WorldWideScience

Sample records for khz multibeam echo

  1. Detection of heavy oil on the seabed by application of a 400 kHz multibeam echo sounder

    International Nuclear Information System (INIS)

    Wendelboe, G.; Fonseca, L.; Eriksen, M.; Mutschler, M.; Hvidbak, F.

    2009-01-01

    Marine spills of heavy oil that sink to the sea floor can have significant impacts on marine ecosystems. This paper described a program implemented by the United States Coast Guard to improve operational techniques for the detection, monitoring, and recovery of sunken oil. The program has developed an algorithm based on data from a multibeam echo sounder. The algorithm used calibrated backscatter strengths (BS) to produce a mosaic of the seabed. Values below a pre-specified threshold were sorted into groups using morphological filtering techniques. The angular response curves from each group were then analyzed and compared to a reference BS curve for heavy oil. Response curves below the upper bound curve were defined as oil. The algorithm had a 90 per cent accuracy rate at a recent demonstration using oil 6, Tesoro, Sundex, and asphalt samples. It was concluded that processing times per square mile are approximately 12 hours. Further studies will be conducted to reduce computation times by replacing raw beam-formed data with data that originated solely from the region near the seabed. 15 refs., 15 tabs., 18 figs

  2. Seafloor multibeam backscatter calibration experiment: comparing 45°-tilted 38-kHz split-beam echosounder and 30-kHz multibeam data

    Science.gov (United States)

    Ladroit, Yoann; Lamarche, Geoffroy; Pallentin, Arne

    2018-06-01

    Obtaining absolute seafloor backscatter measurements from hydrographic multibeam echosounders is yet to be achieved. We propose a low-cost experiment to calibrate the various acquisition modes of a 30-kHz Kongsberg EM 302 multibeam echosounder in a range of water depths. We use a 38-kHz Simrad EK60 calibrated fisheries split-beam echosounder mounted at 45° angle on the vessel's hull as a reference for the calibration. The processing to extract seafloor backscatter from the EK60 requires bottom detection, ray tracing and motion compensation to obtain acceptable geo-referenced backscatter measurements from this non-hydrographic system. Our experiment was run in Cook Strait, New Zealand, on well-known seafloor patches in shallow, mid, and deep-water depths. Despite acquisition issues due to weather, our results demonstrate the strong potential of such an approach to obtain system's absolute calibration which is required for quantitative use of backscatter strength data.

  3. Seafloor characterisation using echo peak amplitudes of multibeam hydrosweep system - A preliminary study at Arabian Sea

    Digital Repository Service at National Institute of Oceanography (India)

    Chakraborty, B.; Sudhakar, T.

    In this paper an interface to acquire 59-beams echo peak amplitudes of the Hydrosweep Multibeam system is established. The echo peak amplitude values collected at varying seabed provinces of Arabian sea are presented. The study reveals...

  4. An approach towards solving refraction problems in EM1002 multi-beam echo-sounder system

    Digital Repository Service at National Institute of Oceanography (India)

    Fernandes, W.A.

    Multi-beam echo-sounding is the technique in which multiple beams of acoustic in nature are sent down to seabed and upon reflection, they are received back and processed to give depths values. The report is focused to give a brief idea about EM1002...

  5. Using multi-beam echo sounder backscatter data for sediment classification in very shallow water environments

    NARCIS (Netherlands)

    Amiri-Simkooei, A.R.; Snellen, M.; Simons, D.G.

    2009-01-01

    In a recent work described in Ref. [1], an angle-independent methodology was developed to use the multi-beam echo sounder backscatter (MBES) data for the seabed sediment classification. The method employs the backscatter data at a certain angle to obtain the number of sediment classes and to

  6. Analysis of multibeam-hydrosweep echo peaks for seabed characterisation

    Digital Repository Service at National Institute of Oceanography (India)

    Chakraborty, B.; Schenke, H.W.; Kodagali, V.N.; Hagen, R.

    , in general, Gaussian in nature except in the case of the Kainan Maru seamount summit (area D). The outer beams of the Enderby abyssal plain (area C) echo-peak PDF statistics reveal the highest possible large-scale feature dominance. Interestingly, Extremal...

  7. Toward a standard line for use in multibeam echo sounder calibration

    Science.gov (United States)

    Weber, Thomas C.; Rice, Glen; Smith, Michael

    2018-06-01

    A procedure is suggested in which a relative calibration for the intensity output of a multibeam echo sounder (MBES) can be performed. This procedure identifies a common survey line (i.e., a standard line), over which acoustic backscatter from the seafloor is collected with multiple MBES systems or by the same system multiple times. A location on the standard line which exhibits temporal stability in its seafloor backscatter response is used to bring the intensity output of the multiple MBES systems to a common reference. This relative calibration procedure has utility for MBES users wishing to generate an aggregate seafloor backscatter mosaic using multiple systems, revisiting an area to detect changes in substrate type, and comparing substrate types in the same general area but with different systems or different system settings. The calibration procedure is demonstrated using three different MBES systems over 3 different years in New Castle, NH, USA.

  8. User expectations for multibeam echo sounders backscatter strength data-looking back into the future

    Science.gov (United States)

    Lucieer, Vanessa; Roche, Marc; Degrendele, Koen; Malik, Mashkoor; Dolan, Margaret; Lamarche, Geoffroy

    2018-06-01

    With the ability of multibeam echo sounders (MBES) to measure backscatter strength (BS) as a function of true angle of insonification across the seafloor, came a new recognition of the potential of backscatter measurements to remotely characterize the properties of the seafloor. Advances in transducer design, digital electronics, signal processing capabilities, navigation, and graphic display devices, have improved the resolution and particularly the dynamic range available to sonar and processing software manufacturers. Alongside these improvements the expectations of what the data can deliver has also grown. In this paper, we identify these user-expectations and explore how MBES backscatter is utilized by different communities involved in marine seabed research at present, and the aspirations that these communities have for the data in the future. The results presented here are based on a user survey conducted by the GeoHab (Marine Geological and Biological Habitat Mapping) association. This paper summarises the different processing procedures employed to extract useful information from MBES backscatter data and the various intentions for which the user community collect the data. We show how a range of backscatter output products are generated from the different processing procedures, and how these results are taken up by different scientific disciplines, and also identify common constraints in handling MBES BS data. Finally, we outline our expectations for the future of this unique and important data source for seafloor mapping and characterisation.

  9. The Calibration and error analysis of Shallow water (less than 100m) Multibeam Echo-Sounding System

    Science.gov (United States)

    Lin, M.

    2016-12-01

    Multibeam echo-sounders(MBES) have been developed to gather bathymetric and acoustic data for more efficient and more exact mapping of the oceans. This gain in efficiency does not come without drawbacks. Indeed, the finer the resolution of remote sensing instruments, the harder they are to calibrate. This is the case for multibeam echo-sounding systems (MBES). We are no longer dealing with sounding lines where the bathymetry must be interpolated between them to engender consistent representations of the seafloor. We now need to match together strips (swaths) of totally ensonified seabed. As a consequence, misalignment and time lag problems emerge as artifacts in the bathymetry from adjacent or overlapping swaths, particularly when operating in shallow water. More importantly, one must still verify that bathymetric data meet the accuracy requirements. This paper aims to summarize the system integration involved with MBES and identify the various source of error pertaining to shallow water survey (100m and less). A systematic method for the calibration of shallow water MBES is proposed and presented as a set of field procedures. The procedures aim at detecting, quantifying and correcting systematic instrumental and installation errors. Hence, calibrating for variations of the speed of sound in the water column, which is natural in origin, is not addressed in this document. The data which used in calibration will reference International Hydrographic Organization(IHO) and other related standards to compare. This paper aims to set a model in the specific area which can calibrate the error due to instruments. We will construct a procedure in patch test and figure out all the possibilities may make sounding data with error then calculate the error value to compensate. In general, the problems which have to be solved is the patch test's 4 correction in the Hypack system 1.Roll 2.GPS Latency 3.Pitch 4.Yaw. Cause These 4 correction affect each others, we run each survey line

  10. Echo

    Energy Technology Data Exchange (ETDEWEB)

    Harvey, Dustin Yewell [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-01-25

    This document is a white paper marketing proposal for Echo™ is a data analysis platform designed for efficient, robust, and scalable creation and execution of complex workflows. Echo’s analysis management system refers to the ability to track, understand, and reproduce workflows used for arriving at results and decisions. Echo improves on traditional scripted data analysis in MATLAB, Python, R, and other languages to allow analysts to make better use of their time. Additionally, the Echo platform provides a powerful data management and curation solution allowing analysts to quickly find, access, and consume datasets. After two years of development and a first release in early 2016, Echo is now available for use with many data types in a wide range of application domains. Echo provides tools that allow users to focus on data analysis and decisions with confidence that results are reported accurately.

  11. Seabed sediment classification for monitoring underwater nourishments using time series of multi-beam echo-soundings

    Science.gov (United States)

    Gaida, T. C.; Snellen, M.; van Dijk, T. A. G. P.; Simons, D. G.

    2017-12-01

    Coastal erosion induced by natural processes, such as wind, waves, tidal currents, or human interferences endangers human beings, infrastructure, fauna and flora at the oceans and rivers all over the world. In The Netherlands, in particular the North Sea islands are strongly affected by sediment erosion. To protect and stabilize the coastline, beach and shoreface nourishments are frequently performed. Thereby, sediment reservoirs are created that replace the eroded sediments. Increasing the long-term efficiency of coastal protection requires monitoring of the temporal and spatial development of the coastal nourishments. Multi-beam echo-sounders (MBES) allow for detailed and comprehensive investigations of the seabed composition and structure. To investigate the potential of using MBES for monitoring nourishments in a tidal inlet, four MBES surveys per year are carried out at the Dutch Wadden island Ameland. A pre-nourishment MBES survey was performed in April 2017 and the subsequent post-nourishment survey will take place in September 2017. Both surveys are equipped with a Kongsberg EM 2040C dual-head MBES and are supported with extensive grab sampling. In this study the use of MBES backscatter and bathymetry data are considered as an approach for monitoring coastal nourishments. The aim is to develop a monitoring procedure that allows for comparing MBES data taken during different surveys, i.e., with variations in environmental conditions, MBES characteristics and acquisition procedures. Different unsupervised and supervised acoustic seafloor classification techniques are applied to the processed MBES data to classify the seabed sediments. The analysis of the pre-nourishment MBES data indicates that the backscatter and consequently the classification are highly driven by the abundancy of shell fragments. These results will be used as a baseline to investigate the accumulation of the underwater nourishments. Independent grab samples will be used to select the

  12. Bathymetric surveys at highway bridges crossing the Missouri River in Kansas City, Missouri, using a multibeam echo sounder, 2010

    Science.gov (United States)

    Huizinga, Richard J.

    2010-01-01

    Bathymetric surveys were conducted by the U.S. Geological Survey, in cooperation with the Missouri Department of Transportation, on the Missouri River in the vicinity of nine bridges at seven highway crossings in Kansas City, Missouri, in March 2010. A multibeam echo sounder mapping system was used to obtain channel-bed elevations for river reaches that ranged from 1,640 to 1,800 feet long and extending from bank to bank in the main channel of the Missouri River. These bathymetric scans will be used by the Missouri Department of Transportation to assess the condition of the bridges for stability and integrity with respect to bridge scour. Bathymetric data were collected around every pier that was in water, except those at the edge of the water or in extremely shallow water, and one pier that was surrounded by a large debris raft. A scour hole was present at every pier for which bathymetric data could be obtained. The scour hole at a given pier varied in depth relative to the upstream channel bed, depending on the presence and proximity of other piers or structures upstream from the pier in question. The surveyed channel bed at the bottom of the scour hole was between 5 and 50 feet above bedrock. At bridges with drilled shaft foundations, generally there was exposure of the upstream end of the seal course and the seal course often was undermined to some extent. At one site, the minimum elevation of the scour hole at the main channel pier was about 10 feet below the bottom of the seal course, and the sides of the drilled shafts were evident in a point cloud visualization of the data at that pier. However, drilled shafts generally penetrated 20 feet into bedrock. Undermining of the seal course was evident as a sonic 'shadow' in the point cloud visualization of several of the piers. Large dune features were present in the channel at nearly all of the surveyed sites, as were numerous smaller dunes and many ripples. Several of the sites are on or near bends in the river

  13. Determination of nodule coverage parameters using multibeam normal incidence echo characteristics: A study in the Indian Ocean

    Digital Repository Service at National Institute of Oceanography (India)

    Chakraborty, B.; Pathak, D.; Sudhakar, M.; Raju, Y.S.N.

    A study of the echo peak amplitudes from known nodule areas is initiated to observe the acoustic response for varying nodule abundances and number densities. A statistical study of the peak amplitudes from different nodule areas confirms...

  14. Temporal characteristics of coherent flow structures generated over alluvial sand dunes, Mississippi River, revealed by acoustic doppler current profiling and multibeam echo sounding

    Science.gov (United States)

    Czuba, John A.; Oberg, Kevin A.; Best, Jim L.; Parsons, Daniel R.; Simmons, S. M.; Johnson, K.K.; Malzone, C.

    2009-01-01

    This paper investigates the flow in the lee of a large sand dune located at the confluence of the Mississippi and Missouri Rivers, USA. Stationary profiles collected from an anchored boat using an acoustic Doppler current profiler (ADCP) were georeferenced with data from a real-time kinematic differential global positioning system. A multibeam echo sounder was used to map the bathymetry of the confluence and provided a morphological context for the ADCP measurements. The flow in the lee of a low-angle dune shows good correspondence with current conceptual models of flow over dunes. As expected, quadrant 2 events (upwellings of low-momentum fluid) are associated with high backscatter intensity. Turbulent events generated in the lower lee of a dune near the bed are associated with periods of vortex shedding and wake flapping. Remnant coherent structures that advect over the lower lee of the dune in the upper portion of the water column, have mostly dissipated and contribute little to turbulence intensities. The turbulent events that occupy most of the water column in the upper lee of the dune are associated with periods of wake flapping.

  15. The Application of a Multi-Beam Echo-Sounder in the Analysis of the Sedimentation Situation of a Large Reservoir after an Earthquake

    Directory of Open Access Journals (Sweden)

    Zhong-Luan Yan

    2018-04-01

    Full Text Available The Wenchuan Earthquake took place in the upper reach catchment of the Min River. It resulted in large amounts of loose materials gathering in the river channel, leading to changes in the sediment transport system in this area. The Zipingpu Reservoir is the last and the largest reservoir located in the upper reach of the Min River. It is near the epicenter and receives sediment from upstream. This paper puts forward a study on the reservoir sedimentation and storage capacity of the Zipingpu Reservoir, employing a multi-beam echo-sounder system in December 2012. Then, the data were merged with digital line graphics and shuttle radar topography mission data in ArcGIS to build a digital elevation model and triangulate the irregular network of Zipingpu Reservoir. Via the analysis of the bathymetric data, the results show the following: (1 The main channels of the reservoir gradually aggrade to a flat bottom from the deep-cutting valley. Sedimentation forms a reach with a W-shaped longitudinal thalweg profile and an almost zero slope reach in the upstream section of the reservoir due to the natural barrier induced by a landslide; (2 The loss ratios of the wetted cross-section surface are higher than 10% in the upstream section of the reservoir and higher than 40% in the natural barrier area; (3 Comparing the surveyed area storage capacity of December 2012 with March 2008, the Zipingpu Reservoir has lost 15.28% of its capacity at the dead storage water level and 10.49% of its capacity at the flood limit water level.

  16. Quantitative seafloor characterization using angular backscatter data of the multi-beam echo-sounding system - Use of models and model free techniques

    Digital Repository Service at National Institute of Oceanography (India)

    Chakraborty, B.

    processing gain, bottom slope corrections, and bottom insonification area normalisation were proposed to generate angular backscattering strength for modelling to infer bottom roughness parameters. A software package (NORGCOR) for similar purpose... bottom backscatter data from multibeam system. For each seafloor area, processed backscatter strength values [presented in Fig.: I(c)], are binned at intervals of 1° from --45° to +45°, and averaged over the entire dataset (approximately around 100...

  17. Studies on normal incidence backscattering in nodule areas using the multibeam-hydrosweep system

    Digital Repository Service at National Institute of Oceanography (India)

    Pathak, D.; Chakraborty, B.

    The acoustic response from areas of varying nodule abundance and number densities in the Central Indian Ocean has been studied by using the echo peak amplitudes of the normal incidence beam in the Multibeam Hydrosweep system. It is observed...

  18. ECHO virus

    Science.gov (United States)

    ... page: //medlineplus.gov/ency/article/001340.htm ECHO virus To use the sharing features on this page, please enable JavaScript. Enteric cytopathic human orphan (ECHO) viruses are a group of viruses that can lead ...

  19. Technical Note: Detection of gas bubble leakage via correlation of water column multibeam images

    Science.gov (United States)

    Schneider von Deimling, J.; Papenberg, C.

    2012-03-01

    Hydroacoustic detection of natural gas release from the seafloor has been conducted in the past by using singlebeam echosounders. In contrast, modern multibeam swath mapping systems allow much wider coverage, higher resolution, and offer 3-D spatial correlation. Up to the present, the extremely high data rate hampers water column backscatter investigations and more sophisticated visualization and processing techniques are needed. Here, we present water column backscatter data acquired with a 50 kHz prototype multibeam system over a period of 75 seconds. Display types are of swath-images as well as of a "re-sorted" singlebeam presentation. Thus, individual and/or groups of gas bubbles rising from the 24 m deep seafloor clearly emerge in the acoustic images, making it possible to estimate rise velocities. A sophisticated processing scheme is introduced to identify those rising gas bubbles in the hydroacoustic data. We apply a cross-correlation technique adapted from particle imaging velocimetry (PIV) to the acoustic backscatter images. Temporal and spatial drift patterns of the bubbles are assessed and are shown to match very well to measured and theoretical rise patterns. The application of this processing to our field data gives clear results with respect to unambiguous bubble detection and remote bubble rise velocimetry. The method can identify and exclude the main source of misinterpretations, i.e. fish-mediated echoes. Although image-based cross-correlation techniques are well known in the field of fluid mechanics for high resolution and non-inversive current flow field analysis, we present the first application of this technique as an acoustic bubble detector.

  20. Technical Note: Detection of gas bubble leakage via correlation of water column multibeam images

    Directory of Open Access Journals (Sweden)

    J. Schneider von Deimling

    2012-03-01

    Full Text Available Hydroacoustic detection of natural gas release from the seafloor has been conducted in the past by using singlebeam echosounders. In contrast, modern multibeam swath mapping systems allow much wider coverage, higher resolution, and offer 3-D spatial correlation. Up to the present, the extremely high data rate hampers water column backscatter investigations and more sophisticated visualization and processing techniques are needed. Here, we present water column backscatter data acquired with a 50 kHz prototype multibeam system over a period of 75 seconds. Display types are of swath-images as well as of a "re-sorted" singlebeam presentation. Thus, individual and/or groups of gas bubbles rising from the 24 m deep seafloor clearly emerge in the acoustic images, making it possible to estimate rise velocities. A sophisticated processing scheme is introduced to identify those rising gas bubbles in the hydroacoustic data. We apply a cross-correlation technique adapted from particle imaging velocimetry (PIV to the acoustic backscatter images. Temporal and spatial drift patterns of the bubbles are assessed and are shown to match very well to measured and theoretical rise patterns. The application of this processing to our field data gives clear results with respect to unambiguous bubble detection and remote bubble rise velocimetry. The method can identify and exclude the main source of misinterpretations, i.e. fish-mediated echoes. Although image-based cross-correlation techniques are well known in the field of fluid mechanics for high resolution and non-inversive current flow field analysis, we present the first application of this technique as an acoustic bubble detector.

  1. Multibeam Bathymetry Database (MBBDB)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Originally designed for military use, the multibeam echosounder has proved very useful for nautical charting, oceanographic research and modeling, habitat...

  2. Echo project

    DEFF Research Database (Denmark)

    Gfader, Verina; Carson, Rebecca; Kraus, Chris

    2016-01-01

    team to both present the printed matter in the format of running a book stall, and stage a discursive event at the Classroom. Echo reverberates some of the encounters and debates there, with new commissioned chapters propelling a ongoing correspondence across urban environs: An essay on the General...... mothers and demonology (Kathy Acker’s property deals in the UK), and more; and future materials formalized as poster texts . . ....

  3. Shallow water acoustic backscatter and reverberation measurements using a 68-kHz cylindrical array: a dissertation

    OpenAIRE

    Gallaudet, Timothy C. (Timothy Cole), 1967-

    2001-01-01

    The characterization of high frequency, shallow water acoustic backscatter and reverberation is important because acoustic systems are used in many scientific, commercial, and military applications. The approach taken is to use data collected by the Toroidal Volume Search Sonar (TVSS), a 68 kHz multibeam sonar capable of 360 deg imaging in a vertical plane perpendicular to its direction of travel. With this unique capability, acoustic backscatter imagery of the seafloor, sea surface, and hori...

  4. Seasonal and diel patterns in sedimentary flux of krill fecal pellets recorded by an echo sounder

    KAUST Repository

    Rø stad, Anders; Kaartvedt, Stein

    2013-01-01

    We used a moored upward-facing 200 kHz echo sounder to address sedimentation of fecal pellets (FPs) from dielly migrating Meganyctiphanes norvegica. The echo sounder was located on the bottom at 150 m depth in the Oslofjord, Norway, and was cabled

  5. AFSC/ABL: Multibeam Database

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Seafloor habitat maps have been created using high resolution multibeam sonar with co-registered backscatter at various locations throughout the Alaska Region. Most...

