrument built specifically for the NCAR HIAPER GV aircraft. These measurements are compared to data from other onboard sensors and show excellent agreement. We discuss the details of the end-to-end calibration procedures and the data quality-assurance and qualitycontrol (QA/QC). Lastly, we quantify a top-down estimate of California's CH4 emission inventory using the CalNex CH4 observations. Observed CH4 enhancements above background concentrations are
Vethamony, P; Babu, M T; Ramanamurty, M V; Saran, A K; Joseph, Antony; Sudheesh, K; Padgaonkar, Rupali S; Jayakumar, S
2007-06-01
The Gulf of Kachchh (GoK) is situated in the northeastern Arabian Sea. The presence of several industries along its coastal belt makes GoK a highly sensitive coastal ecosystem. In the present study, an attempt is made for the first time to study GoK thermohaline structure and its variability, based on field measurements and model simulations. Though GoK is considered as a well-mixed system, the study reveals that only the central Gulf is well mixed. Vertical gradients in temperature and salinity fields are noticed in the eastern Gulf, where a cold and high saline tongue is observed in the subsurface layers. Salinity indicates the characteristic feature of an inverse estuary with low values (37.20 psu) near the mouth and high values (>40.0 psu) near the head of the Gulf. The model simulated temperature and salinity fields exhibit semidiurnal oscillations similar to that of field observations. Model results show cold, high saline waters advecting from the east during ebb forming a transition zone, which oscillates with tides. A high salinity tongue is seen in the bottom layer, indicating a westward flowing bottom current. The transient zone acts as an dynamic barrier, and plays a vital role in the pollutant transport.
Imhoff, Marc L.; Bounoua, Lahouari; Harriss, Robert; Harriss, Robert; Wells, Gordon; Glantz, Michael; Dukhovny, Victor A.; Orlovsky, Leah
2007-01-01
An inverse process approach using satellite-driven (MODIS) biophysical modeling was used to quantitatively assess water resource demand in semi-arid and arid agricultural lands by comparing the carbon and water flux modeled under both equilibrium (in balance with prevailing climate) and non-equilibrium (irrigated) conditions. Since satellite observations of irrigated areas show higher leaf area indices (LAI) than is supportable by local precipitation, we postulate that the degree to which irrigated lands vary from equilibrium conditions is related to the amount of irrigation water used. For an observation year we used MODIS vegetation indices, local climate data, and the SiB2 photosynthesis-conductance model to examine the relationship between climate and the water stress function for a given grid-cell and observed leaf area. To estimate the minimum amount of supplemental water required for an observed cell, we added enough precipitation to the prevailing climatology at each time step to minimize the water stress function and bring the soil to field capacity. The experiment was conducted on irrigated lands on the U.S. Mexico border and Central Asia and compared to estimates of irrigation water used.
Normal Inverse Gaussian Model-Based Image Denoising in the NSCT Domain
Directory of Open Access Journals (Sweden)
Jian Jia
2015-01-01
Full Text Available The objective of image denoising is to retain useful details while removing as much noise as possible to recover an original image from its noisy version. This paper proposes a novel normal inverse Gaussian (NIG model-based method that uses a Bayesian estimator to carry out image denoising in the nonsubsampled contourlet transform (NSCT domain. In the proposed method, the NIG model is first used to describe the distributions of the image transform coefficients of each subband in the NSCT domain. Then, the corresponding threshold function is derived from the model using Bayesian maximum a posteriori probability estimation theory. Finally, optimal linear interpolation thresholding algorithm (OLI-Shrink is employed to guarantee a gentler thresholding effect. The results of comparative experiments conducted indicate that the denoising performance of our proposed method in terms of peak signal-to-noise ratio is superior to that of several state-of-the-art methods, including BLS-GSM, K-SVD, BivShrink, and BM3D. Further, the proposed method achieves structural similarity (SSIM index values that are comparable to those of the block-matching 3D transformation (BM3D method.
An iterative representer-based scheme for data inversion in reservoir modeling
International Nuclear Information System (INIS)
Iglesias, Marco A; Dawson, Clint
2009-01-01
In this paper, we develop a mathematical framework for data inversion in reservoir models. A general formulation is presented for the identification of uncertain parameters in an abstract reservoir model described by a set of nonlinear equations. Given a finite number of measurements of the state and prior knowledge of the uncertain parameters, an iterative representer-based scheme (IRBS) is proposed to find improved parameters. In this approach, the representer method is used to solve a linear data assimilation problem at each iteration of the algorithm. We apply the theory of iterative regularization to establish conditions for which the IRBS will converge to a stable approximation of a solution to the parameter identification problem. These theoretical results are applied to the identification of the second-order coefficient of a forward model described by a parabolic boundary value problem. Numerical results are presented to show the capabilities of the IRBS for the reconstruction of hydraulic conductivity from the steady-state of groundwater flow, as well as the absolute permeability in the single-phase Darcy flow through porous media
Couvreur, Valentin; Kandelous, Maziar; Mairesse, Harmony; Baram, Shahar; Moradi, Ahmad; Pope, Katrin; Hopmans, Jan
2015-04-01
Groundwater quality is specifically vulnerable in irrigated agricultural lands in California and many other (semi-)arid regions of the world. The routine application of nitrogen fertilizers with irrigation water in California is likely responsible for the high nitrate concentrations in groundwater, underlying much of its main agricultural areas. To optimize irrigation/fertigation practices, it is essential that irrigation and fertilizers are applied at the optimal concentration, place, and time to ensure maximum root uptake and minimize leaching losses to the groundwater. The applied irrigation water and dissolved fertilizer, root nitrate and water uptake interact with soil and root properties in a complex manner that cannot easily be resolved. It is therefore that coupled experimental-modelling studies are required to allow for unravelling of the relevant complexities that result from typical variations of crop properties, soil texture and layering across farmer-managed fields. A combined field monitoring and modelling approach was developed to quantify from simple measurements the leaching of water and nitrate below the root zone. The monitored state variables are soil water content within the root zone, soil matric potential below the root zone, and nitrate concentration in the soil solution. Plant and soil properties of incremented complexity are optimized with the software HYDRUS in an inverse modelling scheme, which allows estimating leaching under constraint of hydraulic principles. Questions of optimal irrigation and fertilization timing can then be addressed using predictive results and global optimization algorithms.
Kong, Changduk; Lim, Semyeong
2011-12-01
Recently, the health monitoring system of major gas path components of gas turbine uses mostly the model based method like the Gas Path Analysis (GPA). This method is to find quantity changes of component performance characteristic parameters such as isentropic efficiency and mass flow parameter by comparing between measured engine performance parameters such as temperatures, pressures, rotational speeds, fuel consumption, etc. and clean engine performance parameters without any engine faults which are calculated by the base engine performance model. Currently, the expert engine diagnostic systems using the artificial intelligent methods such as Neural Networks (NNs), Fuzzy Logic and Genetic Algorithms (GAs) have been studied to improve the model based method. Among them the NNs are mostly used to the engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base if there are large amount of learning data. In addition, it has a very complex structure for finding effectively single type faults or multiple type faults of gas path components. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measured performance data, and proposes a fault diagnostic system using the base engine performance model and the artificial intelligent methods such as Fuzzy logic and Neural Network. The proposed diagnostic system isolates firstly the faulted components using Fuzzy Logic, then quantifies faults of the identified components using the NN leaned by fault learning data base, which are obtained from the developed base performance model. In leaning the NN, the Feed Forward Back Propagation (FFBP) method is used. Finally, it is verified through several test examples that the component faults implanted arbitrarily in the engine are well isolated and quantified by the proposed diagnostic system.
Directory of Open Access Journals (Sweden)
K. Verbist
2009-10-01
Full Text Available In arid and semi-arid zones, runoff harvesting techniques are often applied to increase the water retention and infiltration on steep slopes. Additionally, they act as an erosion control measure to reduce land degradation hazards. Nevertheless, few efforts were observed to quantify the water harvesting processes of these techniques and to evaluate their efficiency. In this study, a combination of detailed field measurements and modelling with the HYDRUS-2D software package was used to visualize the effect of an infiltration trench on the soil water content of a bare slope in northern Chile. Rainfall simulations were combined with high spatial and temporal resolution water content monitoring in order to construct a useful dataset for inverse modelling purposes. Initial estimates of model parameters were provided by detailed infiltration and soil water retention measurements. Four different measurement techniques were used to determine the saturated hydraulic conductivity (K_{sat} independently. The tension infiltrometer measurements proved a good estimator of the K_{sat} value and a proxy for those measured under simulated rainfall, whereas the pressure and constant head well infiltrometer measurements showed larger variability. Six different parameter optimization functions were tested as a combination of soil-water content, water retention and cumulative infiltration data. Infiltration data alone proved insufficient to obtain high model accuracy, due to large scatter on the data set, and water content data were needed to obtain optimized effective parameter sets with small confidence intervals. Correlation between the observed soil water content and the simulated values was as high as R^{2}=0.93 for ten selected observation points used in the model calibration phase, with overall correlation for the 22 observation points equal to 0.85. The model results indicate that the infiltration trench has a
Wu, H.; Tang, X.; Wang, Z.
2016-12-01
During the China Victory Day Parade in 2015, temporary emission control measures were conducted over Beijing and surrounding regions to guarantee the air quality of Beijing. This offers a great opportunity to explore the ability and limitations of the top-down emission estimation. In this study, we employed an ensemble Kalman filter (EnKF) in coupling with a Nested Air Quality Prediction Modeling System (NAQPMS) to establish a high temporal and spatial resolution emission inversion estimation scheme. The scheme enables to assimilated more than 400 surface observations of carbon monoxide (CO) and nitrogen dioxides (NO2) into a 5km×5km resolution model to inversely adjust the a priori emission inventory based on the Multi-resolution Emission Inventory for China (MEIC) for the base year of 2010. Fifty ensemble members and an offline hourly inverse analysis were employed during the four-week inverse period. Results suggested that the inverse estimation scheme significantly reduced the biases in the a prior emission inventory. Therefore, a new emission inventory was obtained and served as the base to compare with the inverse emission inventory during the China Victory Day Parade. Comparison between the new base emission inventory and the inverse emission inventory during the China Victory Day Parade revealed the temporal and spatial characters of the emission control measures over Beijing and surrounding areas. Significant emission reductions were found in Beijing-Tianjin-Hebei and surrounding areas. Meanwhile, NOx showed more reductions in areas around Beijing due to more rigorous vehicle control. This study highlighted the advantages and limitations of the EnKF-based emission estimation scheme. The uncertainties related to observation network, sampling strategy, and meteorological errors were also discussed.
A direct and inverse problem for wave crests modelled by interactions of two solitons
Peterson, P.; van Groesen, Embrecht W.C.
2000-01-01
The paper addresses a new "inverse" problem for reconstructing the amplitudes of 2D surface waves from observation of the wave patterns (formed by wave crests). These patterns will depend on the amplitudes because of nonlinear effects. We show that the inverse problem can be solved when the waves
Liou, Jing-Yang; Ting, Chien-Kun; Huang, Yu-Ying; Tsou, Mei-Yung
2016-03-01
A response surface model is a mathematical model used to predict multiple-drug pharmacodynamic interactions. With the use of a previously published volunteer model, we tested the accuracy of the midazolam-alfentanil response surface model during gastrointestinal endoscopy. We enrolled 35 adult patients scheduled for combined endoscopic procedures. Patients were sedated with intravenous midazolam and alfentanil, and monitored with real-time auditory evoked potential. Sedation Observer's Assessment of Alertness/Sedation (OAA/S) scores were recorded by an independent observer every 2 minutes. Patients with OAA/S scores of ≥ 4 were designated as "awake". Pharmacokinetic profiles were calculated using the TIVA trainer. The published response surface model was modified to make estimations more reasonable. Patient response (OAA/S score ≥ 4 or response during gastrointestinal endoscopic procedure sedation. Accuracy in predicting an OAA/S score of response ranged from 0.04% to 2.94% at the time of arousal (OAA/S score ≥ 4) and from 0.24% to 15.55% when the patient was asleep (OAA/S score response of patients undergoing sedated gastrointestinal endoscopic procedures. Future model parameter adjustments are required. Copyright © 2016. Published by Elsevier Taiwan LLC.
Hossain, Murad; Muntasir, Habib Abul; Ishiguro, Masaji; Bhuiyan, Mohiuddin Ahmed; Rashid, Mamunur; Sugihara, Takumichi; Nagatomo, Takafumi
2012-01-01
We previously reported that sarpogrelate, a selective 5-HT2A antagonist, showed a potent inverse agonist activity to constitutively active mutant (C322K) of human 5-HT2A receptor (5-HT2AR). However, it remains to be unknown about the actual mechanism of this mutant for its constitutive activation as well as inverse agonist activity of sarpogrelate. Our model shows that mutation (C322K) of 5-HT2AR causes electronic repulsion between positively charged Arg173(3.50) and Lys322(6.34) residues resulting outward movement of the C-terminus of transmembrane helix (TMH) III. This motion of TMH III leads to a partially active structure of the receptor, which may be a key step in receptor activation. The structural model of the partially active receptor also indicates that the binding of sarpogrelate to the constitutively active receptor causes an inward swing of TMH III to an inactive receptor structure. Therefore, the present study may suggest that the electronic repulsion causing outward movement of the C-terminus of TMH III may be the key step for constitutive activation of mutant C322K of 5-HT2AR and the inward movement of TMH III causes the inverse agonist activity of sarpogrelate.
Large-scale 3-D modeling by integration of resistivity models and borehole data through inversion
DEFF Research Database (Denmark)
Foged, N.; Marker, Pernille Aabye; Christiansen, A. V.
2014-01-01
and the borehole data set in one variable. Finally, we use k-means clustering to generate a 3-D model of the subsurface structures. We apply the procedure to the Norsminde survey in Denmark, integrating approximately 700 boreholes and more than 100 000 resistivity models from an airborne survey...... in the parameterization of the 3-D model covering 156 km2. The final five-cluster 3-D model differentiates between clay materials and different high-resistivity materials from information held in the resistivity model and borehole observations, respectively....
McFarland, Krista; Price, Diana L; Bonhaus, Douglas W
2011-10-01
Parkinson's disease psychosis (PDP) is a condition for which a safe, tolerated, and effective therapy is lacking. Treatment with typical or atypical antipsychotics may be contraindicated in patients with PDP because of the potential for aggravating motor symptoms. This study used a novel animal model with features of both Parkinson's disease (PD) and psychosis to examine a potential mechanism for reversing PDP. Animals with bilateral 6-hydroxydopamine lesions of the substantia nigra displayed motoric impairments characteristic of humans with PD. In addition, they displayed augmented head twitches, augmented amphetamine-induced locomotor activity, and disrupted prepulse inhibition compared with sham controls, behavioral indices frequently used to assess antipsychotic activity in animal models. Pimavanserin, a selective 5-HT2A antagonist/inverse agonist, reversed the psychotic-like behavioral deficits, suggesting that nigrostriatal (6-hydroxydopamine) lesions induced alterations in 5-HT2A-mediated signaling. The selective 5-HT2A inverse agonist M100907, but not the selective 5-HT2C inverse agonist SB 252084 paralleled the effects of pimavanserin. Of note, the reversal of psychotic-like behaviors produced by 5-HT2A inverse agonists occurred without disrupting motor behaviors in lesioned subjects, suggesting that 5HT2A antagonism/inverse agonism may be beneficial in the treatment of PDP.
Real-time inversions for finite fault slip models and rupture geometry based on high-rate GPS data
Minson, Sarah E.; Murray, Jessica R.; Langbein, John O.; Gomberg, Joan S.
2015-01-01
We present an inversion strategy capable of using real-time high-rate GPS data to simultaneously solve for a distributed slip model and fault geometry in real time as a rupture unfolds. We employ Bayesian inference to find the optimal fault geometry and the distribution of possible slip models for that geometry using a simple analytical solution. By adopting an analytical Bayesian approach, we can solve this complex inversion problem (including calculating the uncertainties on our results) in real time. Furthermore, since the joint inversion for distributed slip and fault geometry can be computed in real time, the time required to obtain a source model of the earthquake does not depend on the computational cost. Instead, the time required is controlled by the duration of the rupture and the time required for information to propagate from the source to the receivers. We apply our modeling approach, called Bayesian Evidence-based Fault Orientation and Real-time Earthquake Slip, to the 2011 Tohoku-oki earthquake, 2003 Tokachi-oki earthquake, and a simulated Hayward fault earthquake. In all three cases, the inversion recovers the magnitude, spatial distribution of slip, and fault geometry in real time. Since our inversion relies on static offsets estimated from real-time high-rate GPS data, we also present performance tests of various approaches to estimating quasi-static offsets in real time. We find that the raw high-rate time series are the best data to use for determining the moment magnitude of the event, but slightly smoothing the raw time series helps stabilize the inversion for fault geometry.
Weller, Daniel; Shiwakoti, Suvash; Bergholz, Peter; Grohn, Yrjo; Wiedmann, Martin; Strawn, Laura K
2016-02-01
Technological advancements, particularly in the field of geographic information systems (GIS), have made it possible to predict the likelihood of foodborne pathogen contamination in produce production environments using geospatial models. Yet, few studies have examined the validity and robustness of such models. This study was performed to test and refine the rules associated with a previously developed geospatial model that predicts the prevalence of Listeria monocytogenes in produce farms in New York State (NYS). Produce fields for each of four enrolled produce farms were categorized into areas of high or low predicted L. monocytogenes prevalence using rules based on a field's available water storage (AWS) and its proximity to water, impervious cover, and pastures. Drag swabs (n = 1,056) were collected from plots assigned to each risk category. Logistic regression, which tested the ability of each rule to accurately predict the prevalence of L. monocytogenes, validated the rules based on water and pasture. Samples collected near water (odds ratio [OR], 3.0) and pasture (OR, 2.9) showed a significantly increased likelihood of L. monocytogenes isolation compared to that for samples collected far from water and pasture. Generalized linear mixed models identified additional land cover factors associated with an increased likelihood of L. monocytogenes isolation, such as proximity to wetlands. These findings validated a subset of previously developed rules that predict L. monocytogenes prevalence in produce production environments. This suggests that GIS and geospatial models can be used to accurately predict L. monocytogenes prevalence on farms and can be used prospectively to minimize the risk of preharvest contamination of produce. Copyright © 2016, American Society for Microbiology. All Rights Reserved.
Grate, J W; Patrash, S J; Kaganovet, S N; Abraham, M H; Wise, B M; Gallagher, N B
2001-11-01
In previous work, it was shown that, in principle, vapor descriptors could be derived from the responses of an array of polymer-coated acoustic wave devices. This new chemometric classification approach was based on polymer/vapor interactions following the well-established linear solvation energy relationships (LSERs) and the surface acoustic wave (SAW) transducers being mass sensitive. Mathematical derivations were included and were supported by simulations. In this work, an experimental data set of polymer-coated SAW vapor sensors is investigated. The data set includes 20 diverse polymers tested against 18 diverse organic vapors. It is shown that interfacial adsorption can influence the response behavior of sensors with nonpolar polymers in response to hydrogen-bonding vapors; however, in general, most sensor responses are related to vapor interactions with the polymers. It is also shown that polymer-coated SAW sensor responses can be empirically modeled with LSERs, deriving an LSER for each individual sensor based on its responses to the 18 vapors. Inverse least-squares methods are used to develop models that correlate and predict vapor descriptors from sensor array responses. Successful correlations can be developed by multiple linear regression (MLR), principal components regression (PCR), and partial least-squares (PLS) regression. MLR yields the best fits to the training data, however cross-validation shows that prediction of vapor descriptors for vapors not in the training set is significantly more successful using PCR or PLS. In addition, the optimal dimension of the PCR and PLS models supports the dimensionality of the LSER formulation and SAW response models.
Inversion of the Borden Tracer Experiment Data: Investigation of Stochastic Moment Models
Woodbury, Allan D.; Sudicky, E. A.
1992-09-01
Inversion of Dagan's two- and three-dimensional stochastic models using Freyberg's (1986), Rajaram and Gelhar's (1988, 1991), and Barry and Sposito's (1990) moment data from the Borden experiment is carried out to examine (1) the validity of the two-dimensional (Dagan, 1982) and three-dimensional (Dagan, 1988) models and (2) the reduction in uncertainty of the spatial moments over nonconditioned estimates. A direct application of Bayesian statistical inference, in conjunction with Monte Carlo integration, is used to produce posterior probability density functions for the parameters. The parameter ranges from all methods show horizontal integral scales λ between 1.85 and 4.04 m, vertical integral scales λz between 0.144 and 0.459 m, and log hydraulic conductivities In (K) between 0.120 and 0.197. These results compare well to the earlier estimates of Woodbury and Sudicky (1991) and Robin et al. (1991). We show that moment prediction uncertainty is substantially reduced when both tracer moment data and prior estimates of the In (K) geostatistical parameters are incorporated into theoretical formulae based on stochastic dispersion theory.
Identification of strain-rate and thermal sensitive material model with an inverse method
Directory of Open Access Journals (Sweden)
Peroni M.
2010-06-01
Full Text Available This paper describes a numerical inverse method to extract material strength parameters from the experimental data obtained via mechanical tests at different strainrates and temperatures. It will be shown that this procedure is particularly useful to analyse experimental results when the stress-strain fields in the specimen cannot be correctly described via analytical models. This commonly happens in specimens with no regular shape, in specimens with a regular shape when some instability phenomena occur (for example the necking phenomena in tensile tests that create a strongly heterogeneous stress-strain fields or in dynamic tests (where the strain-rate field is not constant due to wave propagation phenomena. Furthermore the developed procedure is useful to take into account thermal phenomena generally affecting high strain-rate tests due to the adiabatic overheating related to the conversion of plastic work. The method presented requires strong effort both from experimental and numerical point of view, anyway it allows to precisely identify the parameters of different material models. This could provide great advantages when high reliability of the material behaviour is necessary. Applicability of this method is particularly indicated for special applications in the field of aerospace engineering, ballistic, crashworthiness studies or particle accelerator technologies, where materials could be submitted to strong plastic deformations at high-strain rate in a wide range of temperature. Thermal softening effect has been investigated in a temperature range between 20°C and 1000°C.
Application of the Post-Widder Laplace inversion algorithm to post seismic rebound models
International Nuclear Information System (INIS)
Cannelli, V.; Melini, D.; Piersanti, A.; Spada, G.
2009-01-01
The post seismic response of a viscoelastic Earth can be computed analytically with a normal-mode approach, based on the application of propagator methods. This framework suffers from many limitations, mostly connected with the solution of the secular equation, whose degree scales with the number of viscoelastic layers so that only low-resolution models can be practically solved. Recently, a viable alternative to the normal-mode approach has been proposed, based on the Post-Widder inversion formula. This method allows to overcome some of the intrinsic limitations of the normal-mode approach, so that Earth models with arbitrary radial resolution can be employed and general linear non-Maxwell rheologies can be implemented. In this work, we test the robustness of the method against a standard normal-mode approach in order to optimize computation performance while ensuring the solution stability. As an application, we address the issue of finding the minimum number of layers with distinct elastic properties needed to accurately describe the post seismic relaxation of a realistic Earth mode.
Identification of strain-rate and thermal sensitive material model with an inverse method
Peroni, L.; Scapin, M.; Peroni, M.
2010-06-01
This paper describes a numerical inverse method to extract material strength parameters from the experimental data obtained via mechanical tests at different strainrates and temperatures. It will be shown that this procedure is particularly useful to analyse experimental results when the stress-strain fields in the specimen cannot be correctly described via analytical models. This commonly happens in specimens with no regular shape, in specimens with a regular shape when some instability phenomena occur (for example the necking phenomena in tensile tests that create a strongly heterogeneous stress-strain fields) or in dynamic tests (where the strain-rate field is not constant due to wave propagation phenomena). Furthermore the developed procedure is useful to take into account thermal phenomena generally affecting high strain-rate tests due to the adiabatic overheating related to the conversion of plastic work. The method presented requires strong effort both from experimental and numerical point of view, anyway it allows to precisely identify the parameters of different material models. This could provide great advantages when high reliability of the material behaviour is necessary. Applicability of this method is particularly indicated for special applications in the field of aerospace engineering, ballistic, crashworthiness studies or particle accelerator technologies, where materials could be submitted to strong plastic deformations at high-strain rate in a wide range of temperature. Thermal softening effect has been investigated in a temperature range between 20°C and 1000°C.
Inverse stochastic-dynamic models for high-resolution Greenland ice core records
Boers, Niklas; Chekroun, Mickael D.; Liu, Honghu; Kondrashov, Dmitri; Rousseau, Denis-Didier; Svensson, Anders; Bigler, Matthias; Ghil, Michael
2017-12-01
Proxy records from Greenland ice cores have been studied for several decades, yet many open questions remain regarding the climate variability encoded therein. Here, we use a Bayesian framework for inferring inverse, stochastic-dynamic models from δ18O and dust records of unprecedented, subdecadal temporal resolution. The records stem from the North Greenland Ice Core Project (NGRIP), and we focus on the time interval 59-22 ka b2k. Our model reproduces the dynamical characteristics of both the δ18O and dust proxy records, including the millennial-scale Dansgaard-Oeschger variability, as well as statistical properties such as probability density functions, waiting times and power spectra, with no need for any external forcing. The crucial ingredients for capturing these properties are (i) high-resolution training data, (ii) cubic drift terms, (iii) nonlinear coupling terms between the δ18O and dust time series, and (iv) non-Markovian contributions that represent short-term memory effects.
Aljanaideh, Omar; Janaideh, Mohammad Al; Rakheja, Subhash; Su, Chun-Yi
2013-02-01
Magnetostrictive actuators invariably exhibit hysteresis nonlinearities that tend to become significant under high rates of inputs, and could cause oscillations and error in the micro-positioning tasks. This study presents a methodology for compensation of hysteresis nonlinearity in a magnetostrictive actuator subject to a wide range of input rates in an open-loop manner. The hysteresis compensation is attained through application of an inverse rate-dependent Prandtl-Ishlinskii model formulated on the basis of the rate-dependent Prandtl-Ishlinskii hysteresis model and laboratory-measured hysteresis properties of the magnetostrictive actuator under inputs at frequencies up to 200 Hz. The effectiveness of the inverse rate-dependent Prandtl-Ishlinskii model compensator for mitigating the major and minor loop hysteresis nonlinearities is demonstrated through simulation results and hardware-in-the-loop laboratory measurements of a magnetostrictive actuator (stroke ±50 μm) under inputs in the 1-200 Hz frequency range. Both the simulation and experimental results revealed reduction of peak hysteresis from 4.7 to 0.645 μm, when the proposed inverse rate-dependent model is applied as a feedforward hysteresis compensator, which occurred under excitations at the lowest frequency of 1 Hz. The results suggest that the inverse Prandtl-Ishlinskii model could provide hysteresis compensation under different rates of inputs in a simple and effective manner.
Effects of petrophysical uncertainty in Bayesian hydrogeophysical inversion and model selection
Brunetti, Carlotta; Linde, Niklas
2017-04-01
Hydrogeophysical studies rely on petrophysical relationships that link geophysical properties to hydrological proprieties and state variables of interest; these relationships are frequently assumed to be perfect (i.e., a one-to-one relation). Using first-arrival traveltime data from a synthetic crosshole ground-penetrating radar (GPR) experiment, we investigate the role of petrophysical uncertainty on porosity estimates from Markov chain Monte Carlo (MCMC) inversion and on Bayes factors (i.e., ratios of the evidences, or marginal likelihoods, of two competing models) used in Bayesian model selection. The petrophysical errors (PE) are conceptualized by a correlated zero-mean multi-Gaussian field with horizontal anisotropy with a resulting correlation coefficient of 0.8 between porosity and radar wave speed. We consider four different cases: (1) no PE are present (i.e., they are not used to generate the synthetic data) and they are not inferred in the MCMC inversion, (2) the PE are inferred for but they are not present in the data, (3) the PE are present in the data, but not inferred for and (4) the PE are present in the data and inferred for. To obtain appropriate acceptance ratios (i.e., between 35% and 45%), it is necessary to infer the PE as model parameters with a proper proposal distribution (simple Monte Carlo sampling of the petrophysical errors within Metropolis leads to very small acceptance rates). Case 4 provides consistent porosity field estimates (no bias) and the correlation coefficient between the "true" and posterior mean porosity field decreases from 0.9 for case 1 to 0.75. For case 2, we find that the variance of the posterior mean porosity field is too low and the porosity range is underestimated (i.e., some of the variance is accounted for by the inferred petrophysical uncertainty). Correspondingly, the porosity range is too wide for case 3 as it is used to account for petrophysical errors in the data. When comparing three different conceptual
Energy Technology Data Exchange (ETDEWEB)
Krueger, O.; Ebinghaus, R.; Kock, H.H.; Richter-Politz, I.; Geilhufe, C.
1998-12-31
Anthropogenic emission sources of gaseous mercury at the contaminated industrial site BSL Werk Schkopau have been determined by measurements and numerical modelling applying a local dispersion model. The investigations are based on measurements from several field campaigns in the period of time between December 1993 and June 1994. The estimation of the source strengths was performed by inverse modelling using measurements as constraints for the dispersion model. Model experiments confirmed the applicability of the inverse modelling procedure for the source strength estimation at BSL Werk Schkopau. At the factory premises investigated, the source strengths of four source areas, among them three closed chlor-alkali productions, one partly removed acetaldehyde factory and additionaly one still producing chlor-alkali factory have been identified with an approximate total gaseous mercury emission of lower than 2.5 kg/day. (orig.)
DEFF Research Database (Denmark)
Oh, Geok Lian; Brunskog, Jonas
2014-01-01
Techniques have been studied for the localization of an underground source with seismic interrogation signals. Much of the work has involved defining either a P-wave acoustic model or a dispersive surface wave model to the received signal and applying the time-delay processing technique...... and frequency-wavenumber processing to determine the location of the underground tunnel. Considering the case of determining the location of an underground tunnel, this paper proposed two physical models, the acoustic approximation ray tracing model and the finite difference time domain three-dimensional (3D......) elastic wave model to represent the received seismic signal. Two localization algorithms, beamforming and Bayesian inversion, are developed for each physical model. The beam-forming algorithms implemented are the modified time-and-delay beamformer and the F-K beamformer. Inversion is posed...
International Nuclear Information System (INIS)
Hey, Jonathan; Malloy, Adam C.; Martinez-Botas, Ricardo; Lamperth, Michael
2015-01-01
Highlights: • Conjugate heat transfer analysis of an electric machine. • Inverse identification method for estimating the model parameters. • Experimentally determined thermal properties and electromagnetic losses. • Coupling of inverse identification method with a numerical model. • Improved modeling accuracy through introduction of interface material. - Abstract: Energy conversion devices undergo thermal loading during their operation as a result of inefficiencies in the energy conversion process. This will eventually lead to degradation and possible failure of the device if the heat generated is not properly managed. The ability to accurately predict the thermal behavior of such a device during the initial developmental stage is an important requirement. However, accurate predictions of critical temperature is challenging due to the variation of heat transfer parameters from one device to another. The ability to determine the model parameters is key to accurately representing the heat transfer in such a device. This paper presents the use of an inverse identification technique to estimate the model parameters of an energy conversion device designed for vehicular applications. To simulate the imperfect contact and the presence of insulating materials in the permanent magnet electric machine, thin material are introduced at the component interface of the numerical model. The proposed inverse identification method is used to estimate the equivalent thermal conductance of the thin material. In addition, the electromagnetic losses generated in the permanent magnet is also derived indirectly from the temperature measurement using the same method. With the thermal properties and input parameters of the numerical model obtained from the inverse identification method, the critical temperature of the device can be predicted more accurately. The deviation between the maximum measured and predicted winding temperature is less than 2.4%
Tarver, Craig
2017-06-01
An Ignition and Growth reactive flow model for detonating LX-04 (85% HMX / 15% Viton) was developed using new and previously obtained experimental data on: cylinder test expansion; wave curvature; failure diameter; and laser interferometric copper and tantalum foil free surface velocities and LiF interface particle velocity histories. A reaction product JWL EOS generated by the CHEETAH code compared favorably with the existing, well normalized LX-04 product JWL when both were used with the Ignition and Growth model. Good agreement with all existing experimental data was obtained. Keywords: LX-04, HMX, detonation, Ignition and Growth PACS:82.33.Vx, 82.40.Fp This work was performed under the auspices of the U. S. Department of Energy by the Lawrence Livermore National Laboratory under Contract No. DE-AC52-07NA27344.
Population inversion in a stationary recombining plasma
International Nuclear Information System (INIS)
Otsuka, M.
1980-01-01
Population inversion, which occurs in a recombining plasma when a stationary He plasma is brought into contact with a neutral gas, is examined. With hydrogen as a contact gas, noticeable inversion between low-lying levels of H as been found. The overpopulation density is of the order of 10 8 cm -3 , which is much higher then that (approx. =10 5 cm -3 ) obtained previously with He as a contact gas. Relations between these experimental results and the conditions for population inversion are discussed with the CR model
Quantifying Uncertainty in Inverse Models of Geologic Data from Shear Zones
Davis, J. R.; Titus, S.
2016-12-01
We use Bayesian Markov chain Monte Carlo simulation to quantify uncertainty in inverse models of geologic data. Although this approach can be applied to many tectonic settings, field areas, and mathematical models, we focus on transpressional shear zones. The underlying forward model, either kinematic or dynamic, produces a velocity field, which predicts the dikes, foliation-lineations, crystallographic preferred orientation (CPO), shape preferred orientation (SPO), and other geologic data that should arise in the shear zone. These predictions are compared to data using modern methods of geometric statistics, including the Watson (for lines such as dike poles), isotropic matrix Fisher (for orientations such as foliation-lineations and CPO), and multivariate normal (for log-ellipsoids such as SPO) distributions. The result of the comparison is a likelihood, which is a key ingredient in the Bayesian approach. The other key ingredient is a prior distribution, which reflects the geologist's knowledge of the parameters before seeing the data. For some parameters, such as shear zone strike and dip, we identify realistic informative priors. For other parameters, where the geologist has no prior knowledge, we identify useful uninformative priors.We investigate the performance of this approach through numerical experiments on synthetic data sets. A fundamental issue is that many models of deformation exhibit asymptotic behavior (e.g., flow apophyses, fabric attractors) or periodic behavior (e.g., SPO when the clasts are rigid), which causes the likelihood to be too uniform. Based on our experiments, we offer rules of thumb for how many data, of which types, are needed to constrain deformation.
Zhang, D.; Liao, Q.
2016-12-01
The Bayesian inference provides a convenient framework to solve statistical inverse problems. In this method, the parameters to be identified are treated as random variables. The prior knowledge, the system nonlinearity, and the measurement errors can be directly incorporated in the posterior probability density function (PDF) of the parameters. The Markov chain Monte Carlo (MCMC) method is a powerful tool to generate samples from the posterior PDF. However, since the MCMC usually requires thousands or even millions of forward simulations, it can be a computationally intensive endeavor, particularly when faced with large-scale flow and transport models. To address this issue, we construct a surrogate system for the model responses in the form of polynomials by the stochastic collocation method. In addition, we employ interpolation based on the nested sparse grids and takes into account the different importance of the parameters, under the condition of high random dimensions in the stochastic space. Furthermore, in case of low regularity such as discontinuous or unsmooth relation between the input parameters and the output responses, we introduce an additional transform process to improve the accuracy of the surrogate model. Once we build the surrogate system, we may evaluate the likelihood with very little computational cost. We analyzed the convergence rate of the forward solution and the surrogate posterior by Kullback-Leibler divergence, which quantifies the difference between probability distributions. The fast convergence of the forward solution implies fast convergence of the surrogate posterior to the true posterior. We also tested the proposed algorithm on water-flooding two-phase flow reservoir examples. The posterior PDF calculated from a very long chain with direct forward simulation is assumed to be accurate. The posterior PDF calculated using the surrogate model is in reasonable agreement with the reference, revealing a great improvement in terms of
Methane in the Amazon: A forward and inverse regional modeling approach
Beck, V.; Gerbig, C.; Koch, F. T.; Karstens, U.; Chen, H.; Bela, M. M.; Longo, K.; Freitas, S.; Bergamaschi, P. M.; Kaplan, J. O.; Prigent, C.
2011-12-01
The Amazon region is an important player in the global methane (CH4) cycle, the second most important greenhouse gas after CO2. Different major CH4 sources in the Amazon region such as anaerobic microbial production in wetlands and biomass burning will be affected by changing climate. Therefore, a thorough understanding of the processes is required. Within the BARCA (Balanço Atmosférico Regional de Carbono na Amazônia) project, airborne measurements of greenhouse gases, associated tracers and aerosols were taken during the end of the dry season in November 2008 as well as during the end of the wet season in May 2009. These aircraft measurements and additional ground based measurements provide a test bed for high resolution transport simulation of CH4. Here we present a comparison of WRF-Chem passive tracer simulations of CH4 to airborne CH4 observations obtained from the BARCA campaigns in November 2008 and May 2009 using the newly established WRF Greenhouse Gas Model (WRF-GHG) in combination with two different process-based bottom-up models for the calculation of CH4 emissions from anaerobic microbial production in wetlands (Kaplan and Walter-Heimann) and three different wetland inundation maps (Kaplan, JERS-1SAR, Prigent). The comparison illustrates the importance of a wetland inundation map with inundated area changing in time, and the quality of the representation of atmospheric transport in regional models in tropical regions. In addition, we demonstrate a comparison of WRF-GHG CH4 simulations to TT34 tower observations (35 m above ground; located 60 km north-west of Manaus, Brazil) for August 2009, evaluating the performance of WRF-GHG in representing CH4 observations in the planetary boundary layer in tropical regions. Finally, we present preliminary results of a regional inversion using the TM3-STILT model together with the above mentioned observations for the estimation of the CH4 budget of the Amazon region.