  6. Multibeam fiber laser cutting

    DEFF Research Database (Denmark)

    Olsen, Flemming Ove; Hansen, Klaus Schütt; Nielsen, Jakob Skov

    2009-01-01

    The appearance of the high power high brilliance fiber laser has opened for new possibilities in laser materials processing. In laser cutting this laser has demonstrated high cutting performance compared to the dominating Cutting laser, the CO2 laser. However, quality problems in fiber......-laser cutting have until now limited its application to metal cutting. In this paper the first results of proof-of-principle Studies applying a new approach (patent pending) for laser cutting with high brightness and short wavelength lasers will be presented. In the approach, multibeam patterns are applied...... to control the melt flow out of the cut kerf resulting in improved cut quality in metal cutting. The beam patterns in this study are created by splitting up beams from two single mode fiber lasers and combining these beams into a pattern in the cut kerf. The results are obtained with a total of 550 W...

  7. ECHO Gov Login | ECHO | US EPA

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  8. Grating stimulated echo

    International Nuclear Information System (INIS)

    Dubetsky, B.; Berman, P.R.; Sleator, T.

    1992-01-01

    A theory of a grating simulated echo (GTE) is developed. The GSE involves the sequential excitation of atoms by two counterpropagating traveling waves, a standing wave, and a third traveling wave. It is shown that the echo signal is very sensitive to small changes in atomic velocity, much more sensitive than the normal stimulated echo. Use of the GSE as a collisional probe or accelerometer is discussed

  9. Horizontal maps of echo power in the lower stratosphere using the MU radar

    Directory of Open Access Journals (Sweden)

    M. Hirono

    2004-03-01

    Full Text Available In recent works, zenithal and azimuthal angle variations of echo power measured by VHF Stratosphere-Troposphere (ST radars have been analyzed in detail using different radar multi-beam configurations. It was found that the azimuthal angle corresponding to maximum echo power is closely related to the direction of the horizontal wind shear. These properties indicate that local wind shear affects the tilt of the scatterers. Moreover, horizontal maps of echo power collected using a large set of beams steered pulse-to-pulse up to 40 degrees off zenith revealed that the power distribution pattern in the troposphere is often skewed. In this work, a three-dimensional description of echo power variations up to 24 degrees off zenith is shown for measurements in the lower stratosphere (i.e. up to approximately 20km using a "sequential multi-beam" (SMB configuration. Such a description was not possible above the tropopause with classical multi-beam configurations because of the loss of radar sensitivity due to the limited integration time by the use of a large number of beams. This work attempts to complete previous descriptions of the phenomenon by some observations in the lower stratosphere discussed in association with complementary balloon measurements. Key words. Meteorology and atmospheric dynamics (turbulence – Radio Science (remote sensing

  10. Horizontal maps of echo power in the lower stratosphere using the MU radar

    Directory of Open Access Journals (Sweden)

    M. Hirono

    2004-03-01

    Full Text Available In recent works, zenithal and azimuthal angle variations of echo power measured by VHF Stratosphere-Troposphere (ST radars have been analyzed in detail using different radar multi-beam configurations. It was found that the azimuthal angle corresponding to maximum echo power is closely related to the direction of the horizontal wind shear. These properties indicate that local wind shear affects the tilt of the scatterers. Moreover, horizontal maps of echo power collected using a large set of beams steered pulse-to-pulse up to 40 degrees off zenith revealed that the power distribution pattern in the troposphere is often skewed. In this work, a three-dimensional description of echo power variations up to 24 degrees off zenith is shown for measurements in the lower stratosphere (i.e. up to approximately 20km using a "sequential multi-beam" (SMB configuration. Such a description was not possible above the tropopause with classical multi-beam configurations because of the loss of radar sensitivity due to the limited integration time by the use of a large number of beams. This work attempts to complete previous descriptions of the phenomenon by some observations in the lower stratosphere discussed in association with complementary balloon measurements.

    Key words. Meteorology and atmospheric dynamics (turbulence – Radio Science (remote sensing

  11. Study of multibeam techniques for bathymetry and seabottom backscatter applications

    Digital Repository Service at National Institute of Oceanography (India)

    Nair, R.R.; Chakraborty, B.

    Indian ocean is presented using Hydrosweep-multibeam installed onboard ORV Sagarkanya. A seabottom classification model is proposed which can be applied for multibeam backscatter data. Certain aspects of the multibeam backscatter signal data processing...

  12. Happy birthday Echo!

    CERN Document Server

    Staff Association

    2010-01-01

    You are reading the number hundred and one (no. 101) edition of our bulletin Echo. Just over four years ago, on 27th March 2006, the first untitled edition was published (Fig. 1 on the left). The title Echo appeared on the second edition on 10th April 2006 (Fig. 1 in the centre). Today (see Fig. 1 on the right), the layout is slightly different, but the structure of each edition has remained more or less the same: an editorial informing you of the important issues, followed by articles on club life, cultural activities (exhibitions and conferences), information from GAC-EPA, and special offers for our members.     Fig. 1 : Nos. 1, 2 and 100 of our twice-monthly publication Echo Echo was created in March 2006 when, much to our regret, CERN official communication and that of your representatives were separated. November 2009 saw a return to normal practice, and since then the CERN st...

  13. Zonal asymmetry of daytime 150-km echoes observed by Equatorial Atmosphere Radar in Indonesia

    Directory of Open Access Journals (Sweden)

    T. Yokoyama

    2009-03-01

    Full Text Available Multi-beam observations of the daytime ionospheric E-region irregularities and the so-called 150-km echoes with the 47-MHz Equatorial Atmosphere Radar (EAR in West Sumatra, Indonesia (0.20° S, 100.32° E, 10.36° S dip latitude are presented. 150-km echoes have been frequently observed by the EAR, and their characteristics are basically the same as the equatorial ones, except for an intriguing zonal asymmetry; stronger echoes in lower altitudes in the east directions, and weaker echoes in higher altitudes in the west. The highest occurrence is seen at 5.7° east with respect to the magnetic meridian, and the altitude gradually increases as viewing from the east to west. Arc structures which return backscatter echoes are proposed to explain the asymmetry. While the strength of radar echoes below 105 km is uniform within the wide coverage of azimuthal directions, the upper E-region (105–120 km echoes also show a different type of zonal asymmetry, which should be generated by an essentially different mechanism from the lower E-region and 150-km echoes.

  14. Multibeam sonar backscatter data processing

    Science.gov (United States)

    Schimel, Alexandre C. G.; Beaudoin, Jonathan; Parnum, Iain M.; Le Bas, Tim; Schmidt, Val; Keith, Gordon; Ierodiaconou, Daniel

    2018-06-01

    Multibeam sonar systems now routinely record seafloor backscatter data, which are processed into backscatter mosaics and angular responses, both of which can assist in identifying seafloor types and morphology. Those data products are obtained from the multibeam sonar raw data files through a sequence of data processing stages that follows a basic plan, but the implementation of which varies greatly between sonar systems and software. In this article, we provide a comprehensive review of this backscatter data processing chain, with a focus on the variability in the possible implementation of each processing stage. Our objective for undertaking this task is twofold: (1) to provide an overview of backscatter data processing for the consideration of the general user and (2) to provide suggestions to multibeam sonar manufacturers, software providers and the operators of these systems and software for eventually reducing the lack of control, uncertainty and variability associated with current data processing implementations and the resulting backscatter data products. One such suggestion is the adoption of a nomenclature for increasingly refined levels of processing, akin to the nomenclature adopted for satellite remote-sensing data deliverables.

  15. Rotary spin echoes

    Energy Technology Data Exchange (ETDEWEB)

    Solomon, I. [Commissariat a l' energie atomique et aux energies alternatives - CEA, Centre d' Etudes Nucleaires de Saclay, BP2, Gif-sur-Yvette (France)

    1959-07-01

    Torrey has observed the free precession of nuclear spins around an r-f field H{sub 1}, fixed in a frame rotating at the Larmor frequency ω{sub 0} = γH{sub 0} around a large d-c magnetic field H{sub 0}. He showed that for an H{sub 1}, much larger than inhomogeneity of H{sub 0}, the latter has a negligible effect on the decay of the spin magnetization which is mainly due to the inhomogeneity of H{sub 1}. We report here on a method of overcoming the inhomogeneity of H{sub 1}, by production of echoes in the rotating frame ('rotary echoes'). These echoes are obtained by a 180 deg. phase shift at t = τ on the r-f field so that H{sub 1}, is suddenly reversed, producing a re-focussing of the magnetization vectors at the time t = 2 τ. The rotary echoes so obtained are very similar to the usual spin-echoes with, however some specific features that make them particularly suitable for the measurement of long relaxation times. Reprint of a paper published in Physical Review Letters, vol. 2, no. 7, Apr 1959, p. 301-302.

  16. Rotary spin echoes

    International Nuclear Information System (INIS)

    Solomon, I.

    1959-01-01

    Torrey has observed the free precession of nuclear spins around an r-f field H 1 , fixed in a frame rotating at the Larmor frequency ω 0 = γH 0 around a large d-c magnetic field H 0 . He showed that for an H 1 , much larger than inhomogeneity of H 0 , the latter has a negligible effect on the decay of the spin magnetization which is mainly due to the inhomogeneity of H 1 . We report here on a method of overcoming the inhomogeneity of H 1 , by production of echoes in the rotating frame ('rotary echoes'). These echoes are obtained by a 180 deg. phase shift at t = τ on the r-f field so that H 1 , is suddenly reversed, producing a re-focussing of the magnetization vectors at the time t = 2 τ. The rotary echoes so obtained are very similar to the usual spin-echoes with, however some specific features that make them particularly suitable for the measurement of long relaxation times. Reprint of a paper published in Physical Review Letters, vol. 2, no. 7, Apr 1959, p. 301-302

  17. Fast spin-echo imaging

    International Nuclear Information System (INIS)

    Mackey, K.; Zoarski, G.; Bentson, J.R.; Lufkin, R.B.; Melki, P.; Jolesz, F.

    1991-01-01

    This paper reports on a partial radio-frequency (RF) echo-planar pulse sequence called contiguous slice fast spin echo (CSFSE) which is undergoing clinical trials for spine MR imaging. In this variation of rapid acquisition relaxation enhanced (RARE) spin-echo imaging, rapid 180 degrees RF pulse generated refocused echoes, producing T2-weighted images in about one-third the time of conventional double-echo technique. Forty patients with suspected pathology of the spine were imaged with conventional double-echo and closely matched CSFSE techniques on a GE Signa 1.5-T Advantage system. Cases were reviewed by two board-certified neuroradiologists. In all cases the CSFSE images were of equal or superior quality compared with those obtained with the conventional double-echo technique. Pathologic processes that were imaged consisted of inflammatory, neoplastic, posttraumatic, and degenerative conditions

  18. Near-bottom Multibeam Survey Capabilities in the US National Deep Submergence Facility (Invited)

    Science.gov (United States)

    Yoerger, D. R.; McCue, S. J.; Jason; Sentry Operations Groups

    2010-12-01

    The US National Deep Submergence Facility (NDSF) provides near-bottom multibeam mapping capabilities from the autonomous underwater vehicle Sentry and the remotely operated vehicle Jason. These vehicles can be used to depths of 4500 and 6500m respectively. Both vehicles are equipped with Reson 7125 400khz multibeam sonars as well as compatible navigation equipment (inertial navigation systems, doppler velocity logs, and acoustic navigation systems). These vehicles have produced maps of rugged Mid-Ocean Ridge terrain in the Galapagos Rift, natural oil and gas seeps off the coast of Southern California, deep coral sites in the Gulf of Mexico, and sites for the Ocean Observing Initiative off the coast of Oregon. Multibeam surveys are conducted from heights between 20 and 80 meters, allowing the scientific user to select the tradeoff between resolution and coverage rate. In addition to conventional bathymetric mapping, the systems have used to image methane bubble plumes from natural seeps. This talk will provide summaries of these mapping efforts and describe the data processing pipeline used to produce maps shortly after each dive. Development efforts to reduce navigational errors and reconcile discrepancies between adjacent swaths will also be described.

  19. Multibeam radiation therapy treatment application

    International Nuclear Information System (INIS)

    Manens, J.P.; Le Gall, G.; Chenal, C.; Ben Hassel, M.; Fresne, F.; Barillot, C.; Gibaud, B.; Lemoine, D.; Bouliou, A.; Scarabin, J.M.

    1991-01-01

    A software package has been developed for multibeam radiation therapy treatment application. We present in this study a computer-assisted dosimetric planning procedure which includes: i), an analytical stage for setting up the large volume via 2D and 3D displays; ii), a planning stage for issue of a treatment strategy including dosimetric simulations; and iii), a treatment stage to drive the target volume to the radiation unit isocenter. The combined use of stereotactic methods and multimodality imagery ensures spatial coherence and makes target definition and cognition of structure environment more accurate. The dosimetric planning suited to the spatial reference (the stereotactic frame) guarantees optimal distribution of the dose, computed by the original 3D volumetric algorithm. A computer-driven chair-framework cluster was designed to position the target volume at the radiation unit isocenter [fr

  20. Dissecting a Light Echo

    Science.gov (United States)

    2008-01-01

    [figure removed for brevity, see original site] Click on image for audio animation This animation illustrates how a light echo works, and how an optical illusion of material moving outward is created. A light echo occurs when a star explodes, acting like a cosmic flashbulb. The light from this explosion zips through nearby dust clumps, illuminating and heating them up slightly. This brief period of warming causes them to glow in infrared, like a chain of Christmas bulbs lighting up one by one. The animation starts by showing the explosion of a star, which results in a flash of light that moves outward in all directions. The direction of our line of sight from Earth is indicated by the blue arrow. When the light flash reaches surrounding dust, shown here as three dark clouds, the dust is heated up, creating infrared light that begins to travel toward Earth (indicated by the red arrows). Dust closest to the explosion lights up first, while the explosion's shock wave takes longer to reach more distant material. This results in light from different parts of the cloud reaching Earth at different times, creating the illusion of motion over time. As the animation shows, the inclination of the cloud toward our line of sight can result in the material seeming to move both away from and toward the central star.

  1. Comparing microbubble cavitation at 500 kHz and 70 kHz related to micellar drug delivery using ultrasound.

    Science.gov (United States)

    Diaz de la Rosa, Mario A; Husseini, Ghaleb A; Pitt, William G

    2013-02-01

    We have previously reported that ultrasonic drug release at 70kHz was found to correlate with the presence of subharmonic emissions. No evidence of drug release or of the subharmonic emissions were detected in experiments at 500kHz. In an attempt to understand the difference in drug release behavior between low- and mid-frequency ultrasound, a mathematical model of a bubble oscillator was developed to explore the difference in the behavior of a single 10-μm bubble under 500- and 70-kHz ultrasound. The dynamics were found to be fundamentally different; the 500-kHz bubble follows a period-doubling route to chaos while a 70-kHz bubble follows an intermittent route to chaos. We propose that this type of "intermittent subharmonic" oscillation behavior is associated with the drug release observed experimentally. Copyright © 2012 Elsevier B.V. All rights reserved.

  2. The Multibeam Advisory Committee (MAC): a search for solutions for collecting consistent high quality multibeam data across multiple ships, systems, and operators in the U.S. Academic Fleet.

    Science.gov (United States)

    Johnson, P. D.; Ferrini, V. L.; Jerram, K.

    2016-12-01

    In 2015 the National Science Foundation funded the University of New Hampshire's Center for Coastal and Ocean Mapping and Lamont-Doherty Earth Observatory, for the second time, to coordinate the effort of standardizing the quality of multibeam echosounder (MBES) data across the U.S. academic fleet. This effort supports 9 different ship operating institutions who manage a total of 12 multibeam-equipped ships carrying 6 different MBES systems, manufactured by two different companies. These MBES are designed to operate over a very wide range of depths and operational modes. The complexity of this endeavor led to the creation of the Multibeam Advisory Committee (MAC), a team of academic and industry experts whose mission is to support the needs of the U.S academic fleet's multibeam echo sounders through all of the phases of the "life" of a MBES system and its data, from initial acceptance of the system, to recommendations on at-sea acquisition of data, to validation of already installed systems, and finally to the post-survey data evaluation. The main activities of the MAC include 1.) standardizing both the Shipboard Acceptance Testing of all new systems and Quality Assurance Testing of already installed systems, 2.) working with the both the ship operators/technicians and the manufacturers of the multibeam systems to guarantee that each MBES is working at its peak performance level, 3.) developing tools that aid in the collection of data, assessment of the MBES hardware, and evaluation of the quality of the MBES data, 4.) creating "best practices" documentation concerning data acquisition and workflow, and 5.) providing a website, http://mac.unols.org, to host technical information, tools, reports, and a "help desk" for operators of the systems to ask questions concerning issues that they see with their systems.

  3. Auv Multibeam Bathymetry and Sidescan Survey of the SS Montebello wreck Offshore Cambria CA

    Science.gov (United States)

    Caress, D. W.; Thomas, H.; Conlin, D.; Thompson, D.; Paull, C. K.

    2010-12-01

    An MBARI Mapping AUV survey of the SS Montebello wreck offshore Cambria, CA collected high-resolution multibeam bathymetry and sidescan imagery of the vessel and the surrounding seafloor. The Montebello was an oil tanker that was torpedoed and sunk about 11 km offshore in 275 m water depth by a Japanese submarine on December 23, 1941. The Montebello was loaded with 3,000,000 gallons of crude oil, and there is no evidence that significant leakage of that cargo occurred at the time of the sinking or in the 69 years since. The California Department of Fish and Game’s Office of Spill Prevention and Response (OSPR) commissioned the AUV survey as part of a multi-agency Montebello Task Force effort to assess the potential pollution threat. The survey data will be used to determine the extent and general character of the wreckage for a pending Task Force report and to guide any future ROV dive or assessment activity . The AUV surveyed the wreck site from altitudes of 75 and 25 m; the low-altitude high-resolution survey consists of a grid with a 50 m line spacing both parallel and orthogonal to the ship. The 200 kHz multibeam bathymetry images the wreck from both above and from the sides with an 0.5 m lateral resolution. The combination of soundings from all of the survey lines results in a three-dimensional distribution of soundings that delineates the external morphology and some of the internal structure of the wreck. 410 kHz chirp sidescan sonar data also image the site from both directions. The bathymetry data indicate that the Montebello was pitched forward down when it impacted the bottom, crushing and breaking off the bow section. Both forward and aft deckhouses are largely intact, and in fact the multibeam images the individual decks within those structures. About half of the forward mast remains, both amidships masts and the smokestack are missing. A good deal of the deck piping and equipment appears intact, and aside from the bow, the ship’s sides appear

  4. Help Content for ECHO Reports | ECHO | US EPA

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  5. ECHO-UseFY17.png | ECHO | US EPA

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  6. Shallow water acoustic backscatter and reverberation measurements using a 68-kHz cylindrical array

    Science.gov (United States)

    Gallaudet, Timothy Cole

    2001-10-01

    The characterization of high frequency, shallow water acoustic backscatter and reverberation is important because acoustic systems are used in many scientific, commercial, and military applications. The approach taken is to use data collected by the Toroidal Volume Search Sonar (TVSS), a 68 kHz multibeam sonar capable of 360° imaging in a vertical plane perpendicular to its direction of travel. With this unique capability, acoustic backscatter imagery of the seafloor, sea surface, and horizontal and vertical planes in the volume are constructed from data obtained in 200m deep waters in the Northeastern Gulf of Mexico when the TVSS was towed 78m below the surface, 735m astern of a towship. The processed imagery provide a quasi-synoptic characterization of the spatial and temporal structure of boundary and volume acoustic backscatter and reverberation. Diffraction, element patterns, and high sidelobe levels are shown to be the most serious problems affecting cylindrical arrays such as the TVSS, and an amplitude shading method is presented for reducing the peak sidelobe levels of irregular-line and non-coplanar arrays. Errors in the towfish's attitude and motion sensor, and irregularities in the TVSS's transmitted beampattern produce artifacts in the TVSS-derived bathymetry and seafloor acoustic backscatter imagery. Correction strategies for these problems are described, which are unique in that they use environmental information extracted from both ocean boundaries. Sea surface and volume acoustic backscatter imagery are used to explore and characterize the structure of near-surface bubble clouds, schooling fish, and zooplankton. The simultaneous horizontal and vertical coverage provided by the TVSS is shown to be a primary advantage, motivating further use of multibeam sonars in these applications. Whereas boundary backscatter fluctuations are well described by Weibull, K, and Rayleigh mixture probability distributions, those corresponding to volume backscatter are

  7. Custom Search | ECHO | US EPA

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  8. Watershed Statistics | ECHO | US EPA

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  9. Rapid Gradient-Echo Imaging

    Science.gov (United States)

    Hargreaves, Brian

    2012-01-01

    Gradient echo sequences are widely used in magnetic resonance imaging (MRI) for numerous applications ranging from angiography to perfusion to functional MRI. Compared with spin-echo techniques, the very short repetition times of gradient-echo methods enable very rapid 2D and 3D imaging, but also lead to complicated “steady states.” Signal and contrast behavior can be described graphically and mathematically, and depends strongly on the type of spoiling: fully balanced (no spoiling), gradient spoiling, or RF-spoiling. These spoiling options trade off between high signal and pure T1 contrast while the flip angle also affects image contrast in all cases, both of which can be demonstrated theoretically and in image examples. As with spin-echo sequences, magnetization preparation can be added to gradient-echo sequences to alter image contrast. Gradient echo sequences are widely used for numerous applications such as 3D perfusion imaging, functional MRI, cardiac imaging and MR angiography. PMID:23097185

  10. Acoustic structure and echo character of surficial sediments of the northern Hatteras Abyssal Plain

    International Nuclear Information System (INIS)

    McCreery, C.J.; Laine, E.P.

    1986-05-01

    A study has been made of the high frequency acoustic response of abyssal plain depositional facies. Piston cores have been obtained at six stations and deep hydrophone recordings at three stations on the northern Hatteras Abyssal Plain. 3.5 kHz seismic profiles indicate acoustically transparent lobes of surficial sediment which thicken towards the Hatteral Transverse Canyon and Sohm Gap/Wilmington Fan. Physical property data from piston cores indicate a higher percentage of coarse sediment in the areas of transparent acoustic response. Many of the characteristics normally used in mapping of conventional 3.5 kHz profiler acoustic response varied only slightly in the study area. Regions of diffuse 3.5 kHz surface echoes, similar to prolonged echoes attributed to high percent sand beds, have been identified in the study area. High trace to trace variation in deep hydrophone/pinger recordings in these areas suggests that the diffuse echo returns are due to unresolved microtopography and are not necessarily associated with a sandy seafloor

  11. NASA Rat Acoustic Tolerance Test 1994-1995: 8 kHz, 16 kHz, 32 kHz Experiments

    Science.gov (United States)

    Mele, Gary D.; Holley, Daniel C.; Naidu, Sujata

    1996-01-01

    Adult male Sprague-Dawley rats were exposed to chronic applied sound (74 to 79 dB, SPL) with octave band center frequencies of either 8, 16 or 32 kHz for up to 60 days. Control cages had ambient sound levels of about 62 dB (SPL). Groups of rats (test vs. control; N=9 per group) were euthanized after 0. 5. 14, 30, and 60 days. On each euthanasia day, objective evaluation of their physiology and behavior was performed using a Stress Assessment Battery (SAB) of measures. In addition, rat hearing was assessed using the brain stem auditory evoked potential (BAER) method after 60 days of exposure. No statistically significant differences in mean daily food use could be attributed to the presence of the applied test sound. Test rats used 5% more water than control rats. In the 8 kHz and 32 kHz tests this amount was statistically significant(P less than .05). This is a minor difference of questionable physiological significance. However, it may be an indication of a small reaction to the constant applied sound. Across all test frequencies, day 5 test rats had 6% larger spleens than control rats. No other body or organ weight differences were found to be statistically significant with respect to the application of sound. This spleen effect may be a transient adaptive process related to adaptation to the constant applied noise. No significant test effect on differential white blood cell counts could be demonstrated. One group demonstrated a low eosinophil count (16 kHz experiment, day 14 test group). However this was highly suspect. Across all test frequencies studied, day 5 test rats had 17% fewer total leukocytes than day 5 control rats. Sound exposed test rats exhibited 44% lower plasma corticosterone concentrations than did control rats. Note that the plasma corticosterone concentration was lower in the sound exposed test animals than the control animals in every instance (frequency exposure and number of days exposed).

  12. Validation of automated supervised segmentation of multibeam backscatter data from the Chatham Rise, New Zealand

    Science.gov (United States)

    Hillman, Jess I. T.; Lamarche, Geoffroy; Pallentin, Arne; Pecher, Ingo A.; Gorman, Andrew R.; Schneider von Deimling, Jens

    2018-06-01

    Using automated supervised segmentation of multibeam backscatter data to delineate seafloor substrates is a relatively novel technique. Low-frequency multibeam echosounders (MBES), such as the 12-kHz EM120, present particular difficulties since the signal can penetrate several metres into the seafloor, depending on substrate type. We present a case study illustrating how a non-targeted dataset may be used to derive information from multibeam backscatter data regarding distribution of substrate types. The results allow us to assess limitations associated with low frequency MBES where sub-bottom layering is present, and test the accuracy of automated supervised segmentation performed using SonarScope® software. This is done through comparison of predicted and observed substrate from backscatter facies-derived classes and substrate data, reinforced using quantitative statistical analysis based on a confusion matrix. We use sediment samples, video transects and sub-bottom profiles acquired on the Chatham Rise, east of New Zealand. Inferences on the substrate types are made using the Generic Seafloor Acoustic Backscatter (GSAB) model, and the extents of the backscatter classes are delineated by automated supervised segmentation. Correlating substrate data to backscatter classes revealed that backscatter amplitude may correspond to lithologies up to 4 m below the seafloor. Our results emphasise several issues related to substrate characterisation using backscatter classification, primarily because the GSAB model does not only relate to grain size and roughness properties of substrate, but also accounts for other parameters that influence backscatter. Better understanding these limitations allows us to derive first-order interpretations of sediment properties from automated supervised segmentation.

  13. Analysis of seafloor backscatter strength dependence on the survey azimuth using multibeam echosounder data

    Science.gov (United States)

    Lurton, Xavier; Eleftherakis, Dimitrios; Augustin, Jean-Marie

    2018-06-01

    The sediment backscatter strength measured by multibeam echosounders is a key feature for seafloor mapping either qualitative (image mosaics) or quantitative (extraction of classifying features). An important phenomenon, often underestimated, is the dependence of the backscatter level on the azimuth angle imposed by the survey line directions: strong level differences at varying azimuth can be observed in case of organized roughness of the seabed, usually caused by tide currents over sandy sediments. This paper presents a number of experimental results obtained from shallow-water cruises using a 300-kHz multibeam echosounder and specially dedicated to the study of this azimuthal effect, with a specific configuration of the survey strategy involving a systematic coverage of reference areas following "compass rose" patterns. The results show for some areas a very strong dependence of the backscatter level, up to about 10-dB differences at intermediate oblique angles, although the presence of these ripples cannot be observed directly—neither from the bathymetry data nor from the sonar image, due to the insufficient resolution capability of the sonar. An elementary modeling of backscattering from rippled interfaces explains and comforts these observations. The consequences of this backscatter dependence upon survey azimuth on the current strategies of backscatter data acquisition and exploitation are discussed.

  14. Geomorphology, acoustic backscatter, and processes in Santa Monica Bay from multibeam mapping.

    Science.gov (United States)

    Gardner, James V; Dartnell, Peter; Mayer, Larry A; Hughes Clarke, John E

    2003-01-01

    Santa Monica Bay was mapped in 1996 using a high-resolution multibeam system, providing the first substantial update of the submarine geomorphology since the initial compilation by Shepard and Emery [(1941) Geol. Soc. Amer. Spec. Paper 31]. The multibeam mapping generated not only high-resolution bathymetry, but also coregistered, calibrated acoustic backscatter at 95 kHz. The geomorphology has been subdivided into six provinces; shelf, marginal plateau, submarine canyon, basin slope, apron, and basin. The dimensions, gradients, and backscatter characteristics of each province is described and related to a combination of tectonics, climate, sea level, and sediment supply. Fluctuations of eustatic sea level have had a profound effect on the area; by periodically eroding the surface of Santa Monica plateau, extending the mouth of the Los Angeles River to various locations along the shelf break, and by connecting submarine canyons to rivers. A wetter glacial climate undoubtedly generated more sediment to the rivers that then transported the increased sediment load to the low-stand coastline and canyon heads. The trends of Santa Monica Canyon and several bathymetric highs suggest a complex tectonic stress field that has controlled the various segments. There is no geomorphic evidence to suggest Redondo Canyon is fault controlled. The San Pedro fault can be extended more than 30 km to the northwest by the alignment of a series of bathymetric highs and abrupt changes in direction of channel thalwegs.

  15. Remote acoustic seafloor characterization using numerical model and statistical based stochastic multifractals

    Digital Repository Service at National Institute of Oceanography (India)

    Chakraborty, B.; Haris, K.

    The spatial variability of sediment geoacoustic inversion parameters are estimated employing bathymetric systems such as multi-beam echo-sounder (MBES) and dualfrequency single-beam echo-sounder (SBES) operable at 95 kHz and 33/210 kHz, respectively...

  16. Applications of KHZ-CW Lidar in Ecological Entomology

    Science.gov (United States)

    Malmqvist, Elin; Brydegaard, Mikkel

    2016-06-01

    The benefits of kHz lidar in ecological entomology are explained. Results from kHz-measurements on insects, carried out with a CW-lidar system, employing the Scheimpflug principle to obtain range resolution, are presented. A method to extract insect events and analyze the large amount of lidar data is also described.

  17. Echo phenomena in a plasma

    International Nuclear Information System (INIS)

    Pavlenko, V.N.

    1983-01-01

    The mechanism of echo phenomenon in different plasma media: laboratory and cosmic plasma, metals and semiconductors is analyzed to get a more comprehensive idea on collective processes in a plasma and for practical applications in radiophysics and plasma diagnostics. The echo phenomenon permitted to confirm a reversible nature of the Landau damping, to prove the fact that the information on perturbation is conserved in a plasma (as non-damping oscillations of the distribution function) even after disappearing of the macroscopic field. The dependence of the diffusion coefficient on the velocity is measured, microturbulences in a plasma are investigated. New ways of the plasma wave conversion are suggested, as well as ''lightning'' of super-critical plasma layers and regions of plasma non-transparency. Prospective advantages of using echo for studying the mechanisms of charged particle interaction with the surface bounding a plasma are revealed

  18. Hydroacoustic detection of dumped ammunition in the Ocean with multibeam snippet backscatter analyses. A case study from the 'Kolberger Heide' ammunition dump site (Baltic Sea, Germany)

    Science.gov (United States)

    Kunde, Tina; Schneider von Deimling, Jens

    2016-04-01

    Dumped ammunition in the sea is a matter of great concern in terms of safe navigation and environmental threads. Because corrosion of the dumped ammunition's hull is ongoing, future contamination of the ambient water by their toxic interior is likely to occur. The location of such dump sites is approximately known from historical research and ship log book analyses. Subsequent remote sensing of ammunition dumping sites (e.g. mines) on the seafloor is preferentially performed with hydro-acoustic methods such as high resolution towed side scan or by the sophisticated synthetic aperture sonar approach with autonomous underwater vehicles. However, these are time consuming and expensive procedures, while determining the precise position of individual mines remains a challenging task. To mitigate these shortcomings we suggest using ship-born high-frequency multibeam sonar in shallow water to address the task of mine detection and precise localization on the seabed. Multibeam sonar systems have improved their potential in regard to backscatter analyses significantly over the past years and nowadays present fast and accurate tools for shallow water surveying to (1) detect mines in multibeam snippet backscatter data (2) determine their precise location with high accuracy intertial navigation systems. A case study was performed at the prominent ammunition dumping site 'Kolberger Heide' (Baltic Sea, Germany) in the year 2014 using a modern hydro-acoustic multibeam echosounder system with 200-400 kHz (KONGSBERG EM2040c). With an average water depth of not even 20 m and the proximity to the shore line and dense waterways, this investigated area requires permanent navigational care. Previously, the study area was surveyed by the Navy with the very sophisticated HUGIN AUV equipped with a synthetic aperture sonar with best resolution by current technology. Following an evaluation of the collected data, various ammunition bodies on the sea floor could be clearly detected. Analyses

  19. Seasonal and diel patterns in sedimentary flux of krill fecal pellets recorded by an echo sounder

    KAUST Repository

    Røstad, Anders

    2013-11-01

    We used a moored upward-facing 200 kHz echo sounder to address sedimentation of fecal pellets (FPs) from dielly migrating Meganyctiphanes norvegica. The echo sounder was located on the bottom at 150 m depth in the Oslofjord, Norway, and was cabled to shore for continuous measurements during winter and spring. Records of sinking pellets were for the first time observed with an echo sounder. Seasonal patterns of sedimentation of krill FPs were strongly correlated with data from continuous measurement of fluorescence, which illustrate the development of the spring bloom. Sedimenting particles were first observed as fluorescence values started to increase at the end of February and continued to increase until the bloom suddenly culminated at the end of March. This collapse of the bloom was detected on the echo sounder as a pulse of slowly sinking acoustic targets over a 2 d period. Prior to this event, there was a strong diel pattern in sedimentation, which correlated, with some time lag, with the diel migration of krill foraging at night near the surface. Pellet average sinking speeds ranged between 423 m d−1 and 804 m d−1, with a strong relation to pellet target strength, which is an acoustic proxy for size. This novel approach shows that echo sounders may be a valuable tool in studies of vertical pellet flux and, thereby, carbon flux, providing temporal resolution and direct observation of the sedimentation process, which are not obtained from standard methods.

  20. Predicting neutron star spins from twin kHz QPOs

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    We briefly review the proposed relations between the frequencies of twin kilohertz quasi-periodic oscillations(kHz QPOs) and the spin frequencies in neutron star low-mass X-ray binaries(NSLMXBs).To test the validity of the proposed models,we estimate the spin frequencies under these theoretical relations and compare them with the measured ones.It seems that magnetohydrodynamic(MHD) oscillations are more promising to account for the kHz QPOs.

  1. Comparison of third-order plasma wave echoes with ballistic second-order plasma wave echoes

    International Nuclear Information System (INIS)

    Leppert, H.D.; Schuelter, H.; Wiesemann, K.

    1982-01-01

    The apparent dispersion of third-order plasma wave echoes observed in a high frequency plasma is compared with that of simultaneously observed ballistic second-order echoes. Amplitude and wavelength of third-order echoes are found to be always smaller than those of second-order echoes, however, the dispersion curves of both types of echoes are very similar. These observations are in qualitative agreement with calculations of special ballistic third-order echoes. The ballistic nature of the observed third-order echoes may, therefore, be concluded from these measurements. (author)

  2. Short echo time, fast gradient-echo imaging

    International Nuclear Information System (INIS)

    Haacke, E.M.; Lenz, G.W.

    1987-01-01

    Present fast-gradient-echoes schemes can acquire volume data rapidly and are flexible in T1 or T1/T2 contrast behavior. However, sequences used to date employ echo time (TE) values of about 15 ms +- 5 and, because of in vivo field inhomogeneities (short T2), they suffer badly from signal loss near sinuses and tissue boundaries. The authors implemented sequences with TE = 4-6 ms and found significant improvement in image quality, especially at high fields. Examples with long TEs vs. short TEs are given in the knee, spine, head, and orbits. Further advantages include (1) faster repetition times (15 ms), (2) higher-quality spin-density or T1-weighted images, and (3) reduction of blood motion artifacts

  3. Longitudinal collective echoes in coasting particle beams

    Directory of Open Access Journals (Sweden)

    Ahmed Al-Khateeb

    2003-01-01

    Full Text Available Longitudinal ballistic and collective beam echoes with diffusion effects are investigated theoretically. In the presence of the space-charge impedance, the collective echo amplitude is obtained as a closed form expression. In contrast to the ballistic case, the collective echo amplitude consists of one maximum at time t_{echo}. The echo amplitude grows up and damps down with a rate proportional to the Landau damping rate of space-charge waves. The effect of weak diffusion is found to modify the ballistic and the collective echo amplitudes in the same manner. This effect of diffusion was confirmed using a “noiseless,” grid-based simulation code. As a first application the amount of numerical diffusion in our simulation code was determined using the echo effect.

  4. Arabidopsis KHZ1 and KHZ2, two novel non-tandem CCCH zinc-finger and K-homolog domain proteins, have redundant roles in the regulation of flowering and senescence.

    Science.gov (United States)

    Yan, Zongyun; Jia, Jianheng; Yan, Xiaoyuan; Shi, Huiying; Han, Yuzhen

    2017-12-01

    The two novel CCCH zinc-finger and K-homolog (KH) proteins, KHZ1 and KHZ2, play important roles in regulating flowering and senescence redundantly in Arabidopsis. The CCCH zinc-finger proteins and K-homolog (KH) proteins play important roles in plant development and stress responses. However, the biological functions of many CCCH zinc-finger proteins and KH proteins remain uncharacterized. In Arabidopsis, KHZ1 and KHZ2 are characterized as two novel CCCH zinc-finger and KH domain proteins which belong to subfamily VII in CCCH family. We obtained khz1, khz2 mutants and khz1 khz2 double mutants, as well as overexpression (OE) lines of KHZ1 and KHZ2. Compared with the wild type (WT), the khz2 mutants displayed no defects in growth and development, and the khz1 mutants were slightly late flowering, whereas the khz1 khz2 double mutants showed a pronounced late flowering phenotype. In contrast, artificially overexpressing KHZ1 and KHZ2 led to the early flowering. Consistent with the late flowering phenotype, the expression of flowering repressor gene FLC was up-regulated, while the expression of flowering integrator and floral meristem identity (FMI) genes were down-regulated significantly in khz1 khz2. In addition, we also observed that the OE plants of KHZ1 and KHZ2 showed early leaf senescence significantly, whereas the khz1 khz2 double mutants showed delayed senescence of leaf and the whole plant. Both KHZ1 and KHZ2 were ubiquitously expressed throughout the tissues of Arabidopsis. KHZ1 and KHZ2 were localized to the nucleus, and possessed both transactivation activities and RNA-binding abilities. Taken together, we conclude that KHZ1 and KHZ2 have redundant roles in the regulation of flowering and senescence in Arabidopsis.

  5. Independence of echo-threshold and echo-delay in the barn owl.

    Directory of Open Access Journals (Sweden)

    Brian S Nelson

    Full Text Available Despite their prevalence in nature, echoes are not perceived as events separate from the sounds arriving directly from an active source, until the echo's delay is long. We measured the head-saccades of barn owls and the responses of neurons in their auditory space-maps while presenting a long duration noise-burst and a simulated echo. Under this paradigm, there were two possible stimulus segments that could potentially signal the location of the echo. One was at the onset of the echo; the other, after the offset of the direct (leading sound, when only the echo was present. By lengthening the echo's duration, independently of its delay, spikes and saccades were evoked by the source of the echo even at delays that normally evoked saccades to only the direct source. An echo's location thus appears to be signaled by the neural response evoked after the offset of the direct sound.

  6. 3600 digital phase detector with 100-kHz bandwidth

    International Nuclear Information System (INIS)

    Reid, D.W.; Riggin, D.; Fazio, M.V.; Biddle, R.S.; Patton, R.D.; Jackson, H.A.

    1981-01-01

    The general availability of digital circuit components with propagation delay times of a few nanoseconds makes a digital phase detector with good bandwidth feasible. Such a circuit has a distinct advantage over its analog counterpart because of its linearity over wide range of phase shift. A phase detector that is being built at Los Alamos National Laboratory for the Fusion Materials Irradiation Test (FMIT) project is described. The specifications are 100-kHz bandwidth, linearity of +- 1 0 over +- 180 0 of phase shift, and 0.66 0 resolution. To date, the circuit has achieved the bandwidth and resolution. The linearity is approximately +- 3 0 over +- 180 0 phase shift

  7. Classification of radar echoes using fractal geometry

    International Nuclear Information System (INIS)

    Azzaz, Nafissa; Haddad, Boualem

    2017-01-01

    Highlights: • Implementation of two concepts of fractal geometry to classify two types of meteorological radar echoes. • A new approach, called a multi-scale fractal dimension is used for classification between fixed echoes and rain echoes. • An Automatic identification system of meteorological radar echoes was proposed using fractal geometry. - Abstract: This paper deals with the discrimination between the precipitation echoes and the ground echoes in meteorological radar images using fractal geometry. This study aims to improve the measurement of precipitations by weather radars. For this, we considered three radar sites: Bordeaux (France), Dakar (Senegal) and Me lbourne (USA). We showed that the fractal dimension based on contourlet and the fractal lacunarity are pertinent to discriminate between ground and precipitation echoes. We also demonstrated that the ground echoes have a multifractal structure but the precipitations are more homogeneous than ground echoes whatever the prevailing climate. Thereby, we developed an automatic classification system of radar using a graphic interface. This interface, based on the fractal geometry makes possible the identification of radar echoes type in real time. This system can be inserted in weather radar for the improvement of precipitation estimations.

  8. ROV seafloor surveys combining 5-cm lateral resolution multibeam bathymetry with color stereo photographic imagery

    Science.gov (United States)

    Caress, D. W.; Hobson, B.; Thomas, H. J.; Henthorn, R.; Martin, E. J.; Bird, L.; Rock, S. M.; Risi, M.; Padial, J. A.