A highly predictive A 4 flavor 3-3-1 model with radiative inverse seesaw mechanism
Cárcamo Hernández, A. E.; Long, H. N.
2018-04-01
We build a highly predictive 3-3-1 model, where the field content is extended by including several SU(3) L scalar singlets and six right handed Majorana neutrinos. In our model the {SU}{(3)}C× {SU}{(3)}L× U{(1)}X gauge symmetry is supplemented by the {A}4× {Z}4× {Z}6× {Z}16× {Z}16{\\prime } discrete group, which allows to get a very good description of the low energy fermion flavor data. In the model under consideration, the {A}4× {Z}4× {Z}6× {Z}16× {Z}16{\\prime } discrete group is broken at very high energy scale down to the preserved Z 2 discrete symmetry, thus generating the observed pattern of SM fermion masses and mixing angles and allowing the implementation of the loop level inverse seesaw mechanism for the generation of the light active neutrino masses, respectively. The obtained values for the physical observables in the quark sector agree with the experimental data, whereas those ones for the lepton sector also do, only for the case of inverted neutrino mass spectrum. The normal neutrino mass hierarchy scenario of the model is ruled out by the neutrino oscillation experimental data. We find an effective Majorana neutrino mass parameter of neutrinoless double beta decay of m ee = 46.9 meV, a leptonic Dirac CP violating phase of -81.37° and a Jarlskog invariant of about 10-2 for the inverted neutrino mass hierarchy. The preserved Z 2 symmetry allows for a stable scalar dark matter candidate.
Modelling and Inverse-Modelling: Experiences with O.D.E. Linear Systems in Engineering Courses
Martinez-Luaces, Victor
2009-01-01
In engineering careers courses, differential equations are widely used to solve problems concerned with modelling. In particular, ordinary differential equations (O.D.E.) linear systems appear regularly in Chemical Engineering, Food Technology Engineering and Environmental Engineering courses, due to the usefulness in modelling chemical kinetics,…
DEFF Research Database (Denmark)
Sakellariou, Jason; Roudi, Yasser; Mezard, Marc
2012-01-01
We study how the degree of symmetry in the couplings influences the performance of three mean field methods used for solving the direct and inverse problems for generalized Sherrington-Kirkpatrick models. In this context, the direct problem is predicting the potentially time-varying magnetizations...... than the other two approximations, TAP outperforms MF when the coupling matrix is nearly symmetric, while MF works better when it is strongly asymmetric. For the inverse problem, MF performs better than both TAP and nMF, although an ad hoc adjustment of TAP can make it comparable to MF. For high...
3D modelling of Trompsburg Complex (in South Africa) using 3D focusing inversion of gravity data
Rezaie, Mohammad; Moradzadeh, Ali; Kalate, Ali Nejati; Aghajani, Hamid; Kahoo, Amin Roshandel; Moazam, Sahar
2017-06-01
The Trompsburg complex is a huge layered mafic igneous rock that is located near the town of Trompsburg in the Free State Province, South Africa that does not outcrop on the surface. Here, we construct 3D model of Trompsburg intrusion using 3D focusing inversion of gravity data. The inversion of gravity data is one of the most important topics in the quantitative interpretation of practical geophysical data. Focusing inversion can produce compact solution and recover the sharp boundaries between intrusive body and host rocks. In focusing inversion of Trompsburg gravity data we set focusing parameter equals 0.02. According to the geological information, lower density bound set to -0.1 g/cm3 and upper density bound set to 0.5 g/cm3. The results of 3D inversion in this study indicate that the Trompsburg Complex is a deep bowl-shaped intrusion which is extended to 33(km) below the surface. It is like an oval in horizontal plane sections with major axis of nearly 50 km in west- east direction and north- south minor axis about 30 km. The obtained results confirms that this complex could be related to intraplate magmatism.
Kesumastuti, Lintang; Marsono, Agus; Yatimantoro, Tatok; Pribadi, Sugeng
2017-07-01
This study performed W-Phase inversion for eight events with large magnitude (M>7) that occured in Indonesia for the period of 2006-2016 by using global data obtained from IRIS DMC (Incorporated Research Institutions for Seismology Data Management Center). The results of W-Phase inversion; both moment magnitude and focal mechanism were generally similar with the Global CMT (Centroid Moment Tensor) solutions. The result shows that maximum deviation of moment magnitude was 0,09 and the average of magnitudo deviation was 0.03625. Comparison of moment magnitude (Mw) indicates that seismic moments from Global CMT and W-Phase inversion are larger than that from body waves, especially for the 2010 Mentawai earthquake. Tsunami simulation was performed using two different source parameters and sea floor deformation, from Global CMT and W-Phase inversion to get arrival times and heights on the coasts to be validated by observation tide gauge data from IOC (Intergovernmental Oceanographic Commission). The simulation shows that these two models; Global CMT and W-Phase inversion yields similar tsunami arrival times and heights on the coasts, but they have a bit difference with the observation data for some tide gauge station.
Mertin, C.; Huse, K.; Hirt, G.
2016-08-01
Under process conditions such as bending of flat wire made from high strength spring steel, the occurring strains are many times higher than the maximum strains determined from uniaxial tensile tests. To determine the elasto-plastic material behaviour of high strength spring steel (X10CrNi18-8), an inverse modelling approach using a simple testing method is presented. A 3-point bending test with the resulting force-displacement measurements is used for the inverse analysis. The inverse approach is used for determining the Young's modulus and hardening parameters of the Ludwik-Hollomon's law for bending of high strength spring steel. FE simulations with the optimised material data meet the experimentally measured punch forces during bending. The optimised material data considerably enhances the springback prediction.
Developing an Earth system Inverse model for the Earth's energy and water budgets.
Haines, K.; Thomas, C.; Liu, C.; Allan, R. P.; Carneiro, D. M.
2017-12-01
The CONCEPT-Heat project aims at developing a consistent energy budget for the Earth system in order to better understand and quantify global change. We advocate a variational "Earth system inverse" solution as the best methodology to bring the necessary expertise from different disciplines together. L'Ecuyer et al (2015) and Rodell et al (2015) first used a variational approach to adjust multiple satellite data products for air-sea-land vertical fluxes of heat and freshwater, achieving closed budgets on a regional and global scale. However their treatment of horizontal energy and water redistribution and its uncertainties was limited. Following the recent work of Liu et al (2015, 2017) which used atmospheric reanalysis convergences to derive a new total surface heat flux product from top of atmosphere fluxes, we have revisited the variational budget approach introducing a more extensive analysis of the role of horizontal transports of heat and freshwater, using multiple atmospheric and ocean reanalysis products. We find considerable improvements in fluxes in regions such as the North Atlantic and Arctic, for example requiring higher atmospheric heat and water convergences over the Arctic than given by ERA-Interim, thereby allowing lower and more realistic oceanic transports. We explore using the variational uncertainty analysis to produce lower resolution corrections to higher resolution flux products and test these against in situ flux data. We also explore the covariance errors implied between component fluxes that are imposed by the regional budget constraints. Finally we propose this as a valuable methodology for developing consistent observational constraints on the energy and water budgets in climate models. We take a first look at the same regional budget quantities in CMIP5 models and consider the implications of the differences for the processes and biases active in the models. Many further avenues of investigation are possible focused on better valuing
Ceylan, Halil; Gopalakrishnan, Kasthurirangan; Birkan Bayrak, Mustafa; Guclu, Alper
2013-09-01
The need to rapidly and cost-effectively evaluate the present condition of pavement infrastructure is a critical issue concerning the deterioration of ageing transportation infrastructure all around the world. Nondestructive testing (NDT) and evaluation methods are well-suited for characterising materials and determining structural integrity of pavement systems. The falling weight deflectometer (FWD) is a NDT equipment used to assess the structural condition of highway and airfield pavement systems and to determine the moduli of pavement layers. This involves static or dynamic inverse analysis (referred to as backcalculation) of FWD deflection profiles in the pavement surface under a simulated truck load. The main objective of this study was to employ biologically inspired computational systems to develop robust pavement layer moduli backcalculation algorithms that can tolerate noise or inaccuracies in the FWD deflection data collected in the field. Artificial neural systems, also known as artificial neural networks (ANNs), are valuable computational intelligence tools that are increasingly being used to solve resource-intensive complex engineering problems. Unlike the linear elastic layered theory commonly used in pavement layer backcalculation, non-linear unbound aggregate base and subgrade soil response models were used in an axisymmetric finite element structural analysis programme to generate synthetic database for training and testing the ANN models. In order to develop more robust networks that can tolerate the noisy or inaccurate pavement deflection patterns in the NDT data, several network architectures were trained with varying levels of noise in them. The trained ANN models were capable of rapidly predicting the pavement layer moduli and critical pavement responses (tensile strains at the bottom of the asphalt concrete layer, compressive strains on top of the subgrade layer and the deviator stresses on top of the subgrade layer), and also pavement
Dai, Jun; Choo, Min-Kyung; Park, Jin Mo; Fisher, David E
2017-12-01
The retinoic acid receptor-related orphan receptors RORα and RORγ are critical for the functions of specific subsets of T cells and innate lymphoid cells, which are key drivers of inflammatory disease in barrier tissues. Here, we investigate the anti-inflammatory potential of SR1001, a synthetic RORα/γ inverse agonist, in mouse models of atopic dermatitis and acute irritant dermatitis. Topical treatment with SR1001 reduces epidermal and dermal features of MC903-induced atopic dermatitis-like disease and suppresses the production of type 2 cytokines and other inflammatory mediators in lesional skin. In the epidermis, SR1001 treatment blocks MC903-induced expression of TSLP and reverses impaired keratinocyte differentiation. SR1001 is also effective in alleviating acute dermatitis triggered by 12-O-tetradecanoylphorbol-13-acetate. Overall, our results suggest that RORα/γ are important therapeutic targets for cutaneous inflammation and suggest topical usage of inhibitory ligands as an approach to treating skin diseases of inflammatory etiology. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.
Solt, Laura A; Banerjee, Subhashis; Campbell, Sean; Kamenecka, Theodore M; Burris, Thomas P
2015-03-01
Hyperglycemia associated with type 1 diabetes is a consequence of immune-mediated destruction of insulin producing pancreatic β-cells. Although it is apparent that both CD8(+) T cells and TH1 cells are key contributors to type 1 diabetes, the function of TH17 cells in disease onset and progression remains unclear. The nuclear receptors retinoic acid receptor-related orphan receptors-α and γt (RORα and RORγt) play critical roles in the development of TH17 cells and ROR-specific synthetic ligands have proven efficacy in several mouse models of autoimmunity. To investigate the roles and therapeutic potential for targeting the RORs in type 1 diabetes, we administered SR1001, a selective RORα/γ inverse agonist, to nonobese diabetic mice. SR1001 significantly reduced diabetes incidence and insulitis in the treated mice. Furthermore, SR1001 reduced proinflammatory cytokine expression, particularly TH17-mediated cytokines, reduced autoantibody production, and increased the frequency of CD4(+)Foxp3(+) T regulatory cells. These data suggest that TH17 cells may have a pathological role in the development of type 1 diabetes, and use of ROR-specific synthetic ligands targeting this cell type may prove utility as a novel treatment for type 1 diabetes.
Directory of Open Access Journals (Sweden)
B. Verheggen
2006-01-01
Full Text Available Classical nucleation theory is unable to explain the ubiquity of nucleation events observed in the atmosphere. This shows a need for an empirical determination of the nucleation rate. Here we present a novel inverse modeling procedure to determine particle nucleation and growth rates based on consecutive measurements of the aerosol size distribution. The particle growth rate is determined by regression analysis of the measured change in the aerosol size distribution over time, taking into account the effects of processes such as coagulation, deposition and/or dilution. This allows the growth rate to be determined with a higher time-resolution than can be deduced from inspecting contour plots ('banana-plots''. Knowing the growth rate as a function of time enables the evaluation of the time of nucleation of measured particles of a certain size. The nucleation rate is then obtained by integrating the particle losses from time of measurement to time of nucleation. The regression analysis can also be used to determine or verify the optimum value of other parameters of interest, such as the wall loss or coagulation rate constants. As an example, the method is applied to smog chamber measurements. This program offers a powerful interpretive tool to study empirical aerosol population dynamics in general, and nucleation and growth in particular.
Forward modeling and inversion of tensor CSAMT in 3D anisotropic media
Wang, Tao; Wang, Kun-Peng; Tan, Han-Dong
2017-12-01
Tensor controlled-source audio-frequency magnetotellurics (CSAMT) can yield information about electric and magnetic fields owing to its multi-transmitter configuration compared with the common scalar CSAMT. The most current theories, numerical simulations, and inversion of tensor CSAMT are based on far-field measurements and the assumption that underground media have isotropic resistivity. We adopt a three-dimensional (3D) staggered-grid finite difference numerical simulation method to analyze the resistivity in axial anisotropic and isotropic media. We further adopt the limited-memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) method to perform 3D tensor CSAMT axial anisotropic inversion. The inversion results suggest that when the underground structure is anisotropic, the isotropic inversion will introduce errors to the interpretation.
Mirus, B.B.; Perkins, K.S.; Nimmo, J.R.; Singha, K.
2009-01-01
To understand their relation to pedogenic development, soil hydraulic properties in the Mojave Desert were investi- gated for three deposit types: (i) recently deposited sediments in an active wash, (ii) a soil of early Holocene age, and (iii) a highly developed soil of late Pleistocene age. Eff ective parameter values were estimated for a simplifi ed model based on Richards' equation using a fl ow simulator (VS2D), an inverse algorithm (UCODE-2005), and matric pressure and water content data from three ponded infi ltration experiments. The inverse problem framework was designed to account for the eff ects of subsurface lateral spreading of infi ltrated water. Although none of the inverse problems converged on a unique, best-fi t parameter set, a minimum standard error of regression was reached for each deposit type. Parameter sets from the numerous inversions that reached the minimum error were used to develop probability distribu tions for each parameter and deposit type. Electrical resistance imaging obtained for two of the three infi ltration experiments was used to independently test fl ow model performance. Simulations for the active wash and Holocene soil successfully depicted the lateral and vertical fl uxes. Simulations of the more pedogenically developed Pleistocene soil did not adequately replicate the observed fl ow processes, which would require a more complex conceptual model to include smaller scale heterogeneities. The inverse-modeling results, however, indicate that with increasing age, the steep slope of the soil water retention curve shitis toward more negative matric pressures. Assigning eff ective soil hydraulic properties based on soil age provides a promising framework for future development of regional-scale models of soil moisture dynamics in arid environments for land-management applications. ?? Soil Science Society of America.
Solving inverse problems for biological models using the collage method for differential equations.
Capasso, V; Kunze, H E; La Torre, D; Vrscay, E R
2013-07-01
In the first part of this paper we show how inverse problems for differential equations can be solved using the so-called collage method. Inverse problems can be solved by minimizing the collage distance in an appropriate metric space. We then provide several numerical examples in mathematical biology. We consider applications of this approach to the following areas: population dynamics, mRNA and protein concentration, bacteria and amoeba cells interaction, tumor growth.
Kim, Eunjung; Guilak, Farshid; Haider, Mansoor A.
2013-01-01
The pericellular matrix (PCM) is the narrow tissue region surrounding all chondrocytes in articular cartilage and, together, the chondrocyte(s) and surrounding PCM have been termed the chondron. Previous theoretical and experimental studies suggest that the structure and properties of the PCM significantly influence the biomechanical environment at the microscopic scale of the chondrocytes within cartilage. In the present study, an axisymmetric boundary element method (BEM) was developed for linear elastic domains with internal interfaces. The new BEM was employed in a multiscale continuum model to determine linear elastic properties of the PCM in situ, via inverse analysis of previously reported experimental data for the three-dimensional morphological changes of chondrons within a cartilage explant in equilibrium unconfined compression (Choi et al., J Biomech, 40:2596–603, 2007). The microscale geometry of the chondron (cell and PCM) within the cartilage extracellular matrix (ECM) was represented as a three-zone equilibrated biphasic region comprised of an ellipsoidal chondrocyte with encapsulating PCM that was embedded within a spherical ECM subjected to boundary conditions for unconfined compression at its outer boundary. Accuracy of the three-zone BEM model was evaluated and compared to analytical finite element solutions. The model was then integrated with a nonlinear optimization technique (Nelder-Mead) to determine PCM elastic properties within the cartilage explant by solving an inverse problem associated with the in situ experimental data for chondron deformation. Depending on the assumed material properties of the ECM and the choice of cost function in the optimization, estimates of the PCM Young’s modulus ranged from ~24 to 59 kPa, consistent with previous measurements of PCM properties on extracted chondrons using micropipette aspiration. Taken together with previous experimental and theoretical studies of cell-matrix interactions in cartilage
Zhang, Jun; Merced, Emmanuelle; Sepúlveda, Nelson; Tan, Xiaobo
2014-12-01
Vanadium dioxide (V{{O}2}), a promising multifunctional smart material, has shown strong promise in microactuation, memory, and optical applications. During thermally induced insulator-to-metal phase transition of V{{O}2}, the changes of its electrical, mechanical, and optical properties demonstrate pronounced, complex hysteresis with respect to the temperature, which presents a challenge in the utilization of this material. In this paper, an extended generalized Prandtl-Ishlinskii model is proposed to model the hysteresis in V{{O}2}, where a nonlinear memoryless function is introduced to improve its modeling capability. A novel inverse compensation algorithm for this hysteresis model is developed based on fixed-point iteration with which the convergence conditions of the algorithm are derived. The proposed approach is shown to be effective for modeling and compensating the asymmetric and non-monotonic hysteresis with saturation between the curvature output and the temperature input of a V{{O}2}-coated microactuator, as well as the asymmetric hysteresis with partial saturation between the resistance output and the temperature input of a V{{O}2} film.