    2013-12-01

    The Monterey Bay Aquarium Research Institute is developing a low altitude, high-resolution seafloor mapping capability that combines multibeam sonar with stereo photographic imagery. The goal is to obtain spatially quantitative, repeatable renderings of the seafloor with fidelity at scales of 5 cm or better from altitudes of 2-3 m. The initial test surveys using this sensor system are being conducted from a remotely operated vehicle (ROV). Ultimately we intend to field this survey system from an autonomous underwater vehicle (AUV). This presentation focuses on the current sensor configuration, methods for data processing, and results from recent test surveys. Bathymetry data are collected using a 400-kHz Reson 7125 multibeam sonar. This configuration produces 512 beams across a 135° wide swath; each beam has a 0.5° acrosstrack by 1.0° alongtrack angular width. At a 2-m altitude, the nadir beams have a 1.7-cm acrosstrack and 3.5 cm alongtrack footprint. Dual Allied Vision Technology GX1920 2.8 Mpixel color cameras provide color stereo photography of the seafloor. The camera housings have been fitted with corrective optics achieving a 90° field of view through a dome port. Illumination is provided by dual 100J xenon strobes. Position, depth, and attitude data are provided by a Kearfott SeaDevil Inertial Navigation System (INS) integrated with a 300 kHz RDI Doppler velocity log (DVL). A separate Paroscientific pressure sensor is mounted adjacent to the INS. The INS Kalman filter is aided by the DVL velocity and pressure data, achieving navigational drift rates less than 0.05% of the distance traveled during surveys. The sensors are mounted onto a toolsled fitted below MBARI's ROV Doc Ricketts with the sonars, cameras and strobes all pointed vertically down. During surveys the ROV flies at a 2-m altitude at speeds of 0.1-0.2 m/s. During a four-day R/V Western Flyer cruise in June 2013, we successfully collected multibeam and camera survey data from a 2-m altitude

  9. A radar-echo model for Mars

    International Nuclear Information System (INIS)

    Thompson, T.W.; Moore, H.J.

    1990-01-01

    Researchers developed a radar-echo model for Mars based on 12.6 cm continuous wave radio transmissions backscattered from the planet. The model broadly matches the variations in depolarized and polarized total radar cross sections with longitude observed by Goldstone in 1986 along 7 degrees S. and yields echo spectra that are generally similiar to the observed spectra. Radar map units in the model include an extensive cratered uplands unit with weak depolarized echo cross sections, average thermal inertias, moderate normal refelectivities, and moderate rms slopes; the volcanic units of Tharsis, Elysium, and Amazonis regions with strong depolarized echo cross sections, low thermal inertia, low normal reflectivities, and large rms slopes; and the northern planes units with moderate to strong depolarized echo cross sections, moderate to very high thermal inertias, moderate to large normal reflectivities, and moderate rms slopes. The relevance of the model to the interpretation of radar echoes from Mars is discussed

  10. The acoustics of the echo cornet

    Science.gov (United States)

    Pyle, Robert W., Jr.; Klaus, Sabine K.

    2002-11-01

    The echo cornet was an instrument produced by a number of makers in several countries from about the middle of the nineteenth to the early twentieth centuries. It consists of an ordinary three-valve cornet to which a fourth valve has been added, downstream of the three normal valves. The extra valve diverts the airstream from the normal bell to an ''echo'' bell that gives a muted tone quality. Although the air column through the echo bell is typically 15 cm longer than the path through the normal bell, there is no appreciable change of playing pitch when the echo bell is in use. Acoustic input impedance and impulse response measurements and consideration of the standing-wave pattern within the echo bell show how this can be so. Acoustically, the echo bell is more closely related to hand-stopping on the French horn than to the mutes commonly used on the trumpet and cornet.

  11. Beam echoes in the presence of coupling

    Energy Technology Data Exchange (ETDEWEB)

    Gross, Axel [Case Western Reserve U.

    2017-10-03

    Transverse beam echoes could provide a new technique of measuring diusion characteristics orders of magnitude faster than the current methods; however, their interaction with many accelerator parameters is poorly understood. Using a program written in C, we explored the relationship between coupling and echo strength. We found that echoes could be generated in both dimensions, even with a dipole kick in only one dimension. We found that the echo eects are not destroyed even when there is strong coupling, falling o only at extremely high coupling values. We found that at intermediate values of skew quadrupole strength, the decoherence time of the beam is greatly increased, causing a destruction of the echo eects. We found that this is caused by a narrowing of the tune width of the particles. Results from this study will help to provide recommendations to IOTA (Integrable Optics Test Accelerator) for their upcoming echo experiment.

  12. San Francisco Bay Multi-beam Bathymetry: Area A

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — These multi-beam bathymetric data were collected over shallow subtidal areas in the San Francisco Bay estuary system. Bathymetric and acoustic backscatter data were...

  13. February 2007 Multibeam Mapping of Pulley Ridge, southwest Florida

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This disk or set of disks contain high-resolution multibeam and backscatter maps of the Pulley Ridge Area, near the Tortugas, in the Gulf of Mexico. It includes the...

  14. Performance appraisal of multibeam system - Hydrosweep at different seabed provinces

    Digital Repository Service at National Institute of Oceanography (India)

    Kodagali, V.N.; Chakraborty, B.

    The performance of the multibeam sounding system (hydrosweep system) has been assessed at three varying zones of the seabed-continental shelf, continental slope, and deepsea seamount areas. Statistical studies were performed on the beamwise time...

  15. NOAA Ship Pisces Cruise PC1106 (14) Multibeam Sonar Workshop

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The overall objective is to familiarize participating scientists with PISCES multibeam echosounder (Simrad ME70) configuration, operation, calibration and data...

  16. Acoustic Multi-Beam Echosounder Data (ME70)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Southeast Fisheries Science Center Mississippi Laboratories collects data using a Simrad ME70 scientific multibeam acoustic echosounder during resource...

  17. Topology Control in Aerial Multi-Beam Directional Networks

    Science.gov (United States)

    2017-04-24

    Topology Control in Aerial Multi-Beam Directional Networks Brian Proulx, Nathaniel M. Jones, Jennifer Madiedo, Greg Kuperman {brian.proulx, njones...significant interference. Topology control (i.e., selecting a subset of neighbors to communicate with) is vital to reduce the interference. Good topology ...underlying challenges to topology control in multi-beam direction networks. Two topology control algorithms are developed: a centralized algorithm

  18. Detection of active hydrothermal vent fields in the Pescadero Basin and on the Alarcon Rise using AUV multibeam and CTD data

    Science.gov (United States)

    Caress, D. W.; Troni, G.; Clague, D. A.; Paduan, J. B.; Martin, J. F.; Thomas, H. J.; Thompson, D.; Conlin, D.; Martin, E. J.; meneses-Quiroz, E.; Nieves-Cardoso, C.; Angel Santa Rosa del Rio, M.

    2015-12-01

    The MBARI AUV D. Allan B. collected high resolution bathymetry, sidescan, and subbottom profiles along the neovolcanic zone of the Alarcon Rise and across the southern Pescadero Basin during 2012 and 2015 MBARI expeditions to the Gulf of California (GOC). The combination of high resolution multibeam bathymetry and seawater temperature data has proven effective in identifying active high temperature vent fields, as validated by inspection and sampling during ROV dives. The AUV carries a 200 kHz multibeam sonar, 110 kHz chirp sidescan sonar, a 1-6 kHz chirp subbottom profiler, and a conductivity, temperature and depth (CTD) sensor for ~17-hour duration missions. Flying at 5.4 km/hr at 50 m altitude, the processed AUV bathymetry has a 0.1 m vertical precision and a 1 m lateral resolution. Chimneys taller than 1.5 m are sufficiently distinctive to allow provisional identification. The CTD temperature data have a nominal 0.002°C accuracy. Following calculation of potential temperature and correcting for average local variation of potential temperature with depth, anomalies greater than 0.05 °C can be reliably identified using a spike detection filter. MBARI AUV mapping surveys are typically planned using a 150 m survey line spacing, so the CTD data may be collected as much as 75 m away from any vent plume source. Five active high temperature vent fields were discovered in the southern GOC, with the Auka Field in the southern Pescadero Basin, and the Ja Sít, Pericú, Meyibó, and Tzab-ek Fields along the Alarcon Rise. In all five cases, hydrothermal vent chimneys are readily identifiable in the multibeam bathymetry, and temperature anomalies are observed above background variability. Other apparent hydrothermal chimneys were observed in the bathmetry that did not exhibit water temperature anomalies; most of these were visited during ROV dives and confirmed to be inactive sites. The maximum water column anomalies are 0.13°C observed above the Meyibó field and 0.25

  19. How to misuse echo contrast

    Directory of Open Access Journals (Sweden)

    Missios Anna

    2009-01-01

    Full Text Available Abstract Background Primary intracardiac tumours are rare, there are however several entities that can mimic tumours. Contrast echocardiography has been suggested to aid the differentiation of various suspected masses. We present a case where transthoracic echocardiography completely misdiagnosed a left atrial mass, partly due to use of echo contrast. Case presentation An 80 year-old woman was referred for transthoracic echocardiography because of one-month duration of worsening of dyspnoea. Transthoracic echocardiography displayed a large echodense mass in the left atrium. Intravenous injection of contrast (SonoVue, Bracco Inc., It indicated contrast-enhancement of the structure, suggesting tumour. Transesophageal echocardiography revealed, however, a completely normal finding in the left atrium. Subsequent gastroscopy examination showed a hiatal hernia. Conclusion It is noteworthy that the transthoracic echocardiographic exam completely misdiagnosed what seemed like a left atrial mass, which in part was an effect of the use of echo contrast. This example highlights that liberal use of transoesophageal echocardiography is often warranted if optimal display of cardiac structures is desired.

  20. Light echoes - Type II supernovae

    International Nuclear Information System (INIS)

    Schaefer, B.E.

    1987-01-01

    Type II supernovae (SNs) light curves show a remarkable range of shapes. Data have been collected for the 12 Type II SNs that have light curve information for more than four months past maximum. Contrary to previous reports, it is found that (1) the decay rate after 100 days past maximum varies by almost an order of magnitude and (2) the light curve shapes are not bimodally distributed, but actually form a continuum. In addition, it is found that the extinctions to the SNs are related to the light curve shapes. This implies that the absorbing dust is local to the SNs. The dust is likely to be part of a circumstellar shell emitted by the SN progenitor that Dwek (1983) has used to explain infrared echoes. The optical depth of the shell can get quite large. In such cases, it is found that the photons scattered and delayed by reflection off dust grains will dominate the light curve several months after peak brightness. This light echo offers a straightforward explanation of the diversity of Type II SN light curves. 22 references

  1. Multi-beam linear accelerator EVT

    Energy Technology Data Exchange (ETDEWEB)

    Teryaev, Vladimir E., E-mail: vladimir_teryaev@mail.ru [Omega-P, Inc., New Haven, CT 06510 (United States); Kazakov, Sergey Yu. [Fermilab, Batavia, IL 60510 (United States); Hirshfield, Jay L. [Omega-P, Inc., New Haven, CT 06510 (United States); Yale University, New Haven, CT 06511 (United States)

    2016-09-01

    A novel electron multi-beam accelerator is presented. The accelerator, short-named EVT (Electron Voltage Transformer) belongs to the class of two-beam accelerators. It combines an RF generator and essentially an accelerator within the same vacuum envelope. Drive beam-lets and an accelerated beam are modulated in RF modulators and then bunches pass into an accelerating structure, comprising uncoupled with each other and inductive tuned cavities, where the energy transfer from the drive beams to the accelerated beam occurs. A phasing of bunches is solved by choice correspond distances between gaps of the adjacent cavities. Preliminary results of numerical simulations and the initial specification of EVT operating in S-band, with a 60 kV gun and generating a 2.7 A, 1.1 MV beam at its output is presented. A relatively high efficiency of 67% and high design average power suggest that EVT can find its use in industrial applications.

  2. Mosaic of gridded multibeam and lidar bathymetry of the US Territory of Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Gridded multibeam bathymetry is integrated with gridded lidar bathymetry. Gridded (5 m cell size) multibeam bathymetry were collected aboard NOAA Ship Hiialaka'i and...

  3. Coverage map of gridded multibeam and lidar bathymetry of the US Territory of Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Gridded multibeam bathymetry is integrated with gridded lidar bathymetry. Gridded (5 m cell size) multibeam bathymetry were collected aboard NOAA Ship Hiialaka'i and...

  4. Gridded multibeam bathymetry of Apra Harbor, Guam U.S. Territory

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Gridded bathymetry from Apra Harbor, Guam U.S. Territory. The netCDF and Arc ASCII grids include multibeam bathymetry from the Reson SeaBat 8125 multibeam sonar...

  5. Simulating satellite observations of 100 kHz radio waves from relativistic electron beams above thunderclouds

    OpenAIRE

    M. Füllekrug; C. Hanuise; M. Parrot

    2010-01-01

    Relativistic electron beams above thunderclouds emit 100 kHz radio waves which illuminate the Earth's atmosphere and near-Earth space. This contribution aims to clarify the physical processes which are relevant for the spatial spreading of the radio wave energy below and above the ionosphere and thereby enables simulating satellite observations of 100 kHz radio waves from relativistic electron beams above thunderclouds. The simulation uses the DEMETER satellite which observes 100 kHz ...

  6. Gravitational wave sources: reflections and echoes

    Science.gov (United States)

    Price, Richard H.; Khanna, Gaurav

    2017-11-01

    The recent detection of gravitational waves has generated interest in alternatives to the black hole interpretation of sources. A subset of such alternatives involves a prediction of gravitational wave ‘echoes’. We consider two aspects of possible echoes: first, general features of echoes coming from spacetime reflecting conditions. We find that the detailed nature of such echoes does not bear any clear relationship to quasi-normal frequencies. Second, we point out the pitfalls in the analysis of local reflecting ‘walls’ near the horizon of rapidly rotating black holes.

  7. Gravitational wave sources: reflections and echoes

    International Nuclear Information System (INIS)

    Price, Richard H; Khanna, Gaurav

    2017-01-01

    The recent detection of gravitational waves has generated interest in alternatives to the black hole interpretation of sources. A subset of such alternatives involves a prediction of gravitational wave ‘echoes’. We consider two aspects of possible echoes: first, general features of echoes coming from spacetime reflecting conditions. We find that the detailed nature of such echoes does not bear any clear relationship to quasi-normal frequencies. Second, we point out the pitfalls in the analysis of local reflecting ‘walls’ near the horizon of rapidly rotating black holes. (paper)

  8. Sparse adaptive filters for echo cancellation

    CERN Document Server

    Paleologu, Constantin

    2011-01-01

    Adaptive filters with a large number of coefficients are usually involved in both network and acoustic echo cancellation. Consequently, it is important to improve the convergence rate and tracking of the conventional algorithms used for these applications. This can be achieved by exploiting the sparseness character of the echo paths. Identification of sparse impulse responses was addressed mainly in the last decade with the development of the so-called ``proportionate''-type algorithms. The goal of this book is to present the most important sparse adaptive filters developed for echo cancellati

  9. Alarm pheromone does not modulate 22-kHz calls in male rats.

    Science.gov (United States)

    Muyama, Hiromi; Kiyokawa, Yasushi; Inagaki, Hideaki; Takeuchi, Yukari; Mori, Yuji

    2016-03-15

    Rats are known to emit a series of ultrasonic vocalizations, termed 22-kHz calls, when exposed to distressing stimuli. Pharmacological studies have indicated that anxiety mediates 22-kHz calls in distressed rats. We previously found that exposure to the rat alarm pheromone increases anxiety in rats. Therefore, we hypothesized that the alarm pheromone would increase 22-kHz calls in pheromone-exposed rats. Accordingly, we tested whether exposure to the alarm pheromone induced 22-kHz calls, as well as whether the alarm pheromone increased 22-kHz calls in response to an aversive conditioned stimulus (CS). Rats were first fear-conditioned to an auditory and contextual CS. On the following day, the rats were either exposed to the alarm pheromone or a control odor that was released from the neck region of odor-donor rats. Then, the rats were re-exposed to the aversive CS. The alarm pheromone neither induced 22-kHz calls nor increased 22-kHz calls in response to the aversive CS. In contrast, the control odor unexpectedly reduced the total number and duration of 22-kHz calls elicited by the aversive CS, as well as the duration of freezing. These results suggest that the alarm pheromone does not affect 22-kHz calls in rats. However, we may have found evidence for an appeasing olfactory signal, released from the neck region of odor-donor rats. Copyright © 2016 Elsevier Inc. All rights reserved.

  10. Multi-beam injector development at LBL

    International Nuclear Information System (INIS)

    Rutkowski, H.L.; Faltens, A.; Brodzik, D.A.; Johnson, R.M.; Pike, C.D.; Vanecek, D.L.; Humphries, S. Jr.; Meyer, E.A.; Hewett, D.W.

    1990-06-01

    LBL is developing a multi-beam injector that will be used for scaled accelerator experiments related to Heavy Ion Fusion. The device will produce sixteen 0.5 Amp beams of C+ at 2 MeV energy. The carbon arc source has been developed to the point where the emittance is within a factor of four of the design target. Modelling of the source behavior to find ways to reduce the emittance is discussed. Source lifetime and reliability is also of paramount importance to us and data regarding the lifetime and failure modes of different source configurations is discussed. One half of the accelerating column has been constructed and tested at high voltage. One beam experiments in this half column are underway. The second half of the column is being built and the transition 2 MV experiments should begin soon. In addition to beam and source performance we also discuss the controls for the injector and the electronics associated with the source and current injection. 3 refs., 2 figs

  11. Effects on atmospherics at 6 kHz and 9 kHz recorded at Tripura during the India-Pakistan Border earthquake

    Directory of Open Access Journals (Sweden)

    S. S. De

    2010-04-01

    Full Text Available The outcome of the results of some analyses of electromagnetic emissions recorded by VLF receivers at 6 kHz and 9 kHz over Agartala, Tripura, the North-Eastern state of India (Lat. 23° N, Long. 91.4° E during the large earthquake at Muzaffarabad (Lat. 34.53° N, Long. 73.58° E at Kashmir under Pakistan have been presented here. Spiky variations in integrated field intensity of atmospherics (IFIA at 6 and 9 kHz have been observed 10 days prior (from midnight of 28 September 2005 to the day of occurrence of the earthquake on 8 October 2005 and the effect continued, decayed gradually and eventually ceased on 16 October 2005. The spikes distinctly superimposed on the ambient level with mutual separation of 2–5 min. Occurrence number of spikes per hour and total duration of their occurrence have been found remarkably high on the day of occurrence of the earthquake. The spike heights are higher at 6 kHz than at 9 kHz. The results have been explained on the basis of generation of electromagnetic radiation associated with fracture of rocks, their subsequent penetration into the Earth's atmosphere and finally their propagation between Earth-ionosphere waveguide. The present observation shows that VLF anomaly is well-confined between 6 and 9 kHz.

  12. Echoes in correlated neural systems

    International Nuclear Information System (INIS)

    Helias, M; Tetzlaff, T; Diesmann, M

    2013-01-01

    Correlations are employed in modern physics to explain microscopic and macroscopic phenomena, like the fractional quantum Hall effect and the Mott insulator state in high temperature superconductors and ultracold atoms. Simultaneously probed neurons in the intact brain reveal correlations between their activity, an important measure to study information processing in the brain that also influences the macroscopic signals of neural activity, like the electroencephalogram (EEG). Networks of spiking neurons differ from most physical systems: the interaction between elements is directed, time delayed, mediated by short pulses and each neuron receives events from thousands of neurons. Even the stationary state of the network cannot be described by equilibrium statistical mechanics. Here we develop a quantitative theory of pairwise correlations in finite-sized random networks of spiking neurons. We derive explicit analytic expressions for the population-averaged cross correlation functions. Our theory explains why the intuitive mean field description fails, how the echo of single action potentials causes an apparent lag of inhibition with respect to excitation and how the size of the network can be scaled while maintaining its dynamical state. Finally, we derive a new criterion for the emergence of collective oscillations from the spectrum of the time-evolution propagator. (paper)

  13. Wind yield forecast with Echo State Networks; Windertragsprognose mit Echo State Networks

    Energy Technology Data Exchange (ETDEWEB)

    Kobialka, Hans-Ulrich [Fraunhofer IAIS, Sankt Augustin (Germany)

    2012-07-01

    Statistical methods are able to create models of complex system dynamics which are difficult to capture analytically. This paper describes a wind energy prediction system based on a machine learning method, called Echo State Networks. Echo State Networks enable the training of large recurrent neural networks which are able to model and predict highly non-linear system dynamics. This paper gives a short description of Echo State Networks and the realization of the wind energy prediction system. (orig.)

  14. Hazardous Waste Dashboard Help | ECHO | US EPA

    Science.gov (United States)

    The dashboards found on the Enforcement and Compliance History Online (ECHO) website are specialized to track both facility and agency performance as they relate to compliance with and enforcement of environmental standards under the Resource Conservation and Recovery Act (RCRA).

  15. Air Dashboard Help | ECHO | US EPA

    Science.gov (United States)

    The dashboards found on the Enforcement and Compliance History Online (ECHO) website are specialized to track both facility and agency performance as they relate to compliance with and enforcement of environmental standards under the Clean Air Act (CAA).

  16. MEASUREMENT OF TRANSVERSE ECHOES IN RHIC

    International Nuclear Information System (INIS)

    FISCHER, W.; SATOGATA, T.; TOMAS, R.

    2005-01-01

    Beam echoes are a very sensitive method to measure diffusion, and longitudinal echo measurements were performed in a number of machines. In RHIC, for the first time, a transverse beam echo was observed after applying a dipole kick followed by a quadrupole .kick. After application of the dipole kick, the dipole moment decohered completely due to lattice nonlinearities. When a quadrupole kick is applied at time τ after the dipole kick, the beam re-cohered at time 2τ thus showing an echo response. We describe the experimental setup and measurement results. In the measurements the dipole and quadrupole kick amplitudes, amplitude dependent tune shift, and the time between dipole and quadrupole kick were varied. In addition, measurements were taken with gold bunches of different intensities. These should exhibit different transverse diffusion rates due to intra-beam scattering

  17. Water Dashboard Help | ECHO | US EPA

    Science.gov (United States)

    The dashboards on the ECHO website are specialized to track both facility and agency performance as they relate to compliance with and enforcement of environmental standards under the Clean Water Act (CWA).