Directory of Open Access Journals (Sweden)
Qingwei Liu
2014-01-01
Full Text Available The initial step towards a nondestructive technique that estimates grain orientation in an anisotropic weld is presented in this paper. The purpose is to aid future forward simulations of ultrasonic NDT of this kind of weld to achieve a better result. A forward model that consists of a weld model, a transmitter model, a receiver model, and a 2D ray tracing algorithm is introduced. An inversion based on a multiobjective genetic algorithm is also presented. Experiments are conducted for both P and SV waves in order to collect enough data used in the inversion. Calculation is conducted to fulfill the estimation with both the synthetic data and the experimental data. Concluding remarks are presented at the end of the paper.
Xu, Zhengwei
Modeling of induced polarization (IP) phenomena is important for developing effective methods for remote sensing of subsurface geology and is widely used in mineral exploration. However, the quantitative interpretation of IP data in a complex 3D environment is still a challenging problem of applied geophysics. In this dissertation I use the regularized conjugate gradient method to determine the 3D distribution of the four parameters of the Cole-Cole model based on surface induced polarization (IP) data. This method takes into account the nonlinear nature of both electromagnetic induction (EMI) and IP phenomena. The solution of the 3D IP inverse problem is based on the regularized smooth inversion only. The method was tested on synthetic models with DC conductivity, intrinsic chargeability, time constant, and relaxation parameters, and it was also applied to the practical 3D IP survey data. I demonstrate that the four parameters of the Cole-Cole model, DC electrical resistivity, rho 0 , chargeability, eta time constant, tau and the relaxation parameter, C, can be recovered from the observed IP data simultaneously. There are four Cole-Cole parameters involved in the inversion, in other words, within each cell, there are DC conductivity (sigma0 ), chargeability (eta), time parameters (tau), and relaxation parameters (C) compared to conductivity only, used in EM only inversion. In addition to more inversion parameters used in IP survey, dipole-dipole configuration which requires more sources and receivers. One the other hand, calculating Green tensor and Frechet matrix time consuming and storing them requires a lot of memory. So, I develop parallel computation using MATLAB parallel tool to speed up the calculation.
International Nuclear Information System (INIS)
Cartalade, Alain
2002-01-01
This research thesis concerns the modelling of aquifer flows under the CEA/Cadarache site. The author reports the implementation of a numerical simulation tool adapted to large scale flows in fractured media, and its application to the Cadarache nuclear site. After a description of the site geological and hydrogeological characteristics, the author presents the conceptual model on which the modelling is based, presents the inverse model which allows a better definition of parameters, reports the validation of the inverse approach by means of synthetic and semi-synthetic cases. Then, he reports experiments and simulation of the Cadarache site
Elsawy, Hesham
2014-08-01
Using stochastic geometry, we develop a tractable uplink modeling paradigm for outage probability and spectral efficiency in both single and multi-tier cellular wireless networks. The analysis accounts for per user equipment (UE) power control as well as the maximum power limitations for UEs. More specifically, for interference mitigation and robust uplink communication, each UE is required to control its transmit power such that the average received signal power at its serving base station (BS) is equal to a certain threshold ρo. Due to the limited transmit power, the UEs employ a truncated channel inversion power control policy with a cutoff threshold of ρo. We show that there exists a transfer point in the uplink system performance that depends on the following tuple: BS intensity λ, maximum transmit power of UEs Pu, and ρo. That is, when Pu is a tight operational constraint with respect to (w.r.t.) λ and ρo, the uplink outage probability and spectral efficiency highly depend on the values of λ and ρo. In this case, there exists an optimal cutoff threshold ρ*o, which depends on the system parameters, that minimizes the outage probability. On the other hand, when Pu is not a binding operational constraint w.r.t. λ and ρo, the uplink outage probability and spectral efficiency become independent of λ and ρo. We obtain approximate yet accurate simple expressions for outage probability and spectral efficiency, which reduce to closed forms in some special cases. © 2002-2012 IEEE.
Directory of Open Access Journals (Sweden)
Priyanka eSingh
2011-03-01
Full Text Available We have taken advantage of a newly described Drosophila model to gain insights into the potential mechanism of antiepileptic drugs (AEDs, a group of drugs that are widely used in the treatment of several neurological and psychiatric conditions besides epilepsy. In the recently described Drosophila model that is inspired by pentylenetetrazole (PTZ induced kindling epileptogenesis in rodents, chronic PTZ treatment for seven days causes a decreased climbing speed and an altered CNS transcriptome, with the latter mimicking gene expression alterations reported in epileptogenesis. In the model, an increased climbing speed is further observed seven days after withdrawal from chronic PTZ. We used this post-PTZ withdrawal regime to identify potential AED mechanism. In this regime, treatment with each of the five AEDs tested, namely, ethosuximide (ETH, gabapentin (GBP, vigabatrin (VGB, sodium valproate (NaVP and levetiracetam (LEV, resulted in rescuing of the altered climbing behavior. The AEDs also normalized PTZ withdrawal induced transcriptomic perturbation in fly heads; whereas AED untreated flies showed a large number of up- and down-regulated genes which were enriched in several processes including gene expression and cell communication, the AED treated flies showed differential expression of only a small number of genes that did not enrich gene expression and cell communication processes. Gene expression and cell communication related upregulated genes in AED untreated flies overrepresented several pathways - spliceosome, RNA degradation, and ribosome in the former category, and inositol phosphate metabolism, phosphatidylinositol signaling, endocytosis and hedgehog signaling in the latter. Transcriptome remodeling effect of AEDs was overall confirmed by microarray clustering that clearly separated the profiles of AED treated and untreated flies. Besides being consistent with previously implicated pathways, our results provide evidence for a role of
Zakaria, M. A.; Majeed, A. P. P. A.; Taha, Z.; Alim, M. M.; Baarath, K.
2018-03-01
The movement of a lower limb exoskeleton requires a reasonably accurate control method to allow for an effective gait therapy session to transpire. Trajectory tracking is a nontrivial means of passive rehabilitation technique to correct the motion of the patients’ impaired limb. This paper proposes an inverse predictive model that is coupled together with the forward kinematics of the exoskeleton to estimate the behaviour of the system. A conventional PID control system is used to converge the required joint angles based on the desired input from the inverse predictive model. It was demonstrated through the present study, that the inverse predictive model is capable of meeting the trajectory demand with acceptable error tolerance. The findings further suggest the ability of the predictive model of the exoskeleton to predict a correct joint angle command to the system.
International Nuclear Information System (INIS)
Ravetto, P.; Sumini, M.; Ganapol, B.D.
1988-01-01
In an attempt to better understand the influence of prompt and delayed neutrons on nuclear reactor dynamics, a continuous slowing down model based on Fermi age theory was developed several years ago. This model was easily incorporated into the one-group diffusion equation and provided a realistic physical picture of how delayed and prompt neutrons slow down and simultaneously diffuse throughout a medium. The model allows for different slowing down times for each delayed neutron group as well as for prompt neutrons and for spectral differences between the two typed of neutrons. Because of its generality, this model serves not only a a useful predictive tool to anticipate reactor transients, but also as an excellent educational tool to demonstrate the effect of delayed neutrons in reactor kinetics. However, because of numerical complications, the slowing down model could not be developed to its full potential. In particular, the major limitation was the inversion of the Laplace transform, which relied on a knowledge of the poles associated with the resulting transformed flux. For this reason, only one group of delayed neutrons and times longer than the slowing down times could be considered. As is shown, the new inversion procedure removes the short time limitation as well as allows for any number of delayed neutron groups. The inversion technique is versatile and is useful in teaching numerical methods in nuclear science
Sun, Jia; Shi, Shuo; Yang, Jian; Du, Lin; Gong, Wei; Chen, Biwu; Song, Shalei
2018-01-01
Leaf biochemical constituents provide useful information about major ecological processes. As a fast and nondestructive method, remote sensing techniques are critical to reflect leaf biochemistry via models. PROSPECT model has been widely applied in retrieving leaf traits by providing hemispherical reflectance and transmittance. However, the process of measuring both reflectance and transmittance can be time-consuming and laborious. Contrary to use reflectance spectrum alone in PROSPECT model inversion, which has been adopted by many researchers, this study proposes to use transmission spectrum alone, with the increasing availability of the latter through various remote sensing techniques. Then we analyzed the performance of PROSPECT model inversion with (1) only transmission spectrum, (2) only reflectance and (3) both reflectance and transmittance, using synthetic datasets (with varying levels of random noise and systematic noise) and two experimental datasets (LOPEX and ANGERS). The results show that (1) PROSPECT-5 model inversion based solely on transmission spectrum is viable with results generally better than that based solely on reflectance spectrum; (2) leaf dry matter can be better estimated using only transmittance or reflectance than with both reflectance and transmittance spectra.
An inversion-relaxation approach for sampling stationary points of spin model Hamiltonians
International Nuclear Information System (INIS)
Hughes, Ciaran; Mehta, Dhagash; Wales, David J.
2014-01-01
Sampling the stationary points of a complicated potential energy landscape is a challenging problem. Here, we introduce a sampling method based on relaxation from stationary points of the highest index of the Hessian matrix. We illustrate how this approach can find all the stationary points for potentials or Hamiltonians bounded from above, which includes a large class of important spin models, and we show that it is far more efficient than previous methods. For potentials unbounded from above, the relaxation part of the method is still efficient in finding minima and transition states, which are usually the primary focus of attention for atomistic systems
Hoffmann, J.; Galloway, D.L.; Zebker, H.A.
2003-01-01
We use land-subsidence observations from repeatedly surveyed benchmarks and interferometric synthetic aperture radar (InSAR) in Antelope Valley, California, to estimate spatially varying compaction time constants, ??, and inelastic specific skeletal storage coefficients, Skv*, in a previously calibrated regional groundwater flow and subsidence model. The observed subsidence patterns reflect both the spatial distribution of head declines and the spatially variable inelastic skeletal storage coefficient. Using the nonlinear parameter estimation program UCODE we estimate compaction time constants between 3.8 and 285 years. The Skv* values are estimated by linear estimation and range from 0 to almost 0.09. We find that subsidence observations over long time periods are necessary to constrain estimates of the large compaction time constants in Antelope Valley. The InSAR data used in this study cover only a three-year period, limiting their usefulness in constraining these time constants. This problem will be alleviated as more SAR data become available in the future or where time constants are small. By incorporating the resulting parameter estimates in the previously calibrated regional model of groundwater flow and land subsidence we can significantly improve the agreement between simulated and observed land subsidence both in terms of magnitude and spatial extent. The sum of weighted squared subsidence residuals, a common measure of model fit, was reduced by 73% with respect to the original model. However, the ability of the model to adequately reproduce the subsidence observed over only a few years is impaired by the fact that the simulated hydraulic heads over small time periods are often not representative of the actual aquifer hydraulic heads. Errors in the simulated hydraulic aquifer heads constitute the primary limitation of the approach presented here.
Model inverse calculation of current distributions in the cross-section of a superconducting cable
International Nuclear Information System (INIS)
Usak, P.; Sastry, P.V.P.S.S.; Schwartz, J.
2006-01-01
The solution of an inverse problem for magnetic field mapping, and the related current distribution in the cross-section of a superconducting cable are generally not unique. Nevertheless, for many natural configurations of a transport current distribution in the cross-section of a superconducting cable, the resulting magnetic field can be used for the reconstruction of a current distribution even in the presence of noise to a degree. We show it using several examples. To perform the inverse calculation, the Tichonov method of regularization was successfully applied. The approach was applied for superconducting cables, but its application is general
Atmospheric inverse modeling with known physical bounds: an example from trace gas emissions
Directory of Open Access Journals (Sweden)
S. M. Miller
2014-02-01
the relative merits of each. This paper investigates the applicability of several approaches to bounded inverse problems. A common method of data transformations is found to unrealistically skew estimates for the examined example application. The method of Lagrange multipliers and two Markov chain Monte Carlo (MCMC methods yield more realistic and accurate results. In general, the examined MCMC approaches produce the most realistic result but can require substantial computational time. Lagrange multipliers offer an appealing option for large, computationally intensive problems when exact uncertainty bounds are less central to the analysis. A synthetic data inversion of US anthropogenic methane emissions illustrates the strengths and weaknesses of each approach.
Energy Technology Data Exchange (ETDEWEB)
Cole, Charles R.; Bergeron, Marcel P.; Wurstner, Signe K.; Thorne, Paul D.; Orr, Samuel; Mckinley, Mathew I.
2001-05-31
This report describes a new initiative to strengthen the technical defensibility of predictions made with the Hanford site-wide groundwater flow and transport model. The focus is on characterizing major uncertainties in the current model. PNNL will develop and implement a calibration approach and methodology that can be used to evaluate alternative conceptual models of the Hanford aquifer system. The calibration process will involve a three-dimensional transient inverse calibration of each numerical model to historical observations of hydraulic and water quality impacts to the unconfined aquifer system from Hanford operations since the mid-1940s.
International Nuclear Information System (INIS)
Namatame, Hirofumi; Taniguchi, Masaki
1994-01-01
Photoelectron spectroscopy is regarded as the most powerful means since it can measure almost perfectly the occupied electron state. On the other hand, inverse photoelectron spectroscopy is the technique for measuring unoccupied electron state by using the inverse process of photoelectron spectroscopy, and in principle, the similar experiment to photoelectron spectroscopy becomes feasible. The development of the experimental technology for inverse photoelectron spectroscopy has been carried out energetically by many research groups so far. At present, the heightening of resolution of inverse photoelectron spectroscopy, the development of inverse photoelectron spectroscope in which light energy is variable and so on are carried out. But the inverse photoelectron spectroscope for vacuum ultraviolet region is not on the market. In this report, the principle of inverse photoelectron spectroscopy and the present state of the spectroscope are described, and the direction of the development hereafter is groped. As the experimental equipment, electron guns, light detectors and so on are explained. As the examples of the experiment, the inverse photoelectron spectroscopy of semimagnetic semiconductors and resonance inverse photoelectron spectroscopy are reported. (K.I.)
Directory of Open Access Journals (Sweden)
D. Herckenrath
2013-10-01
Full Text Available Increasingly, ground-based and airborne geophysical data sets are used to inform groundwater models. Recent research focuses on establishing coupling relationships between geophysical and groundwater parameters. To fully exploit such information, this paper presents and compares different hydrogeophysical inversion approaches to inform a field-scale groundwater model with time domain electromagnetic (TDEM and electrical resistivity tomography (ERT data. In a sequential hydrogeophysical inversion (SHI a groundwater model is calibrated with geophysical data by coupling groundwater model parameters with the inverted geophysical models. We subsequently compare the SHI with a joint hydrogeophysical inversion (JHI. In the JHI, a geophysical model is simultaneously inverted with a groundwater model by coupling the groundwater and geophysical parameters to explicitly account for an established petrophysical relationship and its accuracy. Simulations for a synthetic groundwater model and TDEM data showed improved estimates for groundwater model parameters that were coupled to relatively well-resolved geophysical parameters when employing a high-quality petrophysical relationship. Compared to a SHI these improvements were insignificant and geophysical parameter estimates became slightly worse. When employing a low-quality petrophysical relationship, groundwater model parameters improved less for both the SHI and JHI, where the SHI performed relatively better. When comparing a SHI and JHI for a real-world groundwater model and ERT data, differences in parameter estimates were small. For both cases investigated in this paper, the SHI seems favorable, taking into account parameter error, data fit and the complexity of implementing a JHI in combination with its larger computational burden.
Inverse modeling of GOSAT-retrieved ratios of total column CH4 and CO2 for 2009 and 2010
Directory of Open Access Journals (Sweden)
S. Pandey
2016-04-01
Full Text Available This study investigates the constraint provided by greenhouse gas measurements from space on surface fluxes. Imperfect knowledge of the light path through the atmosphere, arising from scattering by clouds and aerosols, can create biases in column measurements retrieved from space. To minimize the impact of such biases, ratios of total column retrieved CH4 and CO2 (Xratio have been used. We apply the ratio inversion method described in Pandey et al. (2015 to retrievals from the Greenhouse Gases Observing SATellite (GOSAT. The ratio inversion method uses the measured Xratio as a weak constraint on CO2 fluxes. In contrast, the more common approach of inverting proxy CH4 retrievals (Frankenberg et al., 2005 prescribes atmospheric CO2 fields and optimizes only CH4 fluxes. The TM5–4DVAR (Tracer Transport Model version 5–variational data assimilation system inverse modeling system is used to simultaneously optimize the fluxes of CH4 and CO2 for 2009 and 2010. The results are compared to proxy inversions using model-derived CO2 mixing ratios (XCO2model from CarbonTracker and the Monitoring Atmospheric Composition and Climate (MACC Reanalysis CO2 product. The performance of the inverse models is evaluated using measurements from three aircraft measurement projects. Xratio and XCO2model are compared with TCCON retrievals to quantify the relative importance of errors in these components of the proxy XCH4 retrieval (XCH4proxy. We find that the retrieval errors in Xratio (mean = 0.61 % are generally larger than the errors in XCO2model (mean = 0.24 and 0.01 % for CarbonTracker and MACC, respectively. On the annual timescale, the CH4 fluxes from the different satellite inversions are generally in agreement with each other, suggesting that errors in XCO2model do not limit the overall accuracy of the CH4 flux estimates. On the seasonal timescale, however, larger differences are found due to uncertainties in XCO2model, particularly
Inverse modeling of GOSAT-retrieved ratios of total column CH4 and CO2 for 2009 and 2010
Pandey, Sudhanshu; Houweling, Sander; Krol, Maarten; Aben, Ilse; Chevallier, Frédéric; Dlugokencky, Edward J.; Gatti, Luciana V.; Gloor, Emanuel; Miller, John B.; Detmers, Rob; Machida, Toshinobu; Röckmann, Thomas
2016-04-01
This study investigates the constraint provided by greenhouse gas measurements from space on surface fluxes. Imperfect knowledge of the light path through the atmosphere, arising from scattering by clouds and aerosols, can create biases in column measurements retrieved from space. To minimize the impact of such biases, ratios of total column retrieved CH4 and CO2 (Xratio) have been used. We apply the ratio inversion method described in Pandey et al. (2015) to retrievals from the Greenhouse Gases Observing SATellite (GOSAT). The ratio inversion method uses the measured Xratio as a weak constraint on CO2 fluxes. In contrast, the more common approach of inverting proxy CH4 retrievals (Frankenberg et al., 2005) prescribes atmospheric CO2 fields and optimizes only CH4 fluxes. The TM5-4DVAR (Tracer Transport Model version 5-variational data assimilation system) inverse modeling system is used to simultaneously optimize the fluxes of CH4 and CO2 for 2009 and 2010. The results are compared to proxy inversions using model-derived CO2 mixing ratios (XCO2model) from CarbonTracker and the Monitoring Atmospheric Composition and Climate (MACC) Reanalysis CO2 product. The performance of the inverse models is evaluated using measurements from three aircraft measurement projects. Xratio and XCO2model are compared with TCCON retrievals to quantify the relative importance of errors in these components of the proxy XCH4 retrieval (XCH4proxy). We find that the retrieval errors in Xratio (mean = 0.61 %) are generally larger than the errors in XCO2model (mean = 0.24 and 0.01 % for CarbonTracker and MACC, respectively). On the annual timescale, the CH4 fluxes from the different satellite inversions are generally in agreement with each other, suggesting that errors in XCO2model do not limit the overall accuracy of the CH4 flux estimates. On the seasonal timescale, however, larger differences are found due to uncertainties in XCO2model, particularly over Australia and in the tropics. The
Stupina, T.; Koulakov, I.; Kopp, H.