  18. Stellar Echo Imaging of Exoplanets, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — All stars exhibit intensity fluctuations over several time scales, from nanoseconds to days; these intensity fluctuations echo off planetary bodies in the star...

  19. Stellar Echo Imaging of Exoplanets, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — All stars exhibit intensity fluctuations over several time scales, from nanoseconds to days; these intensity fluctuations echo off planetary bodies in the star...

  20. Time Delay Estimation Algoritms for Echo Cancellation

    Directory of Open Access Journals (Sweden)

    Kirill Sakhnov

    2011-01-01

    Full Text Available The following case study describes how to eliminate echo in a VoIP network using delay estimation algorithms. It is known that echo with long transmission delays becomes more noticeable to users. Thus, time delay estimation, as a part of echo cancellation, is an important topic during transmission of voice signals over packetswitching telecommunication systems. An echo delay problem associated with IP-based transport networks is discussed in the following text. The paper introduces the comparative study of time delay estimation algorithm, used for estimation of the true time delay between two speech signals. Experimental results of MATLab simulations that describe the performance of several methods based on cross-correlation, normalized crosscorrelation and generalized cross-correlation are also presented in the paper.

  1. Enforcement and Compliance History Online (ECHO) Facilities

    Data.gov (United States)

    U.S. Environmental Protection Agency — ECHO provides integrated compliance and enforcement information for about 800,000 regulated facilities nationwide. Its features range from simple to advanced,...

  2. Report Environmental Violations | ECHO | US EPA

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  3. Denuncie violaciones ambientales | ECHO | US EPA

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  4. Analyze Trends: State Hazardous Waste Dashboard | ECHO ...

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  5. DWDashboard_Year.png | ECHO | US EPA

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  6. summarytable.PNG | ECHO | US EPA

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  7. dashboard_3.PNG | ECHO | US EPA

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  8. ExampleDFR.PNG | ECHO | US EPA

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  9. monperload_1.PNG | ECHO | US EPA

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  10. monperload_2.PNG | ECHO | US EPA

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  11. Resources.PNG | ECHO | US EPA

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  12. Dischargers_Example.png | ECHO | US EPA

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  13. dashboard_1.PNG | ECHO | US EPA

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  14. dashboard_2.PNG | ECHO | US EPA

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  15. monperload_3.PNG | ECHO | US EPA

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  16. Hierarchy of Loading Calculations | ECHO | US EPA

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  17. Mobile Bay.pdf | ECHO | US EPA

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  18. Custom Search Help | ECHO | US EPA

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  19. Custom Search Results Help | ECHO | US EPA

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  20. Watershed Statistics Help | ECHO | US EPA

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  1. Water Pollution Search | ECHO | US EPA

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  2. Electric Dipole Echoes in Rydberg Atoms

    International Nuclear Information System (INIS)

    Yoshida, S.; Reinhold, C. O.; Burgdoerfer, J.; Zhao, W.; Mestayer, J. J.; Lancaster, J. C.; Dunning, F. B.

    2007-01-01

    We report the first observation of echoes in the electric dipole moment of an ensemble of Rydberg atoms precessing in an external electric field F. Rapid reversal of the field direction is shown to play a role similar to that of a π pulse in NMR in rephasing a dephased ensemble of electric dipoles resulting in the buildup of an echo. The mechanisms responsible for this are discussed with the aid of classical trajectory Monte Carlo simulations

  3. Technical Users Background Document | ECHO | US EPA

    Science.gov (United States)

    ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

  4. Time-resolved Femtosecond Photon Echo Probes Bimodal Solvent Dynamics

    NARCIS (Netherlands)

    Pshenichnikov, M.S; Duppen, K.; Wiersma, D. A.

    1995-01-01

    We report on time-resolved femtosecond photon echo experiments of a dye molecule in a polar solution. The photon echo is time resolved by mixing the echo with a femtosecond gate pulse in a nonlinear crystal. It is shown that the temporal profile of the photon echo allows separation of the

  5. Multibeam smart antenna field trial experiments in mobile radio environments

    Science.gov (United States)

    Perini, Patrick

    1996-01-01

    Several types of high gain multibeam antennas were tested and compared to traditional sector and omni antennas in various mobile radio environments. A vehicle equipped with a mobile transmitter drove in several mobile radio environments while the received signal strength (RSS) was recorded on multiple antenna channels attached to multibeam, sector and omni directional antennas. The RSS data recorded included the fast (rayleigh) fading and was averaged into local means based on the mobile's position/speed. Description of the experiment and analysis of the gain improvement, average RSS, diversity gain are presented.

  6. Echoes from a Dying Star

    Science.gov (United States)

    Kohler, Susanna

    2017-06-01

    When a passing star is torn apart by a supermassive black hole, it emits a flare of X-ray, ultraviolet, and optical light. What can we learn from the infrared echo of a violent disruption like this one?Stellar DestructionOptical (black triangles) and infrared (blue circles and red squares) observations of F010042237. Day 0 marks the day the optical emission peaked. The infrared emission rises steadily through the end of the data. [Dou et al. 2017]Tidal disruption events occur when a star passes within the tidal radius of a supermassive black hole. After tidal forces pull the star apart, much of the stellar matter falls onto the black hole, radiating briefly in X-ray, ultraviolet and optical as it accretes. This signature rise and gradual fall of emission has allowed us to detect dozens of tidal disruption events thus far.One of the recently discovered candidate events is a little puzzling. Not only does the candidate in ultraluminous infrared galaxy F010042237 have an unusual host most disruptions occur in galaxies that are no longer star-forming, in contrast to this one but its optical light curve also shows an unusually long decay time.Now mid-infrared observations of this event have beenpresented by a team of scientists led by Liming Dou (Guangzhou University and Department of Education, Guangdong Province, China), revealing why this disruption is behaving unusually.Schematic of a convex dusty ring (red bows) that absorbs UV photons and re-emits in the infrared. It simultaneously scatters UV and optical photons into our line of sight. The dashed lines illustrate the delays at lags of 60 days, 1, 2, 3, 4, and 5 years. [Adapted from Dou et al. 2017]A Dusty Solution?The optical flare from F010042237s nucleus peaked in 2010, so Dou and collaborators obtained archival mid-infrared data from the WISE and NEOWISE missions from 2010 to 2016. The data show that the galaxy is quiescent in mid-infrared in 2010 but in data from three years later, the infrared emission has

  7. Novel multi-beam radiometers for accurate ocean surveillance

    DEFF Research Database (Denmark)

    Cappellin, C.; Pontoppidan, K.; Nielsen, P. H.

    2014-01-01

    Novel antenna architectures for real aperture multi-beam radiometers providing high resolution and high sensitivity for accurate sea surface temperature (SST) and ocean vector wind (OVW) measurements are investigated. On the basis of the radiometer requirements set for future SST/OVW missions...

  8. Investigation of echo sounding parameters for the characterisation of bottom sediments in a sub-tropical reservoir

    Directory of Open Access Journals (Sweden)

    Stephan Hilgert

    2016-07-01

    Full Text Available The increasing number of reservoirs around the world today reaches a surface area of around 500,000 km², equaling one third of that of non-artificial surface water bodies. By impounding rivers through the construction of dams, riverine systems and biochemical cycles are disrupted. Different types of transported materials are trapped behind the dams and form layers of sediment. A method to characterise the spatially extensive sediment volumes with an EA 400 echo sounder was tested in the Vossoroca reservoir in the southeast of Brazil, Paraná State. A number of core and grab samples was taken and analysed for a variety of chemical and physical parameters. These data served as ground truthing for the hydro-acoustic assessment of the sediment. Eight hydro-acoustic parameters were derived from the echo signals using the Sonar5-Pro software. The major objective of defining the optimal survey parameters for the echo sounder as well as determining the difference between core and grab samples was reached by correlating the various single parameters and identifying the best combinations. Density and grain size distribution represented the best detectable sediment features with r-values of 0.94 and 0.95. The lower 38 kHz frequency generally had a better performance than the 200 kHz frequency. Results show that core samples reached a significantly higher quality of correlation for sediment characterisation. Additionally, it is was found that shorter pulse lengths yield a better characterisation. The results underline the potential of single beam echo sounders for extensive sediment characterisation. This methodology may be used for future mass balance estimations of large reservoirs.

  9. Comparison of Shallow Survey 2012 Multibeam Datasets

    Science.gov (United States)

    Ramirez, T. M.

    2012-12-01

    The purpose of the Shallow Survey common dataset is a comparison of the different technologies utilized for data acquisition in the shallow survey marine environment. The common dataset consists of a series of surveys conducted over a common area of seabed using a variety of systems. It provides equipment manufacturers the opportunity to showcase their latest systems while giving hydrographic researchers and scientists a chance to test their latest algorithms on the dataset so that rigorous comparisons can be made. Five companies collected data for the Common Dataset in the Wellington Harbor area in New Zealand between May 2010 and May 2011; including Kongsberg, Reson, R2Sonic, GeoAcoustics, and Applied Acoustics. The Wellington harbor and surrounding coastal area was selected since it has a number of well-defined features, including the HMNZS South Seas and HMNZS Wellington wrecks, an armored seawall constructed of Tetrapods and Akmons, aquifers, wharves and marinas. The seabed inside the harbor basin is largely fine-grained sediment, with gravel and reefs around the coast. The area outside the harbor on the southern coast is an active environment, with moving sand and exposed reefs. A marine reserve is also in this area. For consistency between datasets, the coastal research vessel R/V Ikatere and crew were used for all surveys conducted for the common dataset. Using Triton's Perspective processing software multibeam datasets collected for the Shallow Survey were processed for detail analysis. Datasets from each sonar manufacturer were processed using the CUBE algorithm developed by the Center for Coastal and Ocean Mapping/Joint Hydrographic Center (CCOM/JHC). Each dataset was gridded at 0.5 and 1.0 meter resolutions for cross comparison and compliance with International Hydrographic Organization (IHO) requirements. Detailed comparisons were made of equipment specifications (transmit frequency, number of beams, beam width), data density, total uncertainty, and

  10. Air puff-induced 22-kHz calls in F344 rats.

    Science.gov (United States)

    Inagaki, Hideaki; Sato, Jun

    2016-03-01

    Air puff-induced ultrasonic vocalizations in adult rats, termed "22-kHz calls," have been applied as a useful animal model to develop psychoneurological and psychopharmacological studies focusing on human aversive affective disorders. To date, all previous studies on air puff-induced 22-kHz calls have used outbred rats. Furthermore, newly developed gene targeting technologies, which are essential for further advancement of biomedical experiments using air puff-induced 22-kHz calls, have enabled the production of genetically modified rats using inbred rat strains. Therefore, we considered it necessary to assess air puff-induced 22-kHz calls in inbred rats. In this study, we assessed differences in air puff-induced 22-kHz calls between inbred F344 rats and outbred Wistar rats. Male F344 rats displayed similar total (summed) duration of air puff-induced 22 kHz vocalizations to that of male Wistar rats, however, Wistar rats emitted fewer calls of longer duration, while F344 rats emitted higher number of vocalizations of shorter duration. Additionally, female F344 rats emitted fewer air puff-induced 22-kHz calls than did males, thus confirming the existence of a sex difference that was previously reported for outbred Wistar rats. The results of this study could confirm the reliability of air puff stimulus for induction of a similar amount of emissions of 22-kHz calls in different rat strains, enabling the use of air puff-induced 22-kHz calls in inbred F344 rats and derived genetically modified animals in future studies concerning human aversive affective disorders. Copyright © 2015 Elsevier Inc. All rights reserved.

  11. Echo-Interleaved-Spiral MR Imaging

    Energy Technology Data Exchange (ETDEWEB)

    Rosenthal, Shirrie; Azhari, Haim [Department of Biomedical Engineering, Technion, Israel Institute of Technology, Haifa 32000 (Israel); Montag, Avram [Elscint Ltd., MRI division, Haifa (Israel)

    1999-12-31

    Interleaved-Spiral imaging is an efficient method for MRI fast scans. However, images suffer from blurring and artifacts due to field inhomogeneities and the long readout times. In this paper, we combine interleaved-spirals with spin-echo for 3D scans. The refocusing RF-pulses (echoes) refocus off-resonance spins, thus allowing longer acquisition times per excitation, by limiting inhomogeneity effects. The total number of excitations for a 3D scan is reduced by half. The 3D Fourier transform of an object is divided into pairs of slices, one slice is scanned in an outgoing interleaved-spiral, initiated after a 90 degree pulse has been applied. The second slice is scanned in an ingoing interleaved-spiral, after a 180 degree pulse has been applied, thus reaching the slice origin at the echo time. (authors) 4 refs., 3 figs.

  12. Echo-Interleaved-Spiral MR Imaging

    International Nuclear Information System (INIS)

    Rosenthal, Shirrie; Azhari, Haim; Montag, Avram

    1998-01-01

    Interleaved-Spiral imaging is an efficient method for MRI fast scans. However, images suffer from blurring and artifacts due to field inhomogeneities and the long readout times. In this paper, we combine interleaved-spirals with spin-echo for 3D scans. The refocusing RF-pulses (echoes) refocus off-resonance spins, thus allowing longer acquisition times per excitation, by limiting inhomogeneity effects. The total number of excitations for a 3D scan is reduced by half. The 3D Fourier transform of an object is divided into pairs of slices, one slice is scanned in an outgoing interleaved-spiral, initiated after a 90 degree pulse has been applied. The second slice is scanned in an ingoing interleaved-spiral, after a 180 degree pulse has been applied, thus reaching the slice origin at the echo time. (authors)

  13. Meteor head echoes - observations and models

    Directory of Open Access Journals (Sweden)

    A. Pellinen-Wannberg

    2005-01-01

    Full Text Available Meteor head echoes - instantaneous echoes moving with the velocities of the meteors - have been recorded since 1947. Despite many attempts, this phenomenon did not receive a comprehensive theory for over 4 decades. The High Power and Large Aperture (HPLA features, combined with present signal processing and data storage capabilities of incoherent scatter radars, may give an explanation for the old riddle. The meteoroid passage through the radar beam can be followed with simultaneous spatial-time resolution of about 100m-ms class. The current views of the meteor head echo process will be presented and discussed. These will be related to various EISCAT observations, such as dual-frequency target sizes, altitude distributions and vector velocities.

  14. J-NSE: Neutron spin echo spectrometer

    Directory of Open Access Journals (Sweden)

    Olaf Holderer

    2015-08-01

    Full Text Available Neutron Spin-Echo (NSE spectroscopy is well known as the only neutron scattering technique that achieves energy resolution of several neV. By using the spin precession of polarized neutrons in magnetic field one can measure tiny velocity changes of the individual neutron during the scattering process. Contrary to other inelastic neutron scattering techniques, NSE measures the intermediate scattering function S(Q,t in reciprocal space and time directly. The Neutron Spin-Echo spectrometer J-NSE, operated by JCNS, Forschungszentrum Jülich at the Heinz Maier-Leibnitz Zentrum (MLZ in Garching, covers a time range (2 ps to 200 ns on length scales accessible by small angle scattering technique. Along with conventional NSE spectroscopy that allows bulk measurements in transmission mode, J-NSE offers a new possibility - gracing incidence spin echo spectroscopy (GINSENS, developed to be used as "push-button" option in order to resolve the depth dependent near surface dynamics.

  15. Black hole ringdown echoes and howls

    Science.gov (United States)

    Nakano, Hiroyuki; Sago, Norichika; Tagoshi, Hideyuki; Tanaka, Takahiro

    2017-07-01

    Recently the possibility of detecting echoes of ringdown gravitational waves from binary black hole mergers was shown. The presence of echoes is expected if the black hole is surrounded by a mirror that reflects gravitational waves near the horizon. Here, we present slightly more sophisticated templates motivated by a waveform which is obtained by solving the linear perturbation equation around a Kerr black hole with a complete reflecting boundary condition in the stationary traveling wave approximation. We estimate that the proposed template can bring about a 10% improvement in the signal-to-noise ratio.

  16. Evolution of entanglement under echo dynamics

    International Nuclear Information System (INIS)

    Prosen, Tomaz; Znidaric, Marko; Seligman, Thomas H.

    2003-01-01

    Echo dynamics and fidelity are often used to discuss stability in quantum-information processing and quantum chaos. Yet fidelity yields no information about entanglement, the characteristic property of quantum mechanics. We study the evolution of entanglement in echo dynamics. We find qualitatively different behavior between integrable and chaotic systems on one hand and between random and coherent initial states for integrable systems on the other. For the latter the evolution of entanglement is given by a classical time scale. Analytic results are illustrated numerically in a Jaynes-Cummings model

  17. Short term memory in echo state networks

    OpenAIRE

    Jaeger, H.

    2001-01-01

    The report investigates the short-term memory capacity of echo state recurrent neural networks. A quantitative measure MC of short-term memory capacity is introduced. The main result is that MC 5 N for networks with linear Output units and i.i.d. input, where N is network size. Conditions under which these maximal memory capacities are realized are described. Several theoretical and practical examples demonstrate how the short-term memory capacities of echo state networks can be exploited for...

  18. How can dolphins recognize fish according to their echoes? A statistical analysis of fish echoes.

    Directory of Open Access Journals (Sweden)

    Yossi Yovel

    Full Text Available Echo-based object classification is a fundamental task of animals that use a biosonar system. Dolphins and porpoises should be able to rely on echoes to discriminate a predator from a prey or to select a desired prey from an undesired object. Many studies have shown that dolphins and porpoises can discriminate between objects according to their echoes. All of these studies however, used unnatural objects that can be easily characterized in human terminologies (e.g., metallic spheres, disks, cylinders. In this work, we collected real fish echoes from many angles of acquisition using a sonar system that mimics the emission properties of dolphins and porpoises. We then tested two alternative statistical approaches in classifying these echoes. Our results suggest that fish species can be classified according to echoes returning from porpoise- and dolphin-like signals. These results suggest how dolphins and porpoises can classify fish based on their echoes and provide some insight as to which features might enable the classification.

  19. Experimental separation of a frequency spin echo signal

    International Nuclear Information System (INIS)

    Bun'kov, Yu.M.; Dmitriev, V.V.

    1981-01-01

    To study systems with bound nuclear-electron precession CsMnF 2 antiferromagnetic light-plane monocrystal was investigated. Crystal orientation was carried out by roentgenoscopy. Measurements were performed at helium temperatures in the 500-700 MHz frequency range. A NMR pulsed spectrometer with generators of both resonance and doubled frequency was used to produce an echo signal (to study by the parametric echo method). It was shown that the theory of the formation of a frequency modulated echo (FM echo) did not fully describe the properties of the echo signals in systems with dynamic frequency shift (DFS). An intense spin echo signal, which formation was apparently connected with other nonlinear properties of the systems with nuclear-electron precession, was observed. The spin echo signal in magnetics with DFS, which properties correspond to notions of the frequency mechanism of echo formation, was experimentally separated. As a result of the investigations it had been possible to settle contradictions between the theory of FM echo formation and the experimental results for the last 9 years. It turned out that the mechanism of FM echo formation in the magnetics with bound nuclear-electron precession was effective only at large delay times between the pulses. In the range of small delays the FM echo is ''jammed'' by a gigantic echo signal of a nature different from that of the traditional FM signal. The constant of gigantic echo intensity drop at increasing delay between the pulses weakly depends on spin-spin relaxation time [ru

  20. Seabottom characterization using multibeam echosounder angular backscatter: An application of the composite roughness theory

    Digital Repository Service at National Institute of Oceanography (India)

    Chakraborty, B.; Schenke, H.W.; Kodagali, V.N.; Hagen, R.

    multibeam echosounding systems reveal significant results related to seabottom geological processes ([3] and references therein). Jackson et al., [1] had proposed simultaneous application of the two backscatter theories related to the large and small-scale... to acquire multibeam deep ocean seabottom backscatter data of higher angular range (62 20 14 incidence angle). However, with the commercial availability of the multibeam-Hydrosweep system [4], which operates at a 45 14 half fan width, it has become possible...

  1. U 1608-52: a Cornerstone in Our Understanding of the Khz Qpos

    Science.gov (United States)

    Mendez, Mariano

    We propose a series of ASM-triggered TOO observations of the atoll source 4U 1608-52 in outburst for a total of 450 ksec. These triggers are planned to maximize the probability of observing kHz QPOs and type- I X-ray bursts in this source. This is one of only 2 sources where the separation between the 2 simultaneous kHz QPOs varies significantly as a function of mass accretion rate. The detection of near-coherent oscillations during the bursts will provide a strong clue to the nature of the kHz QPOs in LMXBs, as it would make clear at which mass accretion level, if any, the kHz peak separation does approach the inferred spin rate.

  2. Comparison of multi-echo and single-echo gradient-recalled echo sequences for SPIO-enhanced Liver MRI at 3 T

    International Nuclear Information System (INIS)

    Choi, J.S.; Kim, M.-J.; Kim, J.H.; Choi, J.-Y.; Chung, Y.E.; Park, M.-S.; Kim, K.W.