2009-04-01
We consider questions of creating structural models and resolution assessment in tomographic inversion of wide-angle active seismic profiling data. For our investigations, we use the PROFIT (Profile Forward and Inverse Tomographic modeling) algorithm which was tested earlier with different datasets. Here we consider offshore seismic profiling data from three areas (Chile, Java and Central Pacific). Two of the study areas are characterized by subduction zones whereas the third data set covers a seamount province. We have explored different algorithmic issues concerning the quality of the solution, such as (1) resolution assessment using different sizes and complexity of synthetic anomalies; (2) grid spacing effects; (3) amplitude damping and smoothing; (4) criteria for rejection of outliers; (5) quantitative criteria for comparing models. Having determined optimal algorithmic parameters for the observed seismic profiling data we have created structural synthetic models which reproduce the results of the observed data inversion. For the Chilean and Java subduction zones our results show similar patterns: a relatively thin sediment layer on the oceanic plate, thicker inhomogeneous sediments in the overlying plate and a large area of very strong low velocity anomalies in the accretionary wedge. For two seamounts in the Pacific we observe high velocity anomalies in the crust which can be interpreted as frozen channels inside the dormant volcano cones. Along both profiles we obtain considerable crustal thickening beneath the seamounts.
Abass, A.; Zilk, M.; Nanz, S.; Fasold, S.; Ehrhardt, S.; Pertsch, T.; Rockstuhl, C.
2017-11-01
We present an efficient Green's function based analytical method for forward but particularly also for the inverse modeling of light scattering by quasi-periodic and aperiodic surface nanostructures. In the forward modeling, good agreement over an important texture amplitude range is achieved between the developed formalism and exact rigorous calculations on the one hand and angle resolved light scattering measurements of complex quasi-periodic SiO2-Au nanopatterned interfaces on the other hand. Exploiting our formalism, we demonstrate for the first time how the inverse problem of quasi-periodic surface textures for a desired multiresonant absorption response can be expressed in terms of coupled systems of multivariate polynomial equations of the height profile's Fourier amplitudes. A good estimate of the required surface profile can thus be obtained in a computationally cheap manner via solving the multivariate polynomial equations. In principle, the inverse modeling formalism introduced here can be implemented in conjunction with any scattering model that provides expressions of the coupling coefficients between different modes in terms of the surface texture height profile.
Estimates of CO2 fluxes over the city of Cape Town, South Africa, through Bayesian inverse modelling
Nickless, Alecia; Rayner, Peter J.; Engelbrecht, Francois; Brunke, Ernst-Günther; Erni, Birgit; Scholes, Robert J.
2018-04-01
We present a city-scale inversion over Cape Town, South Africa. Measurement sites for atmospheric CO2 concentrations were installed at Robben Island and Hangklip lighthouses, located downwind and upwind of the metropolis. Prior estimates of the fossil fuel fluxes were obtained from a bespoke inventory analysis where emissions were spatially and temporally disaggregated and uncertainty estimates determined by means of error propagation techniques. Net ecosystem exchange (NEE) fluxes from biogenic processes were obtained from the land atmosphere exchange model CABLE (Community Atmosphere Biosphere Land Exchange). Uncertainty estimates were based on the estimates of net primary productivity. CABLE was dynamically coupled to the regional climate model CCAM (Conformal Cubic Atmospheric Model), which provided the climate inputs required to drive the Lagrangian particle dispersion model. The Bayesian inversion framework included a control vector where fossil fuel and NEE fluxes were solved for separately.Due to the large prior uncertainty prescribed to the NEE fluxes, the current inversion framework was unable to adequately distinguish between the fossil fuel and NEE fluxes, but the inversion was able to obtain improved estimates of the total fluxes within pixels and across the domain. The median of the uncertainty reductions of the total weekly flux estimates for the inversion domain of Cape Town was 28 %, but reach as high as 50 %. At the pixel level, uncertainty reductions of the total weekly flux reached up to 98 %, but these large uncertainty reductions were for NEE-dominated pixels. Improved corrections to the fossil fuel fluxes would be possible if the uncertainty around the prior NEE fluxes could be reduced. In order for this inversion framework to be operationalised for monitoring, reporting, and verification (MRV) of emissions from Cape Town, the NEE component of the CO2 budget needs to be better understood. Additional measurements of Δ14C and δ13C isotope
Tran, A. P.; Dafflon, B.; Hubbard, S.
2017-12-01
Soil organic carbon (SOC) is crucial for predicting carbon climate feedbacks in the vulnerable organic-rich Arctic region. However, it is challenging to achieve this property due to the general limitations of conventional core sampling and analysis methods. In this study, we develop an inversion scheme that uses single or multiple datasets, including soil liquid water content, temperature and ERT data, to estimate the vertical profile of SOC content. Our approach relies on the fact that SOC content strongly influences soil hydrological-thermal parameters, and therefore, indirectly controls the spatiotemporal dynamics of soil liquid water content, temperature and their correlated electrical resistivity. The scheme includes several advantages. First, this is the first time SOC content is estimated by using a coupled hydrogeophysical inversion. Second, by using the Community Land Model, we can account for the land surface dynamics (evapotranspiration, snow accumulation and melting) and ice/liquid phase transition. Third, we combine a deterministic and an adaptive Markov chain Monte Carlo optimization algorithm to better estimate the posterior distributions of desired model parameters. Finally, the simulated subsurface variables are explicitly linked to soil electrical resistivity via petrophysical and geophysical models. We validate the developed scheme using synthetic experiments. The results show that compared to inversion of single dataset, joint inversion of these datasets significantly reduces parameter uncertainty. The joint inversion approach is able to estimate SOC content within the shallow active layer with high reliability. Next, we apply the scheme to estimate OC content along an intensive ERT transect in Barrow, Alaska using multiple datasets acquired in the 2013-2015 period. The preliminary results show a good agreement between modeled and measured soil temperature, thaw layer thickness and electrical resistivity. The accuracy of estimated SOC content
Global Monthly CO2 Flux Inversion Based on Results of Terrestrial Ecosystem Modeling
Deng, F.; Chen, J.; Peters, W.; Krol, M.
2008-01-01
Most of our understanding of the sources and sinks of atmospheric CO2 has come from inverse studies of atmospheric CO2 concentration measurements. However, the number of currently available observation stations and our ability to simulate the diurnal planetary boundary layer evolution over
Bayes procedures for adaptive inference in inverse problems for the white noise model
Knapik, B.T.; Szabó, B.T.; van der Vaart, A.W.; van Zanten, J.H.
2016-01-01
We study empirical and hierarchical Bayes approaches to the problem of estimating an infinite-dimensional parameter in mildly ill-posed inverse problems. We consider a class of prior distributions indexed by a hyperparameter that quantifies regularity. We prove that both methods we consider succeed
Sankararaman, Shankar; Goebel, Kai
2013-01-01
This paper investigates the use of the inverse first-order reliability method (inverse- FORM) to quantify the uncertainty in the remaining useful life (RUL) of aerospace components. The prediction of remaining useful life is an integral part of system health prognosis, and directly helps in online health monitoring and decision-making. However, the prediction of remaining useful life is affected by several sources of uncertainty, and therefore it is necessary to quantify the uncertainty in the remaining useful life prediction. While system parameter uncertainty and physical variability can be easily included in inverse-FORM, this paper extends the methodology to include: (1) future loading uncertainty, (2) process noise; and (3) uncertainty in the state estimate. The inverse-FORM method has been used in this paper to (1) quickly obtain probability bounds on the remaining useful life prediction; and (2) calculate the entire probability distribution of remaining useful life prediction, and the results are verified against Monte Carlo sampling. The proposed methodology is illustrated using a numerical example.
Quantification of the emissions of the ozone preceding by inverse modelization. Final report
International Nuclear Information System (INIS)
Granier, C.; Petron, G.; Ciais, Ph.; Bousquet, Ph.
2007-01-01
In the framework of this work, inverse methods have been developed and applied for two types of applications: climatological observations to optimize the monthly average of the observed compounds; the distribution of the carbon monoxide. The report presents the experimental methodologies, the used simulation and the results. (A.L.B.)
Inverse-model estimates of the ocean's coupled phosphorus, silicon, and iron cycles
Directory of Open Access Journals (Sweden)
B. Pasquier
2017-09-01
Full Text Available The ocean's nutrient cycles are important for the carbon balance of the climate system and for shaping the ocean's distribution of dissolved elements. Dissolved iron (dFe is a key limiting micronutrient, but iron scavenging is observationally poorly constrained, leading to large uncertainties in the external sources of iron and hence in the state of the marine iron cycle. Here we build a steady-state model of the ocean's coupled phosphorus, silicon, and iron cycles embedded in a data-assimilated steady-state global ocean circulation. The model includes the redissolution of scavenged iron, parameterization of subgrid topography, and small, large, and diatom phytoplankton functional classes. Phytoplankton concentrations are implicitly represented in the parameterization of biological nutrient utilization through an equilibrium logistic model. Our formulation thus has only three coupled nutrient tracers, the three-dimensional distributions of which are found using a Newton solver. The very efficient numerics allow us to use the model in inverse mode to objectively constrain many biogeochemical parameters by minimizing the mismatch between modeled and observed nutrient and phytoplankton concentrations. Iron source and sink parameters cannot jointly be optimized because of local compensation between regeneration, recycling, and scavenging. We therefore consider a family of possible state estimates corresponding to a wide range of external iron source strengths. All state estimates have a similar mismatch with the observed nutrient concentrations and very similar large-scale dFe distributions. However, the relative contributions of aeolian, sedimentary, and hydrothermal iron to the total dFe concentration differ widely depending on the sources. Both the magnitude and pattern of the phosphorus and opal exports are well constrained, with global values of 8. 1 ± 0. 3 Tmol P yr−1 (or, in carbon units, 10. 3 ± 0. 4
Inverse-model estimates of the ocean's coupled phosphorus, silicon, and iron cycles
Pasquier, Benoît; Holzer, Mark
2017-09-01
The ocean's nutrient cycles are important for the carbon balance of the climate system and for shaping the ocean's distribution of dissolved elements. Dissolved iron (dFe) is a key limiting micronutrient, but iron scavenging is observationally poorly constrained, leading to large uncertainties in the external sources of iron and hence in the state of the marine iron cycle. Here we build a steady-state model of the ocean's coupled phosphorus, silicon, and iron cycles embedded in a data-assimilated steady-state global ocean circulation. The model includes the redissolution of scavenged iron, parameterization of subgrid topography, and small, large, and diatom phytoplankton functional classes. Phytoplankton concentrations are implicitly represented in the parameterization of biological nutrient utilization through an equilibrium logistic model. Our formulation thus has only three coupled nutrient tracers, the three-dimensional distributions of which are found using a Newton solver. The very efficient numerics allow us to use the model in inverse mode to objectively constrain many biogeochemical parameters by minimizing the mismatch between modeled and observed nutrient and phytoplankton concentrations. Iron source and sink parameters cannot jointly be optimized because of local compensation between regeneration, recycling, and scavenging. We therefore consider a family of possible state estimates corresponding to a wide range of external iron source strengths. All state estimates have a similar mismatch with the observed nutrient concentrations and very similar large-scale dFe distributions. However, the relative contributions of aeolian, sedimentary, and hydrothermal iron to the total dFe concentration differ widely depending on the sources. Both the magnitude and pattern of the phosphorus and opal exports are well constrained, with global values of 8. 1 ± 0. 3 Tmol P yr-1 (or, in carbon units, 10. 3 ± 0. 4 Pg C yr-1) and 171. ± 3. Tmol Si yr-1. We diagnose the
Czech Academy of Sciences Publication Activity Database
Tichý, Ondřej; Šmídl, Václav; Hofman, Radek; Šindelářová, Kateřina; Hýža, M.; Stohl, A.
2017-01-01
Roč. 17, č. 20 (2017), s. 12677-12696 ISSN 1680-7316 R&D Projects: GA MŠk(CZ) 7F14287 Institutional support: RVO:67985556 Keywords : Bayesian inverse modeling * iodine-131 * consequences of the iodine release Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Statistics and probability Impact factor: 5.318, year: 2016 http://library.utia.cas.cz/separaty/2017/AS/tichy-0480506.pdf
Czech Academy of Sciences Publication Activity Database
Pecha, Petr; Šmídl, Václav
2016-01-01
Roč. 164, č. 1 (2016), s. 377-394 ISSN 0265-931X R&D Projects: GA MŠk(CZ) 7F14287; GA MV VG20102013018 Institutional support: RVO:67985556 Keywords : Inverse modelling * recursive radioactive plume tracking * Improvement of population protection * monitoring network capability Subject RIV: AQ - Safety, Health Protection, Human - Machine Impact factor: 2.310, year: 2016 http://library.utia.cas.cz/separaty/2016/AS/pecha-0460631.pdf
Tran, A. P.; Dafflon, B.; Hubbard, S. S.; Bisht, G.; Peterson, J.; Ulrich, C.; Romanovsky, V. E.; Kneafsey, T. J.; Wu, Y.
2015-12-01
Quantitative characterization of the soil surface-subsurface hydrological and thermal processes is essential as they are primary factors that control the biogeochemical processes, ecological landscapes and greenhouse gas fluxes. In the Artic region, the surface-subsurface hydrological and thermal regimes co-interact and are both largely influenced by soil texture and soil organic content. In this study, we present a coupled inversion scheme that jointly inverts hydrological, thermal and geophysical data to estimate the vertical profiles of clay, sand and organic contents. Within this inversion scheme, the Community Land Model (CLM4.5) serves as a forward model to simulate the land-surface energy balance and subsurface hydrological-thermal processes. Soil electrical conductivity (from electrical resistivity tomography), temperature and water content are linked together via petrophysical and geophysical models. Particularly, the inversion scheme accounts for the influences of the soil organic and mineral content on both of the hydrological-thermal dynamics and the petrophysical relationship. We applied the inversion scheme to the Next Generation Ecosystem Experiments (NGEE) intensive site in Barrow, AK, which is characterized by polygonal-shaped arctic tundra. The monitoring system autonomously provides a suite of above-ground measurements (e.g., precipitation, air temperature, wind speed, short-long wave radiation, canopy greenness and eddy covariance) as well as below-ground measurements (soil moisture, soil temperature, thaw layer thickness, snow thickness and soil electrical conductivity), which complement other periodic, manually collected measurements. The preliminary results indicate that the model can well reproduce the spatiotemporal dynamics of the soil temperature, and therefore, accurately predict the active layer thickness. The hydrological and thermal dynamics are closely linked to the polygon types and polygon features. The results also enable the
Hedland, D. A.; Degonia, P. K.
1974-01-01
The RAE-1 spacecraft inversion performed October 31, 1972 is described based upon the in-orbit dynamical data in conjunction with results obtained from previously developed computer simulation models. The computer simulations used are predictive of the satellite dynamics, including boom flexing, and are applicable during boom deployment and retraction, inter-phase coast periods, and post-deployment operations. Attitude data, as well as boom tip data, were analyzed in order to obtain a detailed description of the dynamical behavior of the spacecraft during and after the inversion. Runs were made using the computer model and the results were analyzed and compared with the real time data. Close agreement between the actual recorded spacecraft attitude and the computer simulation results was obtained.
Christen, Andreas; Johnson, Mark; Molodovskaya, Marina; Ketler, Rick; Nesic, Zoran; Crawford, Ben; Giometto, Marco; van der Laan, Mike
2013-04-01
The most important long-lived greenhouse gas (LLGHG) emitted during combustion of fuels is carbon dioxide (CO2), however also traces of the LLGHGs methane (CH4) and nitrous oxide (N2O) are released, the quantities of which depend largely on the conditions of the combustion process. Emission factors determine the mass of LLGHGs emitted per energy used (or kilometre driven for cars) and are key inputs for bottom-up emission modelling. Emission factors for CH4 are typically determined in the laboratory or on a test stand for a given combustion system using a small number of samples (vehicles, furnaces), yet associated with larger uncertainties when scaled to entire fleets. We propose an alternative, different approach - Can integrated emission factors be independently determined using direct micrometeorological flux measurements over an urban surface? If so, do emission factors determined from flux measurements (top-down) agree with up-scaled emission factors of relevant combustion systems (heating, vehicles) in the source area of the flux measurement? Direct flux measurements of CH4 were carried out between February and May, 2012 over a relatively densely populated, urban surface in Vancouver, Canada by means of eddy covariance (EC). The EC-system consisted of an ultrasonic anemometer (CSAT-3, Campbell Scientific Inc.) and two open-path infrared gas analyzers (Li7500 and Li7700, Licor Inc.) on a tower at 30m above the surface. The source area of the EC system is characterised by a relative homogeneous morphometry (5.3m average building height), but spatially and temporally varying emission sources, including two major intersecting arterial roads (70.000 cars drive through the 50% source area per day) and seasonal heating in predominantly single-family houses (natural gas). An inverse dispersion model (turbulent source area model), validated against large eddy simulations (LES) of the urban roughness sublayer, allows the determination of the spatial area that
Zhai, G.; Shirzaei, M.
2014-12-01
The Kilauea volcano, Hawaii Island, is one of the most active volcanoes worldwide. Its complex system including magma reservoirs and rift zones, provides a unique opportunity to investigate the dynamics of magma transport and supply. The relatively shallow magma reservoir beneath the caldera stores magma prior to eruption at the caldera or migration to the rift zones. Additionally, the temporally variable pressure in the magma reservoir causes changes in the stress field, driving dike propagation and occasional intrusions at the eastern rift zone. Thus constraining the time-dependent evolution of the magma reservoir plays an important role in understanding magma processes such as supply, storage, transport and eruption. The recent development of space-based monitoring technology, InSAR (Interferometric synthetic aperture radar), allows the detection of subtle deformation of the surface at high spatial resolution and accuracy. In order to understand the dynamics of the magma chamber at Kilauea summit area and the associated stress field, we explored SAR data sets acquired in two overlapping tracks of Envisat SAR data during period 2003-2010. The combined InSAR time series includes 100 samples measuring summit deformation at unprecedented spatiotemporal resolutions. To investigate the source of the summit deformation field, we propose a novel time-dependent inverse modelling approach to constrain the dynamics of the reservoir volume change within the summit magma reservoir in three dimensions. In conjunction with seismic and gas data sets, the obtained time-dependent model could resolve the temporally variable relation between shallow and deep reservoirs, as well as their connection to the rift zone via stress changes. The data and model improve the understanding of the Kilauea plumbing system, physics of eruptions, mechanics of rift intrusions, and enhance eruption forecast models.
Petrov, P.; Newman, G. A.
2011-12-01
Recent developments in high resolution imaging technology of subsurface objects involves a combination of different geophysical measurements (gravity, EM and seismic). A joint image of the subsurface geophysical attributes (velocity, electrical conductivity and density) requires the consistent treatment of the different geophysical data due to their differing physical nature. For example, in conducting media, which is typical of the Earth's interior, EM energy propagation is defined by a diffusive mechanism and may be characterized by two specific length scales: wavelength and skin depth. However, the propagation of seismic signals is a multiwave process and is characterized by a set of wavelengths. Thus, to consistently treat seismic and electromagnetic data an additional length scale is needed for seismic data that does not directly depend on a wavelength and describes a diffusive process, similar to EM wave propagation in the subsurface. Works by Brown et al.(2005), Shin and Cha(2008), and Shin and Ha(2008) suggest that an artificial damping of seismic wave fields via Laplace-Fourier transformation can be an effective approach to obtain a seismic data that have similar spatial resolution to EM data. The key benefit of such transformation is that diffusive wave-field inversion works well for both data sets: seismic (Brown et al.,2005; Shin and Cha,2008) and electromagnetic (Commer and Newman,2008; Newman et al.,2010). With the recent interest in the Laplace-Fourier domain full waveform inversion, 3D fourth and second-order finite-difference schemes for modeling of seismic wave propagation have been developed (Petrov and Newman, 2010). Incorporation of attenuation and anisotropy into a velocity model is a necessary step for a more realistic description of subsurface media. Here we consider the extension of our method which includes attenuation and VTI anisotropy. Our approach is based on the integro-interpolation technique for velocity-stress formulation. Seven
Fang, Z.; Ward, A. L.; Fang, Y.; Yabusaki, S.
2011-12-01
High-resolution geologic models have proven effective in improving the accuracy of subsurface flow and transport predictions. However, many of the parameters in subsurface flow and transport models cannot be determined directly at the scale of interest and must be estimated through inverse modeling. A major challenge, particularly in vadose zone flow and transport, is the inversion of the highly-nonlinear, high-dimensional problem as current methods are not readily scalable for large-scale, multi-process models. In this paper we describe the implementation of a fully automated approach for addressing complex parameter optimization and sensitivity issues on massively parallel multi- and many-core systems. The approach is based on the integration of PNNL's extreme scale Subsurface Transport Over Multiple Phases (eSTOMP) simulator, which uses the Global Array toolkit, with the Beowulf-Cluster inspired parallel nonlinear parameter estimation software, BeoPEST in the MPI mode. In the eSTOMP/BeoPEST implementation, a pre-processor generates all of the PEST input files based on the eSTOMP input file. Simulation results for comparison with observations are extracted automatically at each time step eliminating the need for post-process data extractions. The inversion framework was tested with three different experimental data sets: one-dimensional water flow at Hanford Grass Site; irrigation and infiltration experiment at the Andelfingen Site; and a three-dimensional injection experiment at Hanford's Sisson and Lu Site. Good agreements are achieved in all three applications between observations and simulations in both parameter estimates and water dynamics reproduction. Results show that eSTOMP/BeoPEST approach is highly scalable and can be run efficiently with hundreds or thousands of processors. BeoPEST is fault tolerant and new nodes can be dynamically added and removed. A major advantage of this approach is the ability to use high-resolution geologic models to preserve
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Hansheng Wang
2015-05-01
Full Text Available We use the average crustal structure of the CRUST1.0 model for the Tibetan Plateau to establish a realistic earth model termed as TC1P, and data from the Global Land Data Assimilation System (GLDAS hydrology model and Gravity Recovery and Climate Experiment (GRACE data, to generate the hydrology signals assumed in this study. Modeling of surface radial displacements and gravity variation is performed using both TC1P and the global Preliminary Reference Earth Model (PREM. Furthermore, inversions of the hydrology signals based on simulated Global Positioning System (GPS and GRACE data are performed using PREM. Results show that crust in TC1P is harder and softer than that in PREM above and below a depth of 15 km, respectively, causing larger differences in the computed load Love numbers and loading Green's functions. When annual hydrology signals are assumed, the differences of the radial displacements are found to be as large as approximately 0.6 mm for the truncated degree of 180; while for hydrology-trend signals the differences are very small. When annual hydrology signals and the trends are assumed, the differences in the surface gravity variation are very small. It is considered that TC1P can be used to efficiently remove the hydrological effects on the monitoring of crustal movement. It was also found that when PREM is used inappropriately, the inversion of the hydrology signals from simulated annual GPS signals can only recover approximately 88.0% of the annual hydrology signals for the truncated degree of 180, and the inversion of hydrology signals from the simulated trend GPS signals can recover approximately 92.5% for the truncated degree of 90. However, when using the simulated GRACE data, it is possible to recover almost 100%. Therefore, in future, the TC1P model can be used in the inversions of hydrology signals based on GPS network data. PREM is also valid for use with inversions of hydrology signals from GRACE data at resolutions
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Jesper Sjolte
2014-09-01
Full Text Available The relation between δ 18O of precipitation and temperature has been used in numerous studies to reconstruct past temperatures at ice core sites in Greenland and Antarctica. During the past two decades, it has become clear that the slope between δ 18O and temperature varies in both space and time. Here, we use a general circulation model driven by changes in orbital parameters to investigate the Greenland δ 18O–temperature relation for the previous interglacial, the Eemian. In our analysis, we focus on changes in the moisture source regions, and the results underline the importance of taking the seasonality of climate change into account. The orbitally driven experiments show that continental evaporation over North America increases during summer in the warm parts of the Eemian, while marine evaporation decreases. This likely flattens the Greenland δ 18O response to temperature during summer. Since the main climate change in the experiments occurs during summer this adds to a limited response of δ 18O, which is more strongly tied to temperature during winter than during summer. A south–west to north–east gradient in the δ 18O–temperature slope is also evident for Greenland, with low slopes in the south–west and steeper slopes in the north–east. This probably reflects the proportion of continental moisture and Arctic moisture arriving in Greenland, with more continental moisture in the south–west and less in the north–east, and vice versa for the Arctic moisture.
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Kleiton Augusto dos Santos Silva
Full Text Available Exercise training (ET is an important intervention for chronic diseases such as diabetes mellitus (DM. However, it is not known whether previous exercise training intervention alters the physiological and medical complications of these diseases. We investigated the effects of previous ET on the progression of renal disease and cardiovascular autonomic control in rats with streptozotocin (STZ-induced DM. Male Wistar rats were divided into five groups. All groups were followed for 15 weeks. Trained control and trained diabetic rats underwent 10 weeks of exercise training, whereas previously trained diabetic rats underwent 14 weeks of exercise training. Renal function, proteinuria, renal sympathetic nerve activity (RSNA and the echocardiographic parameters autonomic modulation and baroreflex sensitivity (BRS were evaluated. In the previously trained group, the urinary albumin/creatinine ratio was reduced compared with the sedentary diabetic and trained diabetic groups (p<0.05. Additionally, RSNA was normalized in the trained diabetic and previously trained diabetic animals (p<0.05. The ejection fraction was increased in the previously trained diabetic animals compared with the diabetic and trained diabetic groups (p<0.05, and the myocardial performance index was improved in the previously trained diabetic group compared with the diabetic and trained diabetic groups (p<0.05. In addition, the previously trained rats had improved heart rate variability and BRS in the tachycardic response and bradycardic response in relation to the diabetic group (p<0.05. This study demonstrates that previous ET improves the functional damage that affects DM. Additionally, our findings suggest that the development of renal and cardiac dysfunction can be minimized by 4 weeks of ET before the induction of DM by STZ.
Montesinos, F. G.; Arnoso, J.; Luque, T.; Benavent, T.; Vieira, R.
2009-04-01
It is firmly established that, of all the geodetic or geophysical techniques available, gravity modelling plays an important role in helping us to understand volcanic structures. We present here a study of the structural setting of the volcanic island of La Gomera by the analysis and interpretation of high-resolution gravity data obtained over the island. The gravity data allow us to model the main subsurface anomaly sources of the island, which are related with its volcanic evolution. Our outcome is consistent with the results of previous geophysical and volcanological studies. La Gomera island occupies a central position in the Canarian archipelago. This archipelago is the result of construction and destruction of successive large edifices covering a time span of several million years. Intrusion of magma has caused the development of an enormous amount of dikes that constituted step by step the main framework of the hypabyssal roots of these edifices. La Gomera has a surface about 372 km2 with a roughly circular contour and it is characterised by its central massif of 1487 meters height, dropping steeply to the sea. This island is the only one on the archipelago with no signs of Pleistocene volcanic activity. Its distinctive morphological feature is the intense degree of erosion in all formations, with deep, vertical-walled valleys that cut the island radially and in which the tabular successions of basalts can be seen. The most complex and interesting unit of La Gomera is its Basal Complex, which crops out in a restricted area located at the North and it is formed of plutonic volcanic and sedimentary rocks cut by an extremely dense dyke network. According to several authors, the characteristics of this complex seem to support the hypothesis that these rocks were formed by processes of magmatic sedimentation in a fairly turbulent medium. These conditions could correspond, for instance, to the ones in a reservoir beneath a volcano. Another possibility is that this
Energy Technology Data Exchange (ETDEWEB)
Passos de Figueiredo, Leandro, E-mail: leandrop.fgr@gmail.com [Physics Department, Federal University of Santa Catarina, Florianópolis (Brazil); Grana, Dario [Department of Geology and Geophysics, University of Wyoming, Laramie (United States); Santos, Marcio; Figueiredo, Wagner [Physics Department, Federal University of Santa Catarina, Florianópolis (Brazil); Roisenberg, Mauro [Informatic and Statistics Department, Federal University of Santa Catarina, Florianópolis (Brazil); Schwedersky Neto, Guenther [Petrobras Research Center, Rio de Janeiro (Brazil)
2017-05-01
We propose a Bayesian approach for seismic inversion to estimate acoustic impedance, porosity and lithofacies within the reservoir conditioned to post-stack seismic and well data. The link between elastic and petrophysical properties is given by a joint prior distribution for the logarithm of impedance and porosity, based on a rock-physics model. The well conditioning is performed through a background model obtained by well log interpolation. Two different approaches are presented: in the first approach, the prior is defined by a single Gaussian distribution, whereas in the second approach it is defined by a Gaussian mixture to represent the well data multimodal distribution and link the Gaussian components to different geological lithofacies. The forward model is based on a linearized convolutional model. For the single Gaussian case, we obtain an analytical expression for the posterior distribution, resulting in a fast algorithm to compute the solution of the inverse problem, i.e. the posterior distribution of acoustic impedance and porosity as well as the facies probability given the observed data. For the Gaussian mixture prior, it is not possible to obtain the distributions analytically, hence we propose a Gibbs algorithm to perform the posterior sampling and obtain several reservoir model realizations, allowing an uncertainty analysis of the estimated properties and lithofacies. Both methodologies are applied to a real seismic dataset with three wells to obtain 3D models of acoustic impedance, porosity and lithofacies. The methodologies are validated through a blind well test and compared to a standard Bayesian inversion approach. Using the probability of the reservoir lithofacies, we also compute a 3D isosurface probability model of the main oil reservoir in the studied field.
International Nuclear Information System (INIS)
Groh, Andreas; Krebs, Jochen
2012-01-01
In this paper, a population balance equation, originating from applications in chemical engineering, is considered and novel solution techniques for a related inverse problem are presented. This problem consists in the determination of the breakage rate and the daughter drop distribution of an evolving drop size distribution from time-dependent measurements under the assumption of self-similarity. We analyze two established solution methods for this ill-posed problem and improve the two procedures by adapting suitable data fitting and inversion algorithms to the specific situation. In addition, we introduce a novel technique that, compared to the former, does not require certain a priori information. The improved stability properties of the resulting algorithms are substantiated with numerical examples. (paper)
Wu, Zedong; Alkhalifah, Tariq
2017-09-01
Reflection-waveform inversion (RWI) can help us reduce the nonlinearity of the standard full-waveform inversion by inverting for the background velocity model using the wave path of a single scattered wavefield to an image. However, current RWI implementations usually neglect the multiscattered energy, which will cause some artefacts in the image and the update of the background. To improve existing RWI implementations in taking multiscattered energy into consideration, we split the velocity model into background and perturbation components, integrate them directly in the wave equation and formulate a new optimization problem for both components. In this case, the perturbed model is no longer a single-scattering model, but includes all scattering. Through introducing a new cheap implementation of scattering angle enrichment, the separation of the background and perturbation components can be implemented efficiently. We optimize both components simultaneously to produce updates to the velocity model that is nonlinear with respect to both the background and the perturbation. The newly introduced perturbation model can absorb the non-smooth update of the background in a more consistent way. We apply the proposed approach on the Marmousi model with data that contain frequencies starting from 5 Hz to show that this method can converge to an accurate velocity starting from a linearly increasing initial velocity. Also, our proposed method works well when applied to a field data set.
Wu, Zedong
2017-07-04
Reflection-waveform inversion (RWI) can help us reduce the nonlinearity of the standard full-waveform inversion (FWI) by inverting for the background velocity model using the wave-path of a single scattered wavefield to an image. However, current RWI implementations usually neglect the multi-scattered energy, which will cause some artifacts in the image and the update of the background. To improve existing RWI implementations in taking multi-scattered energy into consideration, we split the velocity model into background and perturbation components, integrate them directly in the wave equation, and formulate a new optimization problem for both components. In this case, the perturbed model is no longer a single-scattering model, but includes all scattering. Through introducing a new cheap implementation of scattering angle enrichment, the separation of the background and perturbation components can be implemented efficiently. We optimize both components simultaneously to produce updates to the velocity model that is nonlinear with respect to both the background and the perturbation. The newly introduced perturbation model can absorb the non-smooth update of the background in a more consistent way. We apply the proposed approach on the Marmousi model with data that contain frequencies starting from 5 Hz to show that this method can converge to an accurate velocity starting from a linearly increasing initial velocity. Also, our proposed method works well when applied to a field data set.
Energy Technology Data Exchange (ETDEWEB)
Granier, C.; Petron, G. [Institut Pierre Simon Laplace (IPSL), Service d' Aeronomie, 75 - Paris (France); Ciais, Ph.; Bousquet, Ph. [Institut Pierre Simon Laplace (IPSL), Lab. des Sciences du Climat et de l' Environnement, 75 - Paris (France)
2007-07-01
In the framework of this work, inverse methods have been developed and applied for two types of applications: climatological observations to optimize the monthly average of the observed compounds; the distribution of the carbon monoxide. The report presents the experimental methodologies, the used simulation and the results. (A.L.B.)
Inverse modeling of groundwater flow in the semiarid evaporitic closed basin of Los Monegros, Spain
Samper-Calvete, F. J.; García-Vera, M. A.
Only minor attention has been given in the past to the study of closed-basin hydrogeology in evaporitic environments, because these basins usually contain poor-quality groundwater. The motivation for hydrogeological research in the Los Monegros area in northeastern Spain was the approval in 1986 of a large irrigation project in the Ebre River basin. The irrigation of 60,000 ha is planned, partly in an evaporitic closed basin containing playa lakes. The project has given rise to environmental concerns. The evaluation of the hydrologic impacts of irrigation requires quantifying properly the hydrogeology of the area. With the available information, a conceptual hydrogeological model was formulated that identifies two main aquifers connected through a leaky aquitard. On the basis of the conceptual model, a numerical model was calibrated under steady-state conditions using the method of maximum-likelihood automatic parameter estimation (Carrera and Neuman, 1986a). The calibrated model reproduces the measured hydraulic heads fairly well and is consistent with independent information on groundwater discharge. By the solution of the inverse problem, reliable parameter estimates were obtained. It is concluded that anisotropy plays a major role in some parts of the lower aquifer. The geometric average of model conductivity is almost two orders of magnitude larger than the average conductivity derived from small-scale field tests. This scale effect in hydraulic conductivity is consistent with the findings of Neuman (1994) and Sánchez-Vila et al. (1996). Résumé Dans le passé, on s'est peu intéresséà l'hydrogéologie des bassins fermés en milieu évaporitique, parce que ces bassins possèdent en général de l'eau souterraine de qualité médiocre. L'intérêt porté aux recherches hydrogéologiques dans la région de Los Monegros, dans le nord-est de l'Espagne est dûà l'approbation en 1986 d'un vaste projet d'irrigation dans le bassin de l'Ebre. L'irrigation de 60000
Silva, Kleiton Augusto dos Santos; Luiz, Rafael da Silva; Rampaso, Rodolfo Rosseto; de Abreu, Nayda Parísio; Moreira, Édson Dias; Mostarda, Cristiano Teixeira; De Angelis, Kátia; de Paulo Castro Teixeira, Vicente; Irigoyen, Maria Cláudia; Schor, Nestor
2012-01-01
Exercise training (ET) is an important intervention for chronic diseases such as diabetes mellitus (DM). However, it is not known whether previous exercise training intervention alters the physiological and medical complications of these diseases. We investigated the effects of previous ET on the progression of renal disease and cardiovascular autonomic control in rats with streptozotocin (STZ)-induced DM. Male Wistar rats were divided into five groups. All groups were followed for 15 weeks. Trained control and trained diabetic rats underwent 10 weeks of exercise training, whereas previously trained diabetic rats underwent 14 weeks of exercise training. Renal function, proteinuria, renal sympathetic nerve activity (RSNA) and the echocardiographic parameters autonomic modulation and baroreflex sensitivity (BRS) were evaluated. In the previously trained group, the urinary albumin/creatinine ratio was reduced compared with the sedentary diabetic and trained diabetic groups (ptrained diabetic and previously trained diabetic animals (ptrained diabetic animals compared with the diabetic and trained diabetic groups (ptrained diabetic group compared with the diabetic and trained diabetic groups (ptrained rats had improved heart rate variability and BRS in the tachycardic response and bradycardic response in relation to the diabetic group (p<0.05). This study demonstrates that previous ET improves the functional damage that affects DM. Additionally, our findings suggest that the development of renal and cardiac dysfunction can be minimized by 4 weeks of ET before the induction of DM by STZ.