    2010-01-01

    Aim: To assess the utility of a T2*-weighted, multi-echo data imaging combination sequenced on superparamagnetic iron oxide (SPIO)-enhanced liver magnetic resonance imaging (MRI) using a 3 T system. Materials and methods: Fifty patients underwent SPIO-enhanced MRI at 3 T using T2*-weighted, single-echo, gradient-recalled echo (GRE) sequences [fast imaging with steady precession; repetition time (TR)/echo time (TE), 126 ms/9 ms; flip angle, 30 o ] and multi-echo GRE (multi-echo data image combination) sequences (TR/TE, 186 ms/9 ms; flip angle, 30 o ). Three radiologists independently reviewed the images in a random order. The sensitivity and accuracy for the detection of focal hepatic lesions (a total of 76 lesions in 33 patients; 48 solid lesions, 28 non-solid lesions) were compared by analysing the area under the receiver operating characteristic curves. Image artefacts (flow artefacts, susceptibility artefacts, dielectric artefacts, and motion artefacts), lesion conspicuity, and overall image quality were evaluated according to a four-point scale: 1, poor; 2, fair; 3, good; 4, excellent. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the lesions were compared. Results: Image artefacts were more frequent with single-echo GRE (p < 0.05). The mean scale of image quality assessment for flow, susceptibility, dielectric, and motion artefacts were 2.76, 3.13, 3.42, and 2.89 with singe-echo, respectively, compared with 3.47, 3.43, 3.47, and 3.39, respectively, with multi-echo GRE. There was no significant difference in lesion conspicuity between single-echo (3.15) and multi-echo (3.30) GRE sequences. The overall image quality was significantly (p < 0.05) better with multi-echo (3.37) than with single-echo GRE (2.89). The mean SNR and CNR of the lesions were significantly (p < 0.05) higher on multi-echo (79 ± 23 and 128 ± 59, respectively) images than on single-echo (38 ± 11 and 102 ± 44, respectively) images. Lesion detection accuracy and

  3. Perancangan Prototipe Receiver Beacon Black Box Locator Acoustic 37,5 kHz Pingers

    OpenAIRE

    RUSTAMAJI RUSTAMAJI; PAULINE RAHMIATI; SARAH PERMATASARI

    2016-01-01

    ABSTRAK Ketika suatu pesawat terbang mengalami kecelakaan terjatuh ke dalam air, maka lokasi keberadaannya dapat dideteksi oleh alat yang disebut receiver beacon black box locator acoustic (pingers receiver). Pingers receiver berfungsi untuk menerima sinyal dengan frekuensi 37,5 kHz ± 1 kHz dari pingers transmitter atau Underwater Locator Beacon (ULB) yang berada pada black box pesawat. Dalam penelitian ini dibuat perancangan pingers receiver yang tersusun dari rangkaian Band Pass Filter (BPF...

  4. Perancangan Prototipe Transmitter Beacon Black Box Locator Acoustic 37.5 kHz Pingers

    OpenAIRE

    RUSTAMAJI RUSTAMAJI; KANIA SAWITRI; RUDI GUNAWAN

    2016-01-01

    ABSTRAK Pingers transmitter berfungsi untuk memancarkan sinyal atau getaran pulsa akustik pada black box. Frekuensi sinyal yang dipancarkan sebesar 37,5 kHz yang dimodulasikan oleh pulsa dengan durasi 10 ms setiap interval 1 second. Modulasi yang digunakan adalah modulasi on off keying. Dalam penelitian ini dibuat perancangan pingers transmitter yang tersusun atas rangkaian osilator, timer, inverter, switch dan rangkaian amplifier. Frekuensi 37,5 kHz tersebut dibangkitkan oleh rangkaian osi...

  5. Model-based seafloor characterization employing multi-beam angular backscatter data - A comparative study with dual-frequency single beam

    Digital Repository Service at National Institute of Oceanography (India)

    Haris, K.; Chakraborty, B.; De, C.; Desai, R.G.P.; Fernandes, W.A.

    developed by Jackson et al., 3 has been extensively applied to this approach. 4-6 In this work, characterization of seafloor sediments is achieved by employing a MB system (Simrad EM 1002) operable at 95 kHz. Here, the seafloor parameters are estimated... pre-formed beams data were acquired during two cruises using the EM1002 echo-sounding system installed onboard CRV Sagar Sukti (cruises: SASU-108 and 134). 13,14 Besides calculating the angular backscatter strengths using the MB system, the modeled...

  6. Pesticide Dashboard Help | ECHO | US EPA

    Science.gov (United States)

    The dashboards found on the Enforcement and Compliance History Online (ECHO) website are specialized to track both facility and agency performance as they relate to compliance with and enforcement of environmental standards under the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA).

  7. Long range echo classification for minehunting sonars

    NARCIS (Netherlands)

    Theije, P.A.M. de; Groen, J.; Sabel, J.C.

    2006-01-01

    This paper focesus on single-ping classification of sea mines, at a range of about 400 m, and combining a hull mounted sonar (HMS) and a propelled variable-depth sonar (PDVS). The deleoped classifier is trained and tested on a set of simulated realistic echoes of mines and non-mines. As the mines

  8. Electric Dipole Echoes and Noise-Induced Coherence

    International Nuclear Information System (INIS)

    Mestayer, J.J.; Zhao, W.; Lancaster, J.C.; Dunning, F.B.; Yoshida, S.; Reinhold, Carlos O.; Burgdorfer, J.

    2007-01-01

    The generation of echoes in the electric dipole moment of a Rydberg wavepacket precessing in an external electric field by reversal of the field is described. When the wavepacket experiences reversible dephasing, large echoes are observed pointing to strong refocusing of the wavepacket. The presence of irreversible dephasing leads to a reduction in the size of the echoes. The effect of irreversible dynamics on echoes is investigated using artificially synthesized noise. Methods to determine the decoherence rate are discussed

  9. In vivo endoscopic multi-beam optical coherence tomography

    Energy Technology Data Exchange (ETDEWEB)

    Standish, Beau A; Mariampillai, Adrian; Munce, Nigel R; Leung, Michael K K; Vitkin, I Alex [Deptartment of Medical Biophysics, University of Toronto, Toronto (Canada); Lee, Kenneth K C; Yang, Victor X D [Ontario Cancer Institute/University Health Network, Toronto (Canada)], E-mail: standish@ee.ryerson.ca

    2010-02-07

    A multichannel optical coherence tomography (multi-beam OCT) system and an in vivo endoscopic imaging probe were developed using a swept-source OCT system. The distal optics were micro-machined to produce a high numerical aperture, multi-focus fibre optic array. This combination resulted in a transverse design resolution of <10 {mu}m full width half maximum (FWHM) throughout the entire imaging range, while also increasing the signal intensity within the focus of the individual channels. The system was used in a pre-clinical rabbit study to acquire in vivo structural images of the colon and ex vivo images of the oesophagus and trachea. A good correlation between the structural multi-beam OCT images and H and E histology was achieved, demonstrating the feasibility of this high-resolution system and its potential for in vivo human endoscopic imaging.

  10. In vivo endoscopic multi-beam optical coherence tomography

    International Nuclear Information System (INIS)

    Standish, Beau A; Mariampillai, Adrian; Munce, Nigel R; Leung, Michael K K; Vitkin, I Alex; Lee, Kenneth K C; Yang, Victor X D

    2010-01-01

    A multichannel optical coherence tomography (multi-beam OCT) system and an in vivo endoscopic imaging probe were developed using a swept-source OCT system. The distal optics were micro-machined to produce a high numerical aperture, multi-focus fibre optic array. This combination resulted in a transverse design resolution of <10 μm full width half maximum (FWHM) throughout the entire imaging range, while also increasing the signal intensity within the focus of the individual channels. The system was used in a pre-clinical rabbit study to acquire in vivo structural images of the colon and ex vivo images of the oesophagus and trachea. A good correlation between the structural multi-beam OCT images and H and E histology was achieved, demonstrating the feasibility of this high-resolution system and its potential for in vivo human endoscopic imaging.

  11. Advanced Communication Technology Satellite (ACTS) multibeam antenna technology verification experiments

    Science.gov (United States)

    Acosta, Roberto J.; Larko, Jeffrey M.; Lagin, Alan R.

    1992-01-01

    The Advanced Communication Technology Satellite (ACTS) is a key to reaching NASA's goal of developing high-risk, advanced communications technology using multiple frequency bands to support the nation's future communication needs. Using the multiple, dynamic hopping spot beams, and advanced on board switching and processing systems, ACTS will open a new era in communications satellite technology. One of the key technologies to be validated as part of the ACTS program is the multibeam antenna with rapidly reconfigurable hopping and fixed spot beam to serve users equipped with small-aperature terminals within the coverage areas. The proposed antenna technology experiments are designed to evaluate in-orbit ACTS multibeam antenna performance (radiation pattern, gain, cross pol levels, etc.).

  12. Mosaic of gridded multibeam bathymetry and bathymetry derived from multispectral IKONOS satellite imagery of Tutuila Island, American Samoa, USA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Gridded multibeam bathymetry is integrated with bathymetry derived from multispectral IKONOS satellite data. Gridded (5 m cell size) multibeam bathymetry collected...

  13. Mosaic of gridded multibeam bathymetry and bathymetry derived from multispectral IKONOS satellite imagery of Rose Atoll, American Samoa, USA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Gridded multibeam bathymetry is integrated with bathymetry derived from multispectral IKONOS satellite data. Gridded (5 m cell size) multibeam bathymetry were...

  14. Vibration piezoelectric energy harvester with multi-beam

    Energy Technology Data Exchange (ETDEWEB)

    Cui, Yan, E-mail: yanc@dlut.edu.cn; Zhang, Qunying, E-mail: zhangqunying89@126.com; Yao, Minglei, E-mail: yaomingleiok@126.com [Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education, Dalian University of Technology, 116024, Dalian, Liaoning Province (China); Dong, Weijie, E-mail: dongwj@dlut.edu.cn [School of Electronic and Information Engineering, Dalian University of Technology, 116024, Dalian, Liaoning Province (China); Gao, Shiqiao, E-mail: gaoshq@bit.edu.cn [State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, 100081, Beijing Province (China)

    2015-04-15

    This work presents a novel vibration piezoelectric energy harvester, which is a micro piezoelectric cantilever with multi-beam. The characteristics of the PZT (Pb(Zr{sub 0.53}Ti{sub 0.47})O{sub 3}) thin film were measured; XRD (X-ray diffraction) pattern and AFM (Atomic Force Microscope) image of the PZT thin film were measured, and show that the PZT (Pb(Zr{sub 0.53}Ti{sub 0.47})O{sub 3}) thin film is highly (110) crystal oriented; the leakage current is maintained in nA magnitude, the residual polarisation Pr is 37.037 μC/cm{sup 2}, the coercive field voltage Ec is 27.083 kV/cm, and the piezoelectric constant d{sub 33} is 28 pC/N. In order to test the dynamic performance of the energy harvester, a new measuring system was set up. The maximum output voltage of the single beam of the multi-beam can achieve 80.78 mV under an acceleration of 1 g at 260 Hz of frequency; the maximum output voltage of the single beam of the multi-beam is almost 20 mV at 1400 Hz frequency. .

  15. Multibeam 3D Underwater SLAM with Probabilistic Registration

    Directory of Open Access Journals (Sweden)

    Albert Palomer

    2016-04-01

    Full Text Available This paper describes a pose-based underwater 3D Simultaneous Localization and Mapping (SLAM using a multibeam echosounder to produce high consistency underwater maps. The proposed algorithm compounds swath profiles of the seafloor with dead reckoning localization to build surface patches (i.e., point clouds. An Iterative Closest Point (ICP with a probabilistic implementation is then used to register the point clouds, taking into account their uncertainties. The registration process is divided in two steps: (1 point-to-point association for coarse registration and (2 point-to-plane association for fine registration. The point clouds of the surfaces to be registered are sub-sampled in order to decrease both the computation time and also the potential of falling into local minima during the registration. In addition, a heuristic is used to decrease the complexity of the association step of the ICP from O ( n 2 to O ( n . The performance of the SLAM framework is tested using two real world datasets: First, a 2.5D bathymetric dataset obtained with the usual down-looking multibeam sonar configuration, and second, a full 3D underwater dataset acquired with a multibeam sonar mounted on a pan and tilt unit.

  16. Systems analysis for modular versus multi-beam HIF drivers

    International Nuclear Information System (INIS)

    Meier, W.R.; Logan, B.G.

    2004-01-01

    Previous modeling for HIF drivers concentrated on designs in which 100 or more beams are grouped in an array and accelerated through a common set of induction cores. The total beam energy required by the target is achieved by the combination of final ion energy, current per beam and number of beams. Economic scaling favors a large number of small (∼1 cm dia.) beams. An alternative architecture has now been investigated, which we refer to as a modular driver. In this case, the driver is subdivided into many (>10) independent accelerators with one or many beams each. A key objective of the modular driver approach is to be able to demonstrate all aspects of the driver (source-to-target) by building a single, lower cost module compared to a full-scale, multi-beam driver. We consider and compare several design options for the modular driver including single-beam designs with solenoid instead of quadrupole magnets in order to transport the required current per module in a single beam, solenoid/quad combinations, and multi-beam, all-quad designs. The drivers are designed to meet the requirements of the hybrid target, which can accommodate a larger spot size than the distributed radiator target that was used for the Robust Point Design. We compare the multi-beam and modular driver configuration for a variety and assumptions and identify key technology advances needed for the modular design

  17. THE SPECTRAL-TIMING PROPERTIES OF UPPER AND LOWER kHz QPOs

    Energy Technology Data Exchange (ETDEWEB)

    Peille, Philippe; Barret, Didier [Université de Toulouse, UPS-OMP, IRAP, Toulouse (France); Uttley, Phil, E-mail: philippe.peille@irap.omp.eu [Anton Pannekoek Institute, University of Amsterdam, Postbus 94249, 1090 GE Amsterdam (Netherlands)

    2015-10-01

    Soft lags from the emission of the lower kilohertz quasi-periodic oscillations (kHz QPOs) of neutron star low-mass X-ray binaries have been reported from 4U1608-522 and 4U1636-536. Those lags hold prospects for constraining the origin of the QPO emission. In this paper, we investigate the spectral-timing properties of both the lower and upper kHz QPOs from the neutron star binary 4U1728-34, using the entire Rossi X-Ray Timing Explorer archive on this source. We show that the lag-energy spectra of the two QPOs are systematically different: while the lower kHz QPO shows soft lags, the upper kHz QPO shows either a flat lag-energy spectrum or hard variations lagging softer variations. This suggests two different QPO-generation mechanisms. We also performed the first spectral deconvolution of the covariance spectra of both kHz QPOs. The QPO spectra are consistent with Comptonized blackbody emission, similar to the one found in the time-averaged spectrum, but with a higher seed-photon temperature, suggesting that a more compact inner region of the Comptonization layer (boundary/spreading layer, corona) is responsible for the QPO emission. Considering our results together with other recent findings, this leads us to the hypothesis that the lower kHz QPO signal is generated by coherent oscillations of the compact boundary layer region itself. The upper kHz QPO signal may then be linked to less-coherent accretion-rate variations produced in the inner accretion disk, and is then detected when they reach the boundary layer.

  18. Effects of the India–Pakistan border earthquake on the atmospherics at 6 kHz and 9 kHz recorded at Tripura

    Directory of Open Access Journals (Sweden)

    Sudarsan Barui

    2011-04-01

    Full Text Available The unusual variations observed in the records of the integrated field intensity of the atmospherics (IFIA at 6 kHz and 9 kHz at Agartala, Tripura, in the north-eastern state of India (latitude, 23˚ N; longitude, 91.4˚ E during the large earthquake on October 8, 2005 at Muzaffarabad (latitude, 34.53˚ N; longitude, 73.58˚ E in Kashmir in Pakistan are here analyzed. Spiky variations in the IFIA at 6 kHz and 9 kHz were observed several days previous to the day of the earthquake (from midnight, September 28, 2005. The effects persisted for some days, decayed gradually, and eventually ceased on October 31, 2005. The spikes are distinctly superimposed on the ambient level

  19. A DC excited waveguide multibeam CO2 laser using high frequency ...

    Indian Academy of Sciences (India)

    High power industrial multibeam CO2 lasers consist of a large number of closely packed ... by producing pre-ionization using an auxiliary high frequency pulsed ... of few kilowatts output power, multibeam technique is used [2]. .... gas mixture of CO2, N2 and He enters in each discharge tube individually from .... Commercial.

  20. Workshop on neutron spin-echo

    Energy Technology Data Exchange (ETDEWEB)

    Aynajian, P.; Habicht, K.; Keller, Th.; Keimer, B.; Mezei, F.; Monkenbusch, M.; Allgaier, J.; Richter, D.; Fetters, L.J.; Muller, K.; Kreiling, S.; Dehnicke, K.; Greiner, A.; Ehlers, G.; Arbe, A.; Colmenero, J.; Richter, D.; Farago, B.; Monkenbusch, M.; Ohl, M.; Butzek, M.; Kozielewski, T.; Monkenbusch, M.; Richter, D.; Pappas, C.; Hillier, A.; Manuel, P.; Cywinski, R.; Bentley, P.; Alba, M.; Mezei, F.; Campbell, I.A.; Zimmermann, U.; Ellis, J.; Jobic, H.; Pickup, R.M.; Pappas, C.; Farago, B.; Cywinski, R.; Haussler, W.; Holderer, O.; Frielinghaus, H.; Byelov, D.; Monkenbusch, M.; Allgaier, J.; Richter, D.; Egger, H.; Hellweg, Th.; Malikova, N.; Cadene, A.; Marry, V.; Dubois, E.; Turq, P.; Gardner, J.S.; Ehlers, G.; Bramwell, St.S.; Grigoriev, S.; Kraan, W.; Rekveldt, T.; Bouwman, W.; Van Dijk, N.; Falus, P.; Vorobiev, A.; Major, J.; Felcher, G.P.; Te-velthuis, S.; Dosch, H.; Vorobiev, A.; Dridi, M.H.; Major, J.; Dosch, H.; Falus, P.; Felcher, G.P.; Te Velthuis, S.G.E.; Bleuel, M.; Broell, M.; Lang, E.; Littrell, K.; Gahler, R.; Lal, J.; Lauter, H.; Toperverg, B.; Lauter, V.; Jernenkov, M.; Stueber, S.; Enderle, M.; Janoschek, M.; Keller, Th.; Klimko, S.; Boeni, P.; Nagao, M.; Yamada, N.; Kawabata, Y.; Seto, H.; Takeda, T.; Yoshizawa, H.; Yoshida, K.; Yamaguchi, T.; Bellissent-Funel, M.C.; Longeville, St

    2005-07-01

    This document gathers the abstracts of most papers presented at the workshop. Neutron spin-echo (NSE) spectroscopy is a well established technique with a growing expert user community, the aim of the meeting was to discuss the latest achievements in neutron spin-echo science and instrumentation. One of the applications presented is the investigation on the microscopic scale of the dynamics of water in montmorillonite clays with Na{sup +} and Cs{sup +} ions in monolayer and bilayer states. The NSE technique has been used in the normal and resonance modes. NSE results show consistently slower dynamics (higher relaxation times) than both time-of-flight technique (TOF) and classical molecular dynamics simulations (MD). In the present TOF and NSE experiments, anisotropy of the water motion in the interlayer is almost impossible to detect, due to the use of powder samples and insufficient resolution. (A.C.)

  1. The EChO science case

    DEFF Research Database (Denmark)

    Tinetti, Giovanna; Drossart, Pierre; Eccleston, Paul

    2015-01-01

    in the Solar System. Observations to date have shown that our Solar System is certainly not representative of the general population of planets in our Milky Way. The key science questions that urgently need addressing are therefore: What are exoplanets made of? Why are planets as they are? How do planetary....... The mission can target super-Earths, Neptune-like, and Jupiter-like planets, in the very hot to temperate zones (planet temperatures of 300–3000 K) of F to M-type host stars. The EChO core science would be delivered by a three-tier survey. The EChO Chemical Census: This is a broad survey of a few...

  2. Improved detection and mapping of deepwater hydrocarbon seeps: optimizing multibeam echosounder seafloor backscatter acquisition and processing techniques

    Science.gov (United States)

    Mitchell, Garrett A.; Orange, Daniel L.; Gharib, Jamshid J.; Kennedy, Paul

    2018-02-01

    Marine seep hunting surveys are a current focus of hydrocarbon exploration surveys due to recent advances in offshore geophysical surveying, geochemical sampling, and analytical technologies. Hydrocarbon seeps are ephemeral, small, discrete, and therefore difficult to sample on the deep seafloor. Multibeam echosounders are an efficient seafloor exploration tool to remotely locate and map seep features. Geophysical signatures from hydrocarbon seeps are acoustically-evident in bathymetric, seafloor backscatter, midwater backscatter datasets. Interpretation of these signatures in backscatter datasets is a fundamental component of commercial seep hunting campaigns. Degradation of backscatter datasets resulting from environmental, geometric, and system noise can interfere with the detection and delineation of seeps. We present a relative backscatter intensity normalization method and an oversampling acquisition technique that can improve the geological resolvability of hydrocarbon seeps. We use Green Canyon (GC) Block 600 in the Northern Gulf of Mexico as a seep calibration site for a Kongsberg EM302 30 kHz MBES prior to the start of the Gigante seep hunting program to analyze these techniques. At GC600, we evaluate the results of a backscatter intensity normalization, assess the effectiveness of 2X seafloor coverage in resolving seep-related features in backscatter data, and determine the off-nadir detection limits of bubble plumes using the EM302. Incorporating these techniques into seep hunting surveys can improve the detectability and sampling of seafloor seeps.