Brunner, Dominik; Arnold, Tim; Henne, Stephan; Manning, Alistair; Thompson, Rona L.; Maione, Michela; O'Doherty, Simon; Reimann, Stefan
2017-09-01
Hydrofluorocarbons (HFCs) are used in a range of industrial applications and have largely replaced previously used gases (CFCs and HCFCs). HFCs are not ozone-depleting but have large global warming potentials and are, therefore, reported to the United Nations Framework Convention on Climate Change (UNFCCC). Here, we use four independent inverse models to estimate European emissions of the two HFCs contributing the most to global warming (HFC-134a and HFC-125) and of SF6 for the year 2011. Using an ensemble of inverse models offers the possibility to better understand systematic uncertainties in inversions. All systems relied on the same measurement time series from Jungfraujoch (Switzerland), Mace Head (Ireland), and Monte Cimone (Italy) and the same a priori estimates of the emissions, but differed in terms of the Lagrangian transport model (FLEXPART, NAME), inversion method (Bayesian, extended Kalman filter), treatment of baseline mole fractions, spatial gridding, and a priori uncertainties. The model systems were compared with respect to the ability to reproduce the measurement time series, the spatial distribution of the posterior emissions, uncertainty reductions, and total emissions estimated for selected countries. All systems were able to reproduce the measurement time series very well, with prior correlations between 0.5 and 0.9 and posterior correlations being higher by 0.05 to 0.1. For HFC-125, all models estimated higher emissions from Spain + Portugal than reported to UNFCCC (median higher by 390 %) though with a large scatter between individual estimates. Estimates for Germany (+140 %) and Ireland (+850 %) were also considerably higher than UNFCCC, whereas the estimates for France and the UK were consistent with the national reports. In contrast to HFC-125, HFC-134a emissions from Spain + Portugal were broadly consistent with UNFCCC, and emissions from Germany were only 30 % higher. The data suggest that the UK over-reports its HFC-134a emissions to
Directory of Open Access Journals (Sweden)
D. Brunner
2017-09-01
Full Text Available Hydrofluorocarbons (HFCs are used in a range of industrial applications and have largely replaced previously used gases (CFCs and HCFCs. HFCs are not ozone-depleting but have large global warming potentials and are, therefore, reported to the United Nations Framework Convention on Climate Change (UNFCCC. Here, we use four independent inverse models to estimate European emissions of the two HFCs contributing the most to global warming (HFC-134a and HFC-125 and of SF6 for the year 2011. Using an ensemble of inverse models offers the possibility to better understand systematic uncertainties in inversions. All systems relied on the same measurement time series from Jungfraujoch (Switzerland, Mace Head (Ireland, and Monte Cimone (Italy and the same a priori estimates of the emissions, but differed in terms of the Lagrangian transport model (FLEXPART, NAME, inversion method (Bayesian, extended Kalman filter, treatment of baseline mole fractions, spatial gridding, and a priori uncertainties. The model systems were compared with respect to the ability to reproduce the measurement time series, the spatial distribution of the posterior emissions, uncertainty reductions, and total emissions estimated for selected countries. All systems were able to reproduce the measurement time series very well, with prior correlations between 0.5 and 0.9 and posterior correlations being higher by 0.05 to 0.1. For HFC-125, all models estimated higher emissions from Spain + Portugal than reported to UNFCCC (median higher by 390 % though with a large scatter between individual estimates. Estimates for Germany (+140 % and Ireland (+850 % were also considerably higher than UNFCCC, whereas the estimates for France and the UK were consistent with the national reports. In contrast to HFC-125, HFC-134a emissions from Spain + Portugal were broadly consistent with UNFCCC, and emissions from Germany were only 30 % higher. The data suggest that the UK over
McGuire, A.D.; Christensen, T.R.; Hayes, D.; Heroult, A.; Euskirchen, E.; Yi, Y.; Kimball, J.S.; Koven, C.; Lafleur, P.; Miller, P.A.; Oechel, W.; Peylin, P.; Williams, M.
2012-01-01
Although arctic tundra has been estimated to cover only 8% of the global land surface, the large and potentially labile carbon pools currently stored in tundra soils have the potential for large emissions of carbon (C) under a warming climate. These emissions as radiatively active greenhouse gases in the form of both CO2 and CH4 could amplify global warming. Given the potential sensitivity of these ecosystems to climate change and the expectation that the Arctic will experience appreciable warming over the next century, it is important to assess whether responses of C exchange in tundra regions are likely to enhance or mitigate warming. In this study we compared analyses of C exchange of Arctic tundra between 1990–1999 and 2000–2006 among observations, regional and global applications of process-based terrestrial biosphere models, and atmospheric inversion models. Syntheses of the compilation of flux observations and of inversion model results indicate that the annual exchange of CO2 between arctic tundra and the atmosphere has large uncertainties that cannot be distinguished from neutral balance. The mean estimate from an ensemble of process-based model simulations suggests that arctic tundra acted as a sink for atmospheric CO2 in recent decades, but based on the uncertainty estimates it cannot be determined with confidence whether these ecosystems represent a weak or a strong sink. Tundra was 0.6 °C warmer in the 2000s compared to the 1990s. The central estimates of the observations, process-based models, and inversion models each identify stronger sinks in the 2000s compared with the 1990s. Similarly, the observations and the applications of regional process-based models suggest that CH4 emissions from arctic tundra have increased from the 1990s to 2000s. Based on our analyses of the estimates from observations, process-based models, and inversion models, we estimate that arctic tundra was a sink for atmospheric CO2 of 110 Tg C yr-1 (uncertainty between a
Ingram, WT
2012-01-01
Inverse limits provide a powerful tool for constructing complicated spaces from simple ones. They also turn the study of a dynamical system consisting of a space and a self-map into a study of a (likely more complicated) space and a self-homeomorphism. In four chapters along with an appendix containing background material the authors develop the theory of inverse limits. The book begins with an introduction through inverse limits on [0,1] before moving to a general treatment of the subject. Special topics in continuum theory complete the book. Although it is not a book on dynamics, the influen
Inverse modelling of air quality data through a neural network approach
Russo, A.; Soares, A.; Trigo, R. M.; Pereira, M. J.
2009-04-01
Air quality is usually driven by a complex combination of factors where meteorology, physical obstacles and interaction between pollutants play significant roles. Considering the characteristics of the atmospheric circulation and also the residence times of certain pollutants in the atmosphere, air pollution is, nowadays, considered to be a global problem that affects everyone. As a result, a generalized and growing interest on air quality issues led to research intensification and publication of several articles with quite different levels of scientific depth. The main objective of this work is to produce an air quality model which allows forecasting critical concentration episodes of a certain pollutant by means of neural network modelling. In this paper, we describe the development of a neural network tool to forecast the daily average NO2 concentrations in Lisbon, Portugal, one day ahead. This research is based upon measurements from 22 air quality monitoring stations during the period 2001-2005. The analysis revealed that the most significant variable in predicting NO2 daily concentration is the previous day value of NO2 concentration followed by the 5a.m. NO2 concentration. This approach shows to be very promising for urban air quality characterization, allowing further developments in order to produce an integrated air quality and health surveillance/monitoring system in the area of Lisbon.
Philip, Sajeev; Johnson, Matthew S.
2018-01-01
Atmospheric mixing ratios of carbon dioxide (CO2) are largely controlled by anthropogenic emissions and biospheric fluxes. The processes controlling terrestrial biosphere-atmosphere carbon exchange are currently not fully understood, resulting in terrestrial biospheric models having significant differences in the quantification of biospheric CO2 fluxes. Atmospheric transport models assimilating measured (in situ or space-borne) CO2 concentrations to estimate "top-down" fluxes, generally use these biospheric CO2 fluxes as a priori information. Most of the flux inversion estimates result in substantially different spatio-temporal posteriori estimates of regional and global biospheric CO2 fluxes. The Orbiting Carbon Observatory 2 (OCO-2) satellite mission dedicated to accurately measure column CO2 (XCO2) allows for an improved understanding of global biospheric CO2 fluxes. OCO-2 provides much-needed CO2 observations in data-limited regions facilitating better global and regional estimates of "top-down" CO2 fluxes through inversion model simulations. The specific objectives of our research are to: 1) conduct GEOS-Chem 4D-Var assimilation of OCO-2 observations, using several state-of-the-science biospheric CO2 flux models as a priori information, to better constrain terrestrial CO2 fluxes, and 2) quantify the impact of different biospheric model prior fluxes on OCO-2-assimilated a posteriori CO2 flux estimates. Here we present our assessment of the importance of these a priori fluxes by conducting Observing System Simulation Experiments (OSSE) using simulated OCO-2 observations with known "true" fluxes.
Sethurajan, Athinthra Krishnaswamy; Krachkovskiy, Sergey A; Halalay, Ion C; Goward, Gillian R; Protas, Bartosz
2015-09-17
We used NMR imaging (MRI) combined with data analysis based on inverse modeling of the mass transport problem to determine ionic diffusion coefficients and transference numbers in electrolyte solutions of interest for Li-ion batteries. Sensitivity analyses have shown that accurate estimates of these parameters (as a function of concentration) are critical to the reliability of the predictions provided by models of porous electrodes. The inverse modeling (IM) solution was generated with an extension of the Planck-Nernst model for the transport of ionic species in electrolyte solutions. Concentration-dependent diffusion coefficients and transference numbers were derived using concentration profiles obtained from in situ (19)F MRI measurements. Material properties were reconstructed under minimal assumptions using methods of variational optimization to minimize the least-squares deviation between experimental and simulated concentration values with uncertainty of the reconstructions quantified using a Monte Carlo analysis. The diffusion coefficients obtained by pulsed field gradient NMR (PFG-NMR) fall within the 95% confidence bounds for the diffusion coefficient values obtained by the MRI+IM method. The MRI+IM method also yields the concentration dependence of the Li(+) transference number in agreement with trends obtained by electrochemical methods for similar systems and with predictions of theoretical models for concentrated electrolyte solutions, in marked contrast to the salt concentration dependence of transport numbers determined from PFG-NMR data.
A Solvent-Mediated Coarse-Grained Model of DNA Derived with the Systematic Newton Inversion Method.
Naômé, Aymeric; Laaksonen, Aatto; Vercauteren, Daniel P
2014-08-12
We present a new class of coarse-grained (CG) force fields (FFs) for B-DNA with explicit ions suited for large-scale mesoscale simulations at microsecond-micrometer scale using a wide spectrum of particle simulation methods from molecular dynamics to dissipative particle dynamics. The effective solvent-mediated pairwise interactions making up the FFs are obtained by inverting radial distribution functions and other particle-particle distributions obtained from all-atom simulations of numbers of octadecamer DNA fragments from the Ascona B-DNA library. The inverse Monte Carlo (IMC) method, later known as Newton inversion (NI) (Lyubartsev, A. P.; Laaksonen, A. Phys. Rev. E, 1995, 52, 3730-3737), was used together with the iterative Boltzmann inversion (IBI) scheme to compute the effective CG potentials. We show that this systematic structure-based approach is capable of providing converged potentials that accurately reproduce the structural features of the underlying atomistic system within a few percents of relative difference. We also show that a simple one-site-per-nucleotide model with 10 intramolecular pair interaction potentials is able to reproduce key features of DNA, for example, the persistence length and its dependence on the ionic concentration, experimentally determined around 50 nm at physiological salt concentration.
Directory of Open Access Journals (Sweden)
Yi-Bo Li
2018-01-01
Full Text Available The accurate estimation of soil hydraulic parameters (θs, α, n, and Ks of the van Genuchten–Mualem model has attracted considerable attention. In this study, we proposed a new two-step inversion method, which first estimated the hydraulic parameter θs using objective function by the final water content, and subsequently estimated the soil hydraulic parameters α, n, and Ks, using a vector-evaluated genetic algorithm and particle swarm optimization (VEGA-PSO method based on objective functions by cumulative infiltration and infiltration rate. The parameters were inversely estimated for four types of soils (sand, loam, silt, and clay under an in silico experiment simulating the tension disc infiltration at three initial water content levels. The results indicated that the method is excellent and robust. Because the objective function had multilocal minima in a tiny range near the true values, inverse estimation of the hydraulic parameters was difficult; however, the estimated soil water retention curves and hydraulic conductivity curves were nearly identical to the true curves. In addition, the proposed method was able to estimate the hydraulic parameters accurately despite substantial measurement errors in initial water content, final water content, and cumulative infiltration, proving that the method was feasible and practical for field application.
Moreno Chaves, C. M.; Ussami, N.
2011-12-01
We developed a simple three-dimensional scheme to invert geoid anomalies, aiming to map density variations in the lower crust and the upper mantle. Using a flat-Earth approximation, the model space is represented by a finite set of rectangular prisms. The linear inversion algorithm is based on Tikhonov regularization and the convergence of the solution is controlled by the Levenberg-Marquardt method. Our linear inversion algorithm does not require an initial density model, allowing it to be used where geological constraints on density are not available. To analyze the quality of the model density obtained by the inversion algorithm, we used the resolution and the covariance matrices. In order to study the thermal and the composition state beneath the Yellowstone and to test our algorithm inversion, geoid anomalies were inverted and modeled. Yellowstone exhibits a high geoid anomaly (~13 m), with a topographic swell of about 500 km wide. Residual geoid anomalies were obtained using the EGM2008 [Pavlis et al., 2008] geopotential model expanded up to degree 2160 after removing the long-wavelength component (degree 10). Lower crust and mantle-related geoid anomalies with -80 m amplitude were obtained after removing crustal effects (topographic masses, sediments and crustal thickness variations). The center of the negative geoid anomaly coincides geographically with the low velocity body (Yuan and Dueker [2005] and Waite et al. [2006]) in the upper mantle and with a depression of 12 km of the 410 km discontinuity detected by Fee and Dueker [2004]. Our results show that the lower crust and the upper mantle of the Yellowstone have a predominantly negative density contrast (-10 to -75 kg/m3) relative to the surrounding mantle. The mass deficiency mapped beneath the Yellowstone suggests the mantle to be hotter (-200 to -300 °C) and buoyant to isostatically sustain the high topography of this province (> 3000 m above sea level). The density model shows that the negative
International Nuclear Information System (INIS)
Koohkan, Mohammad Reza
2012-01-01
Data assimilation in geophysical sciences aims at optimally estimating the state of the system or some parameters of the system's physical model. To do so, data assimilation needs three types of information: observations and background information, a physical/numerical model, and some statistical description that prescribes uncertainties to each component of the system. In my dissertation, new methodologies of data assimilation are used in atmospheric chemistry and physics: the joint use of a 4D-Var with a sub-grid statistical model to consistently account for representativeness errors, accounting for multiple scale in the BLUE estimation principle, and a better estimation of prior errors using objective estimation of hyper-parameters. These three approaches will be specifically applied to inverse modelling problems focusing on the emission fields of tracers or pollutants. First, in order to estimate the emission inventories of carbon monoxide over France, in-situ stations which are impacted by the representativeness errors are used. A sub-grid model is introduced and coupled with a 4D-Var to reduce the representativeness error. Indeed, the results of inverse modelling showed that the 4D-Var routine was not fit to handle the representativeness issues. The coupled data assimilation system led to a much better representation of the CO concentration variability, with a significant improvement of statistical indicators, and more consistent estimation of the CO emission inventory. Second, the evaluation of the potential of the IMS (International Monitoring System) radionuclide network is performed for the inversion of an accidental source. In order to assess the performance of the global network, a multi-scale adaptive grid is optimised using a criterion based on degrees of freedom for the signal (DFS). The results show that several specific regions remain poorly observed by the IMS network. Finally, the inversion of the surface fluxes of Volatile Organic Compounds
Cho, Jeongho; Principe, Jose C.; Erdogmus, Deniz; Motter, Mark A.
2005-01-01
The next generation of aircraft will have dynamics that vary considerably over the operating regime. A single controller will have difficulty to meet the design specifications. In this paper, a SOM-based local linear modeling scheme of an unmanned aerial vehicle (UAV) is developed to design a set of inverse controllers. The SOM selects the operating regime depending only on the embedded output space information and avoids normalization of the input data. Each local linear model is associated with a linear controller, which is easy to design. Switching of the controllers is done synchronously with the active local linear model that tracks the different operating conditions. The proposed multiple modeling and control strategy has been successfully tested in a simulator that models the LoFLYTE UAV.
Koepke, C.; Irving, J.
2015-12-01
Bayesian solutions to inverse problems in near-surface geophysics and hydrology have gained increasing popularity as a means of estimating not only subsurface model parameters, but also their corresponding uncertainties that can be used in probabilistic forecasting and risk analysis. In particular, Markov-chain-Monte-Carlo (MCMC) methods have attracted much recent attention as a means of statistically sampling from the Bayesian posterior distribution. In this regard, two approaches are commonly used to improve the computational tractability of the Bayesian-MCMC approach: (i) Forward models involving a simplification of the underlying physics are employed, which offer a significant reduction in the time required to calculate data, but generally at the expense of model accuracy, and (ii) the model parameter space is represented using a limited set of spatially correlated basis functions as opposed to a more intuitive high-dimensional pixel-based parameterization. It has become well understood that model inaccuracies resulting from (i) can lead to posterior parameter distributions that are highly biased and overly confident. Further, when performing model reduction as described in (ii), it is not clear how the prior distribution for the basis weights should be defined because simple (e.g., Gaussian or uniform) priors that may be suitable for a pixel-based parameterization may result in a strong prior bias when used for the weights. To address the issue of model error resulting from known forward model approximations, we generate a set of error training realizations and analyze them with principal component analysis (PCA) in order to generate a sparse basis. The latter is used in the MCMC inversion to remove the main model-error component from the residuals. To improve issues related to prior bias when performing model reduction, we also use a training realization approach, but this time models are simulated from the prior distribution and analyzed using independent
Inverse problem for a physiologically structured population model with variable-effort harvesting
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Andrusyak Ruslan V.
2017-04-01
Full Text Available We consider the inverse problem of determining how the physiological structure of a harvested population evolves in time, and of finding the time-dependent effort to be expended in harvesting, so that the weighted integral of the density, which may be, for example, the total number of individuals or the total biomass, has prescribed dynamics. We give conditions for the existence of a unique, global, weak solution to the problem. Our investigation is carried out using the method of characteristics and a generalization of the Banach fixed-point theorem.
Boren, E. J.; Boschetti, L.; Johnson, D.