  3. Improved detection and mapping of deepwater hydrocarbon seeps: optimizing multibeam echosounder seafloor backscatter acquisition and processing techniques

    Science.gov (United States)

    Mitchell, Garrett A.; Orange, Daniel L.; Gharib, Jamshid J.; Kennedy, Paul

    2018-06-01

    Marine seep hunting surveys are a current focus of hydrocarbon exploration surveys due to recent advances in offshore geophysical surveying, geochemical sampling, and analytical technologies. Hydrocarbon seeps are ephemeral, small, discrete, and therefore difficult to sample on the deep seafloor. Multibeam echosounders are an efficient seafloor exploration tool to remotely locate and map seep features. Geophysical signatures from hydrocarbon seeps are acoustically-evident in bathymetric, seafloor backscatter, midwater backscatter datasets. Interpretation of these signatures in backscatter datasets is a fundamental component of commercial seep hunting campaigns. Degradation of backscatter datasets resulting from environmental, geometric, and system noise can interfere with the detection and delineation of seeps. We present a relative backscatter intensity normalization method and an oversampling acquisition technique that can improve the geological resolvability of hydrocarbon seeps. We use Green Canyon (GC) Block 600 in the Northern Gulf of Mexico as a seep calibration site for a Kongsberg EM302 30 kHz MBES prior to the start of the Gigante seep hunting program to analyze these techniques. At GC600, we evaluate the results of a backscatter intensity normalization, assess the effectiveness of 2X seafloor coverage in resolving seep-related features in backscatter data, and determine the off-nadir detection limits of bubble plumes using the EM302. Incorporating these techniques into seep hunting surveys can improve the detectability and sampling of seafloor seeps.

  4. Evaluation of Four Supervised Learning Methods for Benthic Habitat Mapping Using Backscatter from Multi-Beam Sonar

    Directory of Open Access Journals (Sweden)

    Jacquomo Monk

    2012-11-01

    Full Text Available An understanding of the distribution and extent of marine habitats is essential for the implementation of ecosystem-based management strategies. Historically this had been difficult in marine environments until the advancement of acoustic sensors. This study demonstrates the applicability of supervised learning techniques for benthic habitat characterization using angular backscatter response data. With the advancement of multibeam echo-sounder (MBES technology, full coverage datasets of physical structure over vast regions of the seafloor are now achievable. Supervised learning methods typically applied to terrestrial remote sensing provide a cost-effective approach for habitat characterization in marine systems. However the comparison of the relative performance of different classifiers using acoustic data is limited. Characterization of acoustic backscatter data from MBES using four different supervised learning methods to generate benthic habitat maps is presented. Maximum Likelihood Classifier (MLC, Quick, Unbiased, Efficient Statistical Tree (QUEST, Random Forest (RF and Support Vector Machine (SVM were evaluated to classify angular backscatter response into habitat classes using training data acquired from underwater video observations. Results for biota classifications indicated that SVM and RF produced the highest accuracies, followed by QUEST and MLC, respectively. The most important backscatter data were from the moderate incidence angles between 30° and 50°. This study presents initial results for understanding how acoustic backscatter from MBES can be optimized for the characterization of marine benthic biological habitats.

  5. Multibeam collection for EW9104: Multibeam data collected aboard Maurice Ewing from 1991-06-16 to 1991-07-27, Papeete, French Polynesia to Valparaiso, Chile

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  6. Multibeam collection for EW0212: Multibeam data collected aboard Maurice Ewing from 2002-11-17 to 2002-11-20, Puntarenas, Costa Rica to Balboa, Panama

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  7. Multibeam collection for EW0007: Multibeam data collected aboard Maurice Ewing from 2000-07-15 to 2000-08-16, St. John's, Newfoundland to Newark, NJ

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  8. Multibeam collection for EW0209: Multibeam data collected aboard Maurice Ewing from 2002-09-07 to 2002-09-11, Newport, OR to San Diego, CA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  9. Multibeam collection for EW0201: Multibeam data collected aboard Maurice Ewing from 2002-01-31 to 2002-02-14, Hobart, Tasmania to Apra, Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  10. Multibeam collection for EW9414: Multibeam data collected aboard Maurice Ewing from 1994-10-03 to 1994-10-10, Coos Bay, OR to Long Beach, CA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  11. Multibeam collection for EW9415: Multibeam data collected aboard Maurice Ewing from 1994-10-13 to 1994-10-21, Long Beach, CA to San Diego, CA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  12. Multibeam collection for EW9506: Multibeam data collected aboard Maurice Ewing from 1995-07-19 to 1995-07-26, San Francisco, CA to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  13. Multibeam collection for EW9504: Multibeam data collected aboard Maurice Ewing from 1995-05-17 to 1995-06-07, San Diego, CA to Eureka, CA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  14. Multibeam collection for EW9601: Multibeam data collected aboard Maurice Ewing from 1996-02-07 to 1996-03-13, Lyttelton, New Zealand to Lyttelton, New Zealand

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  15. Multibeam collection for EW9416: Multibeam data collected aboard Maurice Ewing from 1994-11-03 to 1994-11-08, Unknown Port to Unknown Port

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  16. Multibeam collection for EW9603: Multibeam data collected aboard Maurice Ewing from 1996-05-14 to 1996-05-31, Papeete, French Polynesia to Balboa, Panama

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  17. Multibeam collection for EW9309: Multibeam data collected aboard Maurice Ewing from 1993-11-20 to 1993-12-26, Unknown Port to Unknown Port

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  18. Multibeam collection for EW9401: Multibeam data collected aboard Maurice Ewing from 1994-01-04 to 1994-02-13, Montevideo, Uruguay to Salvador de Bahia, Brazil

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  19. Multibeam collection for EW9303: Multibeam data collected aboard Maurice Ewing from 1993-06-26 to 1993-07-20, Reykjavik, Iceland to Woods Hole, MA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  20. Multibeam collection for EW0102: Multibeam data collected aboard Maurice Ewing from 2001-03-10 to 2001-04-05, Charleston, SC to San Juan, Puerto Rico

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  1. Multibeam collection for EW9708: Multibeam data collected aboard Maurice Ewing from 1997-11-06 to 1997-12-06, Manzanillo, Mexico to San Diego, CA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  2. Multibeam collection for EW9706: Multibeam data collected aboard Maurice Ewing from 1997-08-20 to 1997-09-05, Lisbon, Portugal to Cristobal, Panama

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  3. Multibeam collection for EW0211: Multibeam data collected aboard Maurice Ewing from 2002-11-08 to 2002-11-12, Manzanillo, Mexico to Puntarenas, Costa Rica

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  4. Multibeam collection for EW9803: Multibeam data collected aboard Maurice Ewing from 1998-03-15 to 1998-04-06, Bridgetown, Barbados to Norfolk, VA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  5. Multibeam collection for EW9501: Multibeam data collected aboard Maurice Ewing from 1995-02-16 to 1995-03-21, Tampa, FL to Balboa, Panama

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  6. Multibeam collection for EW9903: Multibeam data collected aboard Maurice Ewing from 1999-03-10 to 1999-04-12, Cristobal, Panama to San Diego, CA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  7. Multibeam collection for EW0103: Multibeam data collected aboard Maurice Ewing from 2001-04-08 to 2001-04-12, San Juan, Puerto Rico to Colon, Panama

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  8. Multibeam collection for EW9910: Multibeam data collected aboard Maurice Ewing from 1999-08-30 to 1999-09-15, Agana, Guam to Lae, Papua New Guinea

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  9. Multibeam collection for EW0206: Multibeam data collected aboard Maurice Ewing from 2002-06-14 to 2002-07-02, Kodiak, AK to Astoria, OR

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  10. Multibeam collection for EW0409: Multibeam data collected aboard Maurice Ewing from 2004-09-28 to 2004-10-14, Kodiak, AK to Astoria, OR

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  11. Multibeam collection for EW9806: Multibeam data collected aboard Maurice Ewing from 1998-07-02 to 1998-08-13, Halifax, Canada to Halifax, Canada

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  12. Multibeam collection for EW0008: Multibeam data collected aboard Maurice Ewing from 2000-09-02 to 2000-10-17, Unknown Port to Unknown Port

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  13. Multibeam collection for EW9301: Multibeam data collected aboard Maurice Ewing from 1993-05-16 to 1993-05-23, Jacksonville, FL to Woods Hole, MA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  14. Multibeam collection for EW0402: Multibeam data collected aboard Maurice Ewing from 2004-02-27 to 2004-03-01, Progresso, Mexico to Gulfport, MS

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  15. Multibeam collection for EW9608: Multibeam data collected aboard Maurice Ewing from 1996-10-18 to 1996-11-16, Unknown Port to Unknown Port

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  16. Multibeam collection for EW9002: Multibeam data collected aboard Maurice Ewing from 1990-06-20 to 1990-06-24, Miami, FL to Port Everglades, FL

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  17. Multibeam collection for EW9707: Multibeam data collected aboard Maurice Ewing from 1997-09-09 to 1997-10-24, Balboa, Panama to Manzanillo, Mexico

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  18. Multibeam collection for EW0301: Multibeam data collected aboard Maurice Ewing from 2003-04-13 to 2003-05-10, Norfolk, VA to San Juan, Puerto Rico

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  19. Multibeam collection for EW9404: Multibeam data collected aboard Maurice Ewing from 1994-04-09 to 1994-04-14, Bridgetown, Barbados to Cristobal, Panama

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  20. Multibeam collection for EW9304: Multibeam data collected aboard Maurice Ewing from 1993-08-01 to 1993-08-09, Woods Hole, MA to Bridgetown, Barbados

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  1. Multibeam collection for EW9907: Multibeam data collected aboard Maurice Ewing from 1999-06-18 to 1999-07-19, Yokohama, Japan to Kochi, Japan

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  2. Multibeam collection for EW9102: Multibeam data collected aboard Maurice Ewing from 1991-03-21 to 1991-05-07, Punta Arenas, Chile to Papeete, French Polynesia

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  3. Multibeam collection for EW0203: Multibeam data collected aboard Maurice Ewing from 2002-03-28 to 2002-04-25, Guam to Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  4. Multibeam collection for EW9802: Multibeam data collected aboard Maurice Ewing from 1998-02-15 to 1998-03-12, Honolulu, HI to Bridgetown, Barbados

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  5. Multibeam collection for EW9502: Multibeam data collected aboard Maurice Ewing from 1995-03-27 to 1995-04-27, Balboa, Panama to Manzanillo, Mexico

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  6. Multibeam collection for EW9405: Multibeam data collected aboard Maurice Ewing from 1994-04-20 to 1994-05-14, Unknown Port to Unknown Port

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  7. Multibeam collection for EW0005: Multibeam data collected aboard Maurice Ewing from 2000-05-27 to 2000-06-27, Puntarenas, Costa Rica to Balboa, Panama

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  8. Multibeam collection for EW9206: Multibeam data collected aboard Maurice Ewing from 1992-05-27 to 1992-05-31, Cristobal, Panama to Bridgetown, Barbados

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  9. Multibeam collection for EW0406: Multibeam data collected aboard Maurice Ewing from 2004-06-23 to 2004-07-09, Tampa, FL to San Diego, CA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  10. Multibeam collection for EW0104: Multibeam data collected aboard Maurice Ewing from 2001-04-14 to 2001-05-19, Cristobal, Panama to Costa Rica

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  11. Multibeam collection for EW0403: Multibeam data collected aboard Maurice Ewing from 2004-04-07 to 2004-04-13, Mobile, AL to San Juan, Puerto Rico

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  12. Multibeam collection for 650610_p_100: Multibeam data collected aboard Mary Sears from 2010-08-08 to 2010-09-03, Unknown Port to Unknown Port

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  13. Multibeam collection for SEA1201P: Multibeam data collected aboard Mary Sears from 2012-03-20 to 2012-05-11, Unknown Port to Unknown Port

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  14. Multibeam collection for RR1208: Multibeam data collected aboard Roger Revelle from 2012-06-28 to 2012-07-17, Danang, Vietnam to Apia, Samoa

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  15. Multibeam collection for AT21-02: Multibeam data collected aboard Atlantis from 2012-06-01 to 2012-06-17, Bridgetown, Barbados to Bridgetown, Barbados

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  16. Multibeam collection for MV1012: Multibeam data collected aboard Melville from 2010-09-25 to 2010-10-03, San Diego, CA to San Diego, CA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  17. Multibeam collection for AT26-15: Multibeam data collected aboard Atlantis from 2014-05-21 to 2014-06-14, Gulfport, MS to St. Petersburg, FL

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  18. Multibeam collection for FK140625: Multibeam data collected aboard Falkor from 2014-06-25 to 2014-07-07, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  19. Multibeam collection for SEAW02RR: Multibeam data collected aboard Roger Revelle from 2001-02-17 to 2001-02-25, San Diego, CA to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  20. Multibeam collection for MV1302: Multibeam data collected aboard Melville from 2013-02-01 to 2013-02-15, Apra, Guam to Apra, Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  1. Multibeam collection for MGL1205: Multibeam data collected aboard Marcus G. Langseth from 2012-03-04 to 2012-03-21, Apra, Guam to Apra, Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  2. Multibeam collection for MGL1204: Multibeam data collected aboard Marcus G. Langseth from 2012-02-02 to 2012-02-29, Apra, Guam to Apra, Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  3. Multibeam collection for B00027: Multibeam data collected aboard Surveyor from 1985-09-03 to 1985-09-15, Seattle, WA to Seattle, WA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  4. Multibeam collection for A131L11: Multibeam data collected aboard Atlantis II from 1994-03-10 to 1994-03-22, Acapulco, Mexico to Manzanillo, Mexico

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  5. Multibeam collection for KN203-02: Multibeam data collected aboard Knorr from 2011-08-22 to 2011-09-22, Reykjavik, Iceland to Isafjorour, Iceland

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  6. Multibeam collection for EW0303: Multibeam data collected aboard Maurice Ewing from 2003-05-28 to 2003-06-24, Gulfport, MS to Galveston, TX

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  7. Multibeam collection for EX1004: Multibeam data collected aboard Okeanos Explorer from 2010-06-08 to 2010-08-19, Apra, Guam to Apra, Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  8. Multibeam collection for FK150523: Multibeam data collected aboard Falkor from 2015-05-23 to 2015-06-22, Singapore, Singapore to Padang, Indonesia

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  9. Multibeam collection for BD0601: Multibeam data collected aboard Bowditch from 2006-10-16 to 2006-11-11, Okinawa, Japan to Apra, Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  10. Multibeam collection for TN273: Multibeam data collected aboard Thomas G. Thompson from 2011-12-22 to 2012-01-22, Apra, Guam to Apra, Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  11. Multibeam collection for TUNE06WT: Multibeam data collected aboard Thomas Washington from 1991-10-31 to 1991-12-02, Kwajalein, Marshall Islands to Apra, Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  12. Multibeam collection for EX1005: Multibeam data collected aboard Okeanos Explorer from 2010-08-23 to 2010-09-05, Apra, Guam to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  13. Multibeam collection for MGLN36MV: Multibeam data collected aboard Melville from 2008-04-05 to 2008-04-26, Apra, Guam to Apra, Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  14. Multibeam collection for KM1004: Multibeam data collected aboard Kilo Moana from 2010-02-26 to 2010-03-11, Honolulu, HI to Apra, Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  15. Multibeam collection for EX1604: Multibeam data collected aboard Okeanos Explorer from 2016-03-23 to 2016-04-13, Kwajalein, Marshall Islands to Apra, Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  16. Multibeam collection for MGL1206: Multibeam data collected aboard Marcus G. Langseth from 2012-03-24 to 2012-04-16, Apra, Guam to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  17. Multibeam collection for MGLN01MV: Multibeam data collected aboard Melville from 2006-03-22 to 2006-04-11, San Diego, CA to Apra, Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  18. Multibeam collection for FK151221: Multibeam data collected aboard Falkor from 2015-12-21 to 2016-01-05, Apra, Guam to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  19. Multibeam collection for FK141109: Multibeam data collected aboard Falkor from 2014-11-09 to 2014-12-09, Apra, Guam to Apra, Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  20. Multibeam collection for FK151121: Multibeam data collected aboard Falkor from 2015-11-21 to 2015-12-17, Apra, Guam to Apra, Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  1. Multibeam collection for KM0913: Multibeam data collected aboard Kilo Moana from 2009-06-07 to 2009-06-18, Apra, Guam to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  2. Multibeam collection for EX1003: Multibeam data collected aboard Okeanos Explorer from 2010-05-19 to 2010-06-03, Honolulu, HI to Apra, Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  3. Multibeam collection for TN272: Multibeam data collected aboard Thomas G. Thompson from 2011-11-05 to 2011-12-17, Honolulu, HI to Apra, Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  4. Multibeam collection for FK141215: Multibeam data collected aboard Falkor from 2014-12-15 to 2014-12-21, Apra, Guam to Apra, Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  5. Multibeam collection for TN233: Multibeam data collected aboard Thomas G. Thompson from 2009-04-20 to 2009-05-01, Apra, Guam to Apia, Samoa

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  6. Multibeam collection for MGL1202: Multibeam data collected aboard Marcus G. Langseth from 2012-01-09 to 2012-01-24, Honolulu, HI to Apra, Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  7. Multibeam collection for TUNE08WT: Multibeam data collected aboard Thomas Washington from 1992-01-01 to 1992-01-31, Apra, Guam to Majuro, Marshall Islands

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  8. Multibeam collection for KM1021: Multibeam data collected aboard Kilo Moana from 2010-10-17 to 2010-11-09, Honolulu, HI to Apra, Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  9. Multibeam collection for MV1301: Multibeam data collected aboard Melville from 2013-01-16 to 2013-01-29, Honolulu, HI to Apra, Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  10. Multibeam collection for TN275: Multibeam data collected aboard Thomas G. Thompson from 2012-02-11 to 2012-02-21, Apra, Guam to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  11. Multibeam collection for Heceta: Multibeam data collected aboard Ocean Alert from 1998-05-18 to 1998-05-23, Unknown Port to Unknown Port

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  12. Multibeam collection for RR0914: Multibeam data collected aboard Roger Revelle from 2009-11-06 to 2009-11-19, Apra, Guam to Nuku'alofa, Tonga

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  13. Multibeam collection for EW0410: Multibeam data collected aboard Maurice Ewing from 2004-10-21 to 2004-11-03, Astoria, OR to San Diego, CA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  14. Multibeam collection for TN290B: Multibeam data collected aboard Thomas G. Thompson from 2013-01-16 to 2013-01-20, Seattle, WA to Seattle, WA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  15. Multibeam collection for KM1201: Multibeam data collected aboard Kilo Moana from 2012-01-04 to 2012-01-07, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  16. Multibeam collection for TN267: Multibeam data collected aboard Thomas G. Thompson from 2011-07-29 to 2011-08-09, Seattle, WA to Seattle, WA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  17. Multibeam collection for AMAT04RR: Multibeam data collected aboard Roger Revelle from 2006-04-18 to 2006-05-23, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  18. Multibeam collection for KN205: Multibeam data collected aboard Knorr from 2012-02-22 to 2012-03-17, Woods Hole, MA to Woods Hole, MA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  19. Multibeam collection for KN184: Multibeam data collected aboard Knorr from 2006-08-07 to 2006-08-21, Woods Hole, MA to Woods Hole, MA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  20. Multibeam collection for KNOX11RR: Multibeam data collected aboard Roger Revelle from 2007-11-07 to 2007-11-24, Victoria, Seychelles to Port Louis, Mauritius

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  1. Multibeam collection for EW0112: Multibeam data collected aboard Maurice Ewing from 2001-10-06 to 2001-10-23, Seychelles to Fremantle, Australia

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  2. Multibeam collection for KN162L15: Multibeam data collected aboard Knorr from 2001-05-07 to 2001-05-20, Seychelles to Istanbul, Turkey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  3. Multibeam collection for KNOX06RR: Multibeam data collected aboard Roger Revelle from 2007-06-18 to 2007-08-06, Phuket, Thailand to Singapore

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  4. Multibeam collection for cv11_seai: Multibeam data collected aboard Celtic Voyager from 2011-04-21 to 2011-04-29, Unknown Port to Unknown Port

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  5. Multibeam collection for KM1006: Multibeam data collected aboard Kilo Moana from 2010-04-04 to 2010-04-14, Apra, Guam to Suva, Fiji

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  6. Multibeam collection for KIWI10RR: Multibeam data collected aboard Roger Revelle from 1998-03-24 to 1998-04-29, Lyttelton, New Zealand to Suva, Fiji

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  7. Multibeam collection for KM0416: Multibeam data collected aboard Kilo Moana from 2004-08-28 to 2004-09-07, Honolulu, HI to Suva, Fiji

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  8. Multibeam collection for MGLN06MV: Multibeam data collected aboard Melville from 2006-07-21 to 2006-08-31, Rabaul, Papua New Guinea to Suva, Fiji