2017-12-01
Water plays a critical role in all plant physiological processes, including transpiration, photosynthesis, nutrient transportation, and maintenance of proper plant cell functions. Deficits in water content cause drought-induced stress conditions, such as constrained plant growth and cellular metabolism, while overabundance of water cause anoxic conditions which limit plant physiological processes and promote disease. Vegetation water content maps can provide agricultural producers key knowledge for improving production capacity and resiliency in agricultural systems while facilitating the ability to pinpoint, monitor, and resolve water scarcity issues. Radiative transfer model (RTM) inversion has been successfully applied to remotely sensed data to retrieve biophysical and canopy parameter estimates, including water content. The successful launch of the Landsat 8 Operational Land Imager (OLI) in 2012, Sentinel 2A Multispectral Instrument (MSI) in 2015, followed by Sentinel 2B in 2017, the systematic acquisition schedule and free data distribution policy provide the opportunity for water content estimation at a spatial and temporal scale that can meet the demands of potential operational users: combined, these polar-orbiting systems provide 10 m to 30 m multi-spectral global coverage up to every 3 days. The goal of the present research is to prototype the generation of a cropland canopy water content product, obtained from the newly developed Landsat 8 and Sentinel 2 atmospherically corrected HLS product, through the inversion of the leaf and canopy model PROSAIL5B. We assess the impact of a novel spatial and temporal stratification, where some parameters of the model are constrained by crop type and phenological phase, based on ancillary biophysical data, collected from various crop species grown in a controlled setting and under different water stress conditions. Canopy-level data, collected coincidently with satellite overpasses during four summer field campaigns
Commer, M.; Kowalsky, M. B.; Dafflon, B.; Wu, Y.; Hubbard, S. S.
2013-12-01
Geologic carbon sequestration is being evaluated as a means to mitigate the effects of greenhouse gas emissions. Efforts are underway to identify adequate reservoirs and to evaluate the behavior of injected CO2 over time; time-lapse geophysical methods are considered effective tools for these purposes. Pilot studies have shown that the invasion of CO2 into a background pore fluid can alter the electrical resistivity, with increases from CO2 in the super-critical or gaseous phase, and decreases from CO2 dissolved in groundwater (especially when calcite dissolution is occurring). Because of their sensitivity to resistivity changes, electrical and electromagnetic (EM) methods have been used in such studies for indirectly assessing CO2 saturation changes. While the electrical resistance tomography (ERT) method is a well-established technique for both crosswell and surface applications, its usefulness is limited by the relatively low-resolution information it provides. Controlled-source EM methods, including both frequency-domain and time-domain (transient EM) methods, can offer improved resolution. We report on three studies that aim to maximize the information content of electrical and electromagnetic measurements in inverse modeling applications that target the monitoring of resistivity changes due to CO2 migration and/or leakage. The first study considers a three-dimensional crosswell data set collected at an analogue site used for investigating CO2 distribution and geochemical reactivity within a shallow formation. We invert both resistance and phase data using a gradient-weighting method for descent-based inversion algorithms. This method essentially steers the search direction in the model space using low-cost non-linear conjugate gradient methods towards the more computationally expensive Gauss-Newton direction. The second study involves ERT data that were collected at the SECARB Cranfield site near Natchez, Mississippi, at depths exceeding 3000 m. We employ a
Zhan, Yu; Sun, Jianteng; Luo, Yuzhou; Pan, Lili; Deng, Xunfei; Wei, Zi; Zhu, Lizhong
2016-03-01
A georeferenced multimedia model was developed for evaluating the emissions and environmental fate of di-2-ethylhexyl phthalate (DEHP) in the Yangtze River Delta (YRD), China. Due to the lack of emission inventories, the emission rates were estimated using the observed concentrations in soil as inputs for the multimedia model solved analytically in an inverse manner. The estimated emission rates were then used to evaluate the environmental fate of DEHP with the regular multimedia modeling approach. The predicted concentrations in air, surface water, and sediment were all consistent with the ranges and spatial variations of observed data. The total emission rate of DEHP in YRD was 13.9 thousand t/year (95% confidence interval: 9.4-23.6), of which urban and rural sources accounted for 47% and 53%, respectively. Soil in rural areas and sediment stored 79% and 13% of the total mass, respectively. The air received 61% of the total emissions of DEHP but was only associated with 0.2% of the total mass due to fast degradation and intensive deposition. We suggest the use of an inverse modeling approach under a tiered risk assessment framework to assist future development and refinement of DEHP emission inventories.
Mesgouez, A.
2018-05-01
The determination of equivalent viscoelastic properties of heterogeneous objects remains challenging in various scientific fields such as (geo)mechanics, geophysics or biomechanics. The present investigation addresses the issue of the identification of effective constitutive properties of a binary object by using a nonlinear and full waveform inversion scheme. The inversion process, without any regularization technique or a priori information, aims at minimizing directly the discrepancy between the full waveform responses of a bi-material viscoelastic cylindrical object and its corresponding effective homogeneous object. It involves the retrieval of five constitutive equivalent parameters. Numerical simulations are performed in a laboratory-scale two-dimensional configuration: a transient acoustic plane wave impacts the object and the diffracted fluid pressure, solid stress or velocity component fields are determined using a semi-analytical approach. Results show that the retrieval of the density and of the real parts of both the compressional and the shear wave velocities have been carried out successfully regarding the number and location of sensors, the type of sensors, the size of the searching space, the frequency range of the incident plane pressure wave, and the change in the geometric or mechanical constitution of the bi-material object. The retrieval of the imaginary parts of the wave velocities can reveal in some cases the limitations of the proposed approach.
Time Domain Waveform Inversion for the Q Model Based on the First-Order Viscoacoustic Wave Equations
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Guowei Zhang
2016-01-01
Full Text Available Propagating seismic waves are dispersed and attenuated in the subsurface due to the conversion of elastic energy into heat. The absorptive property of a medium can be described by the quality factor Q. In this study, the first-order pressure-velocity viscoacoustic wave equations based on the standard linear solid model are used to incorporate the effect of Q. For the Q model inversion, an iterative procedure is then proposed by minimizing an objective function that measures the misfit energy between the observed data and the modeled data. The adjoint method is applied to derive the gradients of the objective function with respect to the model parameters, that is, bulk modulus, density, and Q-related parameter τ. Numerical tests on the crosswell recording geometry indicate the feasibility of the proposed approach for the Q anomaly estimation.
Dadić, Martin
2013-06-01
The increased interest in vacuum tube audio amplifiers led to an increased interest in mathematical modelling of such kind of amplifiers. The main purpose of this paper is to develop a novel global numerical approach in calculation of the harmonic distortion (HD) and intermodulation distortion (IM) of vacuum-triode audio amplifiers, suitable for applications using brute-force of modern computers. Since the 3/2 power law gives only the transcharacteristic inverse of a vacuum triode amplifier, unknown plate currents are determined in this paper iteratively using Newton's method. Using the resulting input/output pairs, harmonic distortions and intermodulations are calculated using discrete Fourier transform and three different analytical methods.
Sharp spatially constrained inversion
DEFF Research Database (Denmark)
Vignoli, Giulio G.; Fiandaca, Gianluca G.; Christiansen, Anders Vest C A.V.C.
2013-01-01
We present sharp reconstruction of multi-layer models using a spatially constrained inversion with minimum gradient support regularization. In particular, its application to airborne electromagnetic data is discussed. Airborne surveys produce extremely large datasets, traditionally inverted...... by using smoothly varying 1D models. Smoothness is a result of the regularization constraints applied to address the inversion ill-posedness. The standard Occam-type regularized multi-layer inversion produces results where boundaries between layers are smeared. The sharp regularization overcomes...... inversions are compared against classical smooth results and available boreholes. With the focusing approach, the obtained blocky results agree with the underlying geology and allow for easier interpretation by the end-user....
Mara, Thierry A.; Fajraoui, Noura; Younes, Anis; Delay, Frederick
2015-02-01
We introduce the concept of maximal conditional posterior distribution (MCPD) to assess the uncertainty of model parameters in a Bayesian framework. Although, Markov Chains Monte Carlo (MCMC) methods are particularly suited for this task, they become challenging with highly parameterized nonlinear models. The MCPD represents the conditional probability distribution function of a given parameter knowing that the other parameters maximize the conditional posterior density function. Unlike MCMC which accepts or rejects solutions sampled in the parameter space, MCPD is calculated through several optimization processes. Model inversion using MCPD algorithm is particularly useful for highly parameterized problems because calculations are independent. Consequently, they can be evaluated simultaneously with a multi-core computer. In the present work, the MCPD approach is applied to invert a 2D stochastic groundwater flow problem where the log-transmissivity field of the medium is inferred from scarce and noisy data. For this purpose, the stochastic field is expanded onto a set of orthogonal functions using a Karhunen-Loève (KL) transformation. Though the prior guess on the stochastic structure (covariance) of the transmissivity field is erroneous, the MCPD inference of the KL coefficients is able to extract relevant inverse solutions.
Time reversal mirror and perfect inverse filter in a microscopic model for sound propagation
Calvo, Hernán L.; Danieli, Ernesto P.; Pastawski, Horacio M.
2007-09-01
Time reversal of quantum dynamics can be achieved by a global change of the Hamiltonian sign (a hasty Loschmidt daemon), as in the Loschmidt Echo experiments in NMR, or by a local but persistent procedure (a stubborn daemon) as in the time reversal mirror (TRM) used in ultrasound acoustics. While the first is limited by chaos and disorder, the last procedure seems to benefit from it. As a first step to quantify such stability we develop a procedure, the perfect inverse filter (PIF), that accounts for memory effects, and we apply it to a system of coupled oscillators. In order to ensure a numerical many-body dynamics intrinsically reversible, we develop an algorithm, the pair partitioning, based on the Trotter strategy used for quantum dynamics. We analyze situations where the PIF gives substantial improvements over the TRM.
Andrews, A. E.; Hu, L.; Thoning, K. W.; Nehrkorn, T.; Mountain, M. E.; Jacobson, A. R.; Michalak, A.; Dlugokencky, E. J.; Sweeney, C.; Worthy, D. E. J.; Miller, J. B.; Fischer, M. L.; Biraud, S.; van der Velde, I. R.; Basu, S.; Tans, P. P.
2017-12-01
CarbonTracker-Lagrange (CT-L) is a new high-resolution regional inverse modeling system for improved estimation of North American CO2 fluxes. CT-L uses footprints from the Stochastic Time-Inverted Lagrangian Transport (STILT) model driven by high-resolution (10 to 30 km) meteorological fields from the Weather Research and Forecasting (WRF) model. We performed a suite of synthetic-data experiments to evaluate a variety of inversion configurations, including (1) solving for scaling factors to an a priori flux versus additive corrections, (2) solving for fluxes at 3-hrly resolution versus at coarser temporal resolution, (3) solving for fluxes at 1o × 1o resolution versus at large eco-regional scales. Our framework explicitly and objectively solves for the optimal solution with a full error covariance matrix with maximum likelihood estimation, thereby enabling rigorous uncertainty estimates for the derived fluxes. In the synthetic-data inversions, we find that solving for weekly scaling factors of a priori Net Ecosystem Exchange (NEE) at 1o × 1o resolution with optimization of diurnal cycles of CO2 fluxes yields faithful retrieval of the specified "true" fluxes as those solved at 3-hrly resolution. In contrast, a scheme that does not allow for optimization of diurnal cycles of CO2 fluxes suffered from larger aggregation errors. We then applied the optimal inversion setup to estimate North American fluxes for 2007-2015 using real atmospheric CO2 observations, multiple prior estimates of NEE, and multiple boundary values estimated from the NOAA's global Eulerian CarbonTracker (CarbonTracker) and from an empirical approach. Our derived North American land CO2 fluxes show larger seasonal amplitude than those estimated from the CarbonTracker, removing seasonal biases in the CarbonTracker's simulated CO2 mole fractions. Independent evaluations using in-situ CO2 eddy covariance flux measurements and independent aircraft profiles also suggest an improved estimation on North
International Nuclear Information System (INIS)
Dupuy, B.
2011-11-01
Seismic wave propagation in multiphasic porous media have various environmental (natural risks, geotechnics, groundwater pollutions...) and resources (aquifers, oil and gas, CO 2 storage...) issues. When seismic waves are crossing a given material, they are distorted and thus contain information on fluid and solid phases. This work focuses on the characteristics of seismic waves propagating in multiphasic media, from the physical complex description to the parameter characterisation by inversion, including 2D numerical modelling of the wave propagation. The first part consists in the description of the physics of multiphasic media (each phase and their interactions), using several up-scaling methods, in order to obtain an equivalent mesoscale medium defined by seven parameters. Thus, in simple porosity saturated media and in complex media (double porosity, patchy saturation, visco-poro-elasticity), I can compute seismic wave propagation without any approximation. Indeed, I use a frequency-space domain for the numerical method, which allows to consider all the frequency dependent terms. The spatial discretization employs a discontinuous finite elements method (discontinuous Galerkin), which allows to take into account complex interfaces.The computation of the seismic attributes (velocities and attenuations) of complex porous media shows strong variations in respect with the frequency. Waveforms, computed without approximation, are strongly different if we take into account the full description of the medium or an homogenisation by averages. The last part of this work deals with the poro-elastic parameters characterisation by inversion. For this, I develop a two-steps method: the first one consists in a classical inversion (tomography, full waveform inversion) of seismograms data to obtain macro-scale parameters (seismic attributes). The second step allows to recover, from the macro-scale parameters, the poro-elastic micro-scale properties. This down-scaling step
Afanasiev, M.; Boehm, C.; van Driel, M.; Krischer, L.; May, D.; Rietmann, M.; Fichtner, A.
2016-12-01
Recent years have been witness to the application of waveform inversion to new and exciting domains, ranging from non-destructive testing to global seismology. Often, each new application brings with it novel wave propagation physics, spatial and temporal discretizations, and models of variable complexity. Adapting existing software to these novel applications often requires a significant investment of time, and acts as a barrier to progress. To combat these problems we introduce Salvus, a software package designed to solve large-scale full-waveform inverse problems, with a focus on both flexibility and performance. Based on a high order finite (spectral) element discretization, we have built Salvus to work on unstructured quad/hex meshes in both 2 or 3 dimensions, with support for P1-P3 bases on triangles and tetrahedra. A diverse (and expanding) collection of wave propagation physics are supported (i.e. coupled solid-fluid). With a focus on the inverse problem, functionality is provided to ease integration with internal and external optimization libraries. Additionally, a python-based meshing package is included to simplify the generation and manipulation of regional to global scale Earth models (quad/hex), with interfaces available to external mesh generators for complex engineering-scale applications (quad/hex/tri/tet). Finally, to ensure that the code remains accurate and maintainable, we build upon software libraries such as PETSc and Eigen, and follow modern software design and testing protocols. Salvus bridges the gap between research and production codes with a design based on C++ mixins and Python wrappers that separates the physical equations from the numerical core. This allows domain scientists to add new equations using a high-level interface, without having to worry about optimized implementation details. Our goal in this presentation is to introduce the code, show several examples across the scales, and discuss some of the extensible design points.
Ye, X.; Lauvaux, T.; Kort, E. A.; Lin, J. C.; Oda, T.; Yang, E.; Wu, D.
2016-12-01
Rapid economic development has given rise to a steady increase of global carbon emissions, which have accumulated in the atmosphere for the past 200 years. Urbanization has concentrated about 70% of the global fossil-fuel CO2 emissions in large metropolitan areas distributed around the world, which represents the most significant anthropogenic contribution to climate change. However, highly uncertain quantifications of urban CO2 emissions are commonplace for numerous cities because of poorly-documented inventories of energy consumption. Therefore, accurate estimates of carbon emissions from global observing systems are a necessity if mitigation strategies are meant to be implemented at global scales. Space-based observations of total column averaged CO2 concentration (XCO2) provide a very promising and powerful tool to quantify urban CO2 fluxes. For the first time, measurements from the Orbiting Carbon Observatory 2 (OCO-2) mission are assimilated in a high resolution inverse modeling system to quantify fossil-fuel CO2 emissions of multiple cities around the globe. The Open-source Data Inventory for Anthropogenic CO2 (ODIAC) emission inventory is employed as a first guess, while the atmospheric transport is simulated using the WRF-Chem model at 1-km resolution. Emission detection and quantification is performed with an Ensemble Kalman Filter method. We demonstrate here the potential of the inverse approach for assimilating thousands of OCO-2 retrievals along tracks near metropolitan areas. We present the detection potential of the system with real-case applications near power plants and present inverse emissions using actual OCO-2 measurements on various urban landscapes. Finally, we will discuss the potential of OCO-2-like satellite instruments for monitoring temporal variations of fossil-fuel CO2 emissions over multiple years, which can provide valuable insights for future satellite observation strategies.
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Matthew Nahorniak
Full Text Available In ecology, as in other research fields, efficient sampling for population estimation often drives sample designs toward unequal probability sampling, such as in stratified sampling. Design based statistical analysis tools are appropriate for seamless integration of sample design into the statistical analysis. However, it is also common and necessary, after a sampling design has been implemented, to use datasets to address questions that, in many cases, were not considered during the sampling design phase. Questions may arise requiring the use of model based statistical tools such as multiple regression, quantile regression, or regression tree analysis. However, such model based tools may require, for ensuring unbiased estimation, data from simple random samples, which can be problematic when analyzing data from unequal probability designs. Despite numerous method specific tools available to properly account for sampling design, too often in the analysis of ecological data, sample design is ignored and consequences are not properly considered. We demonstrate here that violation of this assumption can lead to biased parameter estimates in ecological research. In addition, to the set of tools available for researchers to properly account for sampling design in model based analysis, we introduce inverse probability bootstrapping (IPB. Inverse probability bootstrapping is an easily implemented method for obtaining equal probability re-samples from a probability sample, from which unbiased model based estimates can be made. We demonstrate the potential for bias in model-based analyses that ignore sample inclusion probabilities, and the effectiveness of IPB sampling in eliminating this bias, using both simulated and actual ecological data. For illustration, we considered three model based analysis tools--linear regression, quantile regression, and boosted regression tree analysis. In all models, using both simulated and actual ecological data, we
Sreelash, K.; Buis, Samuel; Sekhar, M.; Ruiz, Laurent; Kumar Tomer, Sat; Guérif, Martine
2017-03-01
Characterization of the soil water reservoir is critical for understanding the interactions between crops and their environment and the impacts of land use and environmental changes on the hydrology of agricultural catchments especially in tropical context. Recent studies have shown that inversion of crop models is a powerful tool for retrieving information on root zone properties. Increasing availability of remotely sensed soil and vegetation observations makes it well suited for large scale applications. The potential of this methodology has however never been properly evaluated on extensive experimental datasets and previous studies suggested that the qualit