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  9. Multibeam collection for RR0916: Multibeam data collected aboard Roger Revelle from 2009-12-09 to 2009-12-15, Suva, Fiji to Nuku'alofa, Tonga

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  10. Multibeam collection for KM0704: Multibeam data collected aboard Kilo Moana from 2007-04-19 to 2007-04-30, Suva, Fiji to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  11. Multibeam collection for RR0915: Multibeam data collected aboard Roger Revelle from 2009-11-21 to 2009-12-05, Nuku'alofa, Tonga to Suva, Fiji

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  12. Multibeam collection for SO99: Multibeam data collected aboard Sonne from 1995-01-07 to 1995-01-14, Manila, Philippines to Suva, Fiji

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  13. Multibeam collection for TUIM08MV: Multibeam data collected aboard Melville from 2005-07-01 to 2005-07-11, Suva, Fiji to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  14. Multibeam collection for BMRG08MV: Multibeam data collected aboard Melville from 1996-05-07 to 1996-06-08, Suva, Fiji to Pago Pago, American Samoa

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  15. Multibeam collection for TUIM06MV: Multibeam data collected aboard Melville from 2005-05-15 to 2005-06-02, Suva, Fiji to Suva, Fiji

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  16. Multibeam collection for EW0002: Multibeam data collected aboard Maurice Ewing from 2000-02-05 to 2000-02-28, Lyttelton, New Zealand to Suva, Fiji

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  17. Multibeam collection for KM0409: Multibeam data collected aboard Kilo Moana from 2004-03-24 to 2004-04-03, Honolulu, HI to Suva, Fiji

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  18. Multibeam collection for TUIM05MV: Multibeam data collected aboard Melville from 2005-04-05 to 2005-05-11, Nuku'alofa, Tonga to Suva, Fiji

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  19. Multibeam collection for KM1007: Multibeam data collected aboard Kilo Moana from 2010-04-16 to 2010-04-25, Suva, Fiji to Apia, Samoa

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  20. Multibeam collection for EW0003: Multibeam data collected aboard Maurice Ewing from 2000-03-02 to 2000-03-26, Suva, Fiji to Puntarenas, Costa Rica

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  1. Multibeam collection for KM1023: Multibeam data collected aboard Kilo Moana from 2010-11-24 to 2010-12-08, Suva, Fiji to Nuku'alofa, Tonga

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  2. Multibeam collection for EW9512: Multibeam data collected aboard Maurice Ewing from 1995-11-23 to 1995-12-23, Suva, Fiji to Auckland, New Zealand

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  3. Multibeam collection for RR1310: Multibeam data collected aboard Roger Revelle from 2013-07-22 to 2013-08-25, Apra, Guam to Suva, Fiji

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  4. Multibeam collection for RR1211: Multibeam data collected aboard Roger Revelle from 2012-09-09 to 2012-09-26, Suva, Fiji to Apia, Samoa

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  5. Multibeam collection for KM0411: Multibeam data collected aboard Kilo Moana from 2004-05-12 to 2004-05-19, Suva, Fiji to Wellington, New Zealand

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  6. Multibeam collection for EW9511: Multibeam data collected aboard Maurice Ewing from 1995-10-16 to 1995-11-18, Honiara, Solomon Island to Suva, Fiji

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  7. Multibeam collection for MGLN08MV: Multibeam data collected aboard Melville from 2006-10-04 to 2006-10-10, Suva, Fiji to Apia, Samoa

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  8. Multibeam collection for BMRG07MV: Multibeam data collected aboard Melville from 1996-04-19 to 1996-05-03, Port Hedland, Australia to Suva, Fiji

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  9. Multibeam collection for KM0703: Multibeam data collected aboard Kilo Moana from 2007-03-14 to 2007-04-18, Townsville, Australia to Suva, Fiji

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  10. Multibeam collection for KM1022: Multibeam data collected aboard Kilo Moana from 2010-11-11 to 2010-11-21, Apra, Guam to Suva, Fiji

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  11. Multibeam collection for MV1104: Multibeam data collected aboard Melville from 2011-03-23 to 2011-04-23, Valparaiso, Chile to Arica, Chile

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  12. Multibeam collection for AT15-61: Multibeam data collected aboard Atlantis from 2010-01-29 to 2010-03-03, Iquique, Chile to Arica, Chile

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  13. Multibeam collection for BAS_GAP: Multibeam data collected aboard James Clark Ross from 2003-06-13 to 2003-06-14, Unknown Port to Unknown Port

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  14. Multibeam collection for EW9805: Multibeam data collected aboard Maurice Ewing from 1998-05-16 to 1998-07-01, Halifax, Canada to Halifax, Canada

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  15. Multibeam collection for EW0310: Multibeam data collected aboard Maurice Ewing from 2003-11-14 to 2003-11-20, St. George's, Bermuda to Newark, NJ

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  16. Multibeam collection for EW0107: Multibeam data collected aboard Maurice Ewing from 2001-07-02 to 2001-07-10, St. George's, Bermuda to Ponta Delgada, Azores

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  17. Multibeam collection for EW0309: Multibeam data collected aboard Maurice Ewing from 2003-10-22 to 2003-11-09, Bridgetown, Barbados to St. George's, Bermuda

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  18. Multibeam collection for EW0108: Multibeam data collected aboard Maurice Ewing from 2001-07-23 to 2001-08-01, Patras, Greece to Piraeus, Greece

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  19. Multibeam collection for EW0208: Multibeam data collected aboard Maurice Ewing from 2002-08-12 to 2002-09-06, Newport, OR to Newport, OR

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  20. Multibeam collection for EW9704: Multibeam data collected aboard Maurice Ewing from 1997-06-01 to 1997-07-04, Jacksonville, FL to Lisbon, Portugal

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  1. Multibeam collection for EW9902: Multibeam data collected aboard Maurice Ewing from 1999-02-28 to 1999-03-05, Bridgetown, Barbados to Cristobal, Panama

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  2. Multibeam collection for EW0202: Multibeam data collected aboard Maurice Ewing from 2002-02-24 to 2002-03-26, Guam to Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  3. Multibeam collection for EW9205: Multibeam data collected aboard Maurice Ewing from 1992-05-12 to 1992-05-26, Budhypramono, Marquesas Islands to Balboa, Panama

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  4. Multibeam collection for EW0401: Multibeam data collected aboard Maurice Ewing from 2004-02-20 to 2004-02-26, Norfolk, VA to Progresso, Mexico

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  5. Multibeam collection for KNOX19RR: Multibeam data collected aboard Roger Revelle from 2008-08-21 to 2008-10-03, Miami, FL to Port Everglades, FL

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  6. Multibeam collection for HLY09TD: Multibeam data collected aboard Healy from 2009-07-06 to 2009-07-25, Seattle, WA to Barrow, AK

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  7. Multibeam collection for EX0907: Multibeam data collected aboard Okeanos Explorer from 2009-07-14 to 2009-07-23, Astoria, OR to San Francisco, CA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  8. Multibeam collection for RB0703: Multibeam data collected aboard Ronald H. Brown from 2007-05-02 to 2007-05-29, Bridgetown, Barbados to Fort Lauderdale, FL

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  9. Multibeam collection for SOJN02MV: Multibeam data collected aboard Melville from 1996-10-28 to 1996-11-21, Papeete, French Polynesia to Valparaiso, Chile

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  10. Multibeam collection for KM0505: Multibeam data collected aboard Kilo Moana from 2005-03-24 to 2005-04-01, Brisbane, Australia to Pago Pago, American Samoa

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  11. Multibeam collection for B00025: Multibeam data collected aboard Surveyor from 1985-08-29 to 1985-09-18, Seattle, WA to Seattle, WA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  12. Multibeam collection for AT11L32: Multibeam data collected aboard Atlantis from 2005-09-08 to 2005-09-19, Seattle, WA to Seattle, WA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  13. Multibeam collection for HLY0805: Multibeam data collected aboard Healy from 2008-08-14 to 2008-09-05, Barrow, AK to Barrow, AK

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  14. Multibeam collection for B00144: Multibeam data collected aboard Surveyor from 1988-06-08 to 1988-06-15, Seattle, WA to Seattle, WA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  15. Multibeam collection for RB09TR: Multibeam data collected aboard Ronald H. Brown from 2009-01-15 to 2009-04-15, Rodman, Panama to Charleston, SC

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  16. Multibeam collection for TN274: Multibeam data collected aboard Thomas G. Thompson from 2012-01-25 to 2012-02-09, Apra, Guam to Apra, Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  17. Multibeam collection for Puna-rid: Multibeam data collected aboard Ocean Alert from 1998-03-01 to 1998-03-03, Unknown Port to Unknown Port

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  18. Multibeam collection for HLY1302: Multibeam data collected aboard Healy from 2013-08-16 to 2013-09-07, Barrow, AK to Barrow, AK

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  19. Multibeam collection for KM1116: Multibeam data collected aboard Kilo Moana from 2011-05-20 to 2011-06-07, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  20. Multibeam collection for B00118: Multibeam data collected aboard Davidson from 1987-11-09 to 1987-11-23, Seattle, WA to Seattle, WA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  1. Multibeam collection for USF1999: Multibeam data collected aboard Bellows from 1999-02-17 to 1999-10-08, Unknown Port to Unknown Port

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  2. Multibeam collection for NEMO03MV: Multibeam data collected aboard Melville from 2000-05-15 to 2000-06-08, Manzanillo, Mexico to Puerto Caldera, Costa Rica

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  3. Multibeam collection for AT18-12: Multibeam data collected aboard Atlantis from 2011-10-04 to 2011-10-28, San Diego, CA to Balboa, Panama

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  4. Multibeam collection for AT15-16: Multibeam data collected aboard Atlantis from 2007-02-13 to 2007-03-19, Manzanillo, Mexico to Manzanillo, Mexico

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  5. Multibeam collection for RR1412: Multibeam data collected aboard Roger Revelle from 2014-10-29 to 2014-11-23, Apra, Guam to Apra, Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  6. Multibeam collection for RNDB02WT: Multibeam data collected aboard Thomas Washington from 1988-05-18 to 1988-06-10, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  7. Multibeam collection for KM0205: Multibeam data collected aboard Kilo Moana from 2002-09-22 to 2002-10-18, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  8. Multibeam collection for HLY07TC: Multibeam data collected aboard Healy from 2007-04-04 to 2007-04-09, Seattle, WA to Dutch Harbor, AK

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  9. Multibeam collection for HLY13TC: Multibeam data collected aboard Healy from 2013-09-08 to 2013-09-21, Barrow, AK to Seward, AK

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  10. Multibeam collection for EW9602: Multibeam data collected aboard Maurice Ewing from 1996-03-23 to 1996-03-25, Lyttelton, New Zealand to Papeete, French Polynesia

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  11. Multibeam collection for TN159: Multibeam data collected aboard Thomas G. Thompson from 2003-07-28 to 2003-08-20, Victoria, Canada to Astoria, OR

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  12. Multibeam collection for FK141015: Multibeam data collected aboard Falkor from 2014-10-15 to 2014-11-03, Pohnpei, Micronesia to Apra, Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  13. Multibeam collection for KM0918: Multibeam data collected aboard Kilo Moana from 2009-07-23 to 2009-07-27, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  14. Multibeam collection for AMAT05RR: Multibeam data collected aboard Roger Revelle from 2006-05-26 to 2006-05-31, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  15. Multibeam collection for KM0606: Multibeam data collected aboard Kilo Moana from 2006-02-18 to 2006-02-20, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  16. Multibeam collection for KM0616: Multibeam data collected aboard Kilo Moana from 2006-06-07 to 2006-06-09, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  17. Multibeam collection for KRY11_01: Multibeam data collected aboard Keary from 2011-04-13 to 2011-06-14, Waterford, Ireland to Waterford, Ireland

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  18. Multibeam collection for KRUS05RR: Multibeam data collected aboard Roger Revelle from 2004-09-14 to 2004-10-09, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  19. Multibeam collection for COOK22MV: Multibeam data collected aboard Melville from 2002-04-14 to 2002-04-27, Majuro, Marshall Islands to Osaka, Japan

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  20. Multibeam collection for MV1102: Multibeam data collected aboard Melville from 2011-02-20 to 2011-03-14, Cape Town, South Africa to Valparaiso, Chile

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  1. Multibeam collection for RR1201: Multibeam data collected aboard Roger Revelle from 2012-01-10 to 2012-02-12, Phuket, Thailand to Durban, South Africa

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  2. Multibeam collection for RR0903: Multibeam data collected aboard Roger Revelle from 2009-03-20 to 2009-05-13, Cape Town, South Africa to Fremantle, Australia

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  3. Multibeam collection for KM0311: Multibeam data collected aboard Kilo Moana from 2003-06-22 to 2003-08-04, Seattle, WA to Dutch Harbor, AK

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  4. Multibeam collection for FK006B: Multibeam data collected aboard Falkor from 2012-11-06 to 2012-11-28, Pascagoula, MS to Pascagoula, MS

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  5. Multibeam collection for KN195-12: Multibeam data collected aboard Knorr from 2009-07-27 to 2009-08-09, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  6. Multibeam collection for KM0631: Multibeam data collected aboard Kilo Moana from 2006-11-16 to 2006-11-20, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  7. Multibeam collection for BMRG01MV: Multibeam data collected aboard Melville from 1995-10-15 to 1995-11-23, San Diego, CA to Papeete, French Polynesia

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  8. Multibeam collection for BMRG04MV: Multibeam data collected aboard Melville from 1996-01-07 to 1996-01-11, Dunedin, New Zealand to Hobart, Tasmania

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  9. Multibeam collection for BMRG05MV: Multibeam data collected aboard Melville from 1996-01-16 to 1996-02-16, Hobart, Tasmania to Fremantle, Australia

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  10. Multibeam collection for FK005C: Multibeam data collected aboard Falkor from 2012-10-08 to 2012-10-20, Corpus Christi, TX to Corpus Christi, TX

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  11. Multibeam collection for TN080: Multibeam data collected aboard Thomas G. Thompson from 1998-07-14 to 1998-07-15, Seattle, WA to Seattle, WA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  12. Multibeam collection for KM0415: Multibeam data collected aboard Kilo Moana from 2004-07-16 to 2004-08-24, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  13. Multibeam collection for HLY03DF: Multibeam data collected aboard Healy from 2003-01-31 to 2003-04-01, Seattle, WA to Seattle, WA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  14. Multibeam collection for FK150324: Multibeam data collected aboard Falkor from 2015-03-24 to 2015-04-06, Broome, Australia to Broome, Australia

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  15. Multibeam collection for MGLN11MV: Multibeam data collected aboard Melville from 2006-11-15 to 2006-12-17, Honolulu, HI to Brisbane, Australia

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  16. Multibeam collection for EW9911: Multibeam data collected aboard Maurice Ewing from 1999-10-10 to 1999-10-10, Lae, Papua New Guinea to Townsville, Australia

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  17. Multibeam collection for KM0701: Multibeam data collected aboard Kilo Moana from 2007-01-03 to 2007-02-12, Honolulu, HI to Brisbane, Australia

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  18. Multibeam collection for WEST10MV: Multibeam data collected aboard Melville from 1995-01-29 to 1995-03-12, Fremantle, Australia to Hobart, Tasmania

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  19. Multibeam collection for KRY14_04: Multibeam data collected aboard Keary from 2014-08-22 to 2014-09-07, Blacksod, Ireland to Blacksod, Ireland

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  20. Multibeam collection for AT05L07: Multibeam data collected aboard Atlantis from 2001-09-03 to 2001-09-05, Bermuda to Woods Hole, MA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  1. Multibeam collection for TN244: Multibeam data collected aboard Thomas G. Thompson from 2009-12-05 to 2009-12-08, Seattle, WA to Seattle, WA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  2. Multibeam collection for lostcity2005: Multibeam data collected aboard Ronald H. Brown from 2005-07-17 to 2005-08-04, Unknown Port to Unknown Port

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  3. Multibeam collection for MV1209: Multibeam data collected aboard Melville from 2012-06-30 to 2012-07-10, San Diego, CA to San Diego, CA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  4. Multibeam collection for KM0518: Multibeam data collected aboard Kilo Moana from 2005-10-15 to 2005-11-05, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  5. Multibeam collection for EW9003: Multibeam data collected aboard Maurice Ewing from 1990-06-25 to 1990-07-02, Port Everglades, FL to Newark, NJ

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  6. Multibeam collection for KM1520: Multibeam data collected aboard Kilo Moana from 2015-11-20 to 2015-12-20, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  7. Multibeam collection for M26-2: Multibeam data collected aboard Meteor from 1993-10-03 to 1993-10-21, Unknown Port to Unknown Port

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  8. Multibeam collection for AT07L32: Multibeam data collected aboard Atlantis from 2003-03-25 to 2003-04-14, Freeport, Bahamas to Bridgetown, Barbados

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  9. Multibeam collection for TN331: Multibeam data collected aboard Thomas G. Thompson from 2015-10-01 to 2015-10-15, Newport, OR to Newport, OR

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  10. Multibeam collection for KN165: Multibeam data collected aboard Knorr from 2001-11-06 to 2001-11-10, Woods Hole, MA to Woods Hole, MA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  11. Multibeam collection for KM0620: Multibeam data collected aboard Kilo Moana from 2006-07-05 to 2006-07-10, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  12. Multibeam collection for B00255: Multibeam data collected aboard Mt. Mitchell from 1990-11-17 to 1990-11-20, Norfolk, VA to Norfolk, VA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  13. Multibeam collection for KM0716: Multibeam data collected aboard Kilo Moana from 2007-08-23 to 2007-08-30, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  14. Multibeam collection for VANC04MV: Multibeam data collected aboard Melville from 2002-11-02 to 2002-12-05, Arica, Chile to Valparaiso, Chile

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  15. Multibeam collection for MV1218: Multibeam data collected aboard Melville from 2012-12-18 to 2013-01-14, San Diego, CA to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  16. Multibeam collection for COOK23MV: Multibeam data collected aboard Melville from 2002-05-01 to 2002-06-06, Osaka, Japan to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  17. Multibeam collection for EW9908: Multibeam data collected aboard Maurice Ewing from 1999-07-21 to 1999-08-18, Kochi, Japan to Yokohama, Japan

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  18. Multibeam collection for RR1507: Multibeam data collected aboard Roger Revelle from 2015-04-21 to 2015-05-14, Auckland, New Zealand to Auckland, New Zealand

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  19. Multibeam collection for KM0625: Multibeam data collected aboard Kilo Moana from 2006-08-15 to 2006-10-04, Honolulu, HI to Rabaul, Papua New Guinea

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  20. Multibeam collection for KM0626: Multibeam data collected aboard Kilo Moana from 2006-10-05 to 2006-10-15, Rabaul, Papua New Guinea to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  1. Multibeam collection for titanic2004: Multibeam data collected aboard Ronald H. Brown from 2004-05-27 to 2004-06-12, Boston, MA to Woods Hole, MA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  2. Multibeam collection for EX1603: Multibeam data collected aboard Okeanos Explorer from 2016-02-23 to 2016-03-18, Pearl Harbor, HI to Kwajalein, Marshall Islands

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  3. Multibeam collection for KM0410: Multibeam data collected aboard Kilo Moana from 2004-04-06 to 2004-05-09, Suva, Fiji to Suva, Fiji

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  4. Multibeam collection for KM0305: Multibeam data collected aboard Kilo Moana from 2003-03-22 to 2003-03-24, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  5. Multibeam collection for EW9417: Multibeam data collected aboard Maurice Ewing from 1994-11-27 to 1994-12-08, Balboa, Panama to Tampa, FL

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  6. Multibeam collection for NT05-10: Multibeam data collected aboard Natsushima from 2005-07-06 to 2005-07-13, Unknown Port to Unknown Port

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  7. Multibeam collection for NT05-12: Multibeam data collected aboard Natsushima from 2005-07-27 to 2005-07-31, Unknown Port to Unknown Port

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  8. Multibeam collection for KN201: Multibeam data collected aboard Knorr from 2011-06-24 to 2011-07-17, Woods Hole, MA to Woods Hole, MA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  9. Multibeam collection for RC2912: Multibeam data collected aboard Robert D. Conrad from 1988-12-02 to 1989-01-07, Cadiz, Spain to Azores

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  10. Multibeam collection for NT06-12: Multibeam data collected aboard Natsushima from 2006-06-24 to 2006-06-26, Unknown Port to Unknown Port

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  11. Multibeam collection for MGL1110: Multibeam data collected aboard Marcus G. Langseth from 2011-06-29 to 2011-08-05, Kodiak, AK to Dutch Harbor, AK

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  12. Multibeam collection for AT18-14: Multibeam data collected aboard Atlantis from 2011-11-25 to 2011-12-08, Piraievs, Greece to Piraievs, Greece

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  13. Multibeam collection for AT18-08: Multibeam data collected aboard Atlantis from 2011-07-19 to 2011-08-01, Astoria, OR to Astoria, OR

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  14. Multibeam collection for EW9901: Multibeam data collected aboard Maurice Ewing from 1999-01-30 to 1999-02-24, Norfolk, VA to Bridgetown, Barbados

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  15. Multibeam collection for KN162L11: Multibeam data collected aboard Knorr from 2001-02-11 to 2001-03-15, Mombassa, Kenya to Seychelles

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  16. Multibeam collection for TN183: Multibeam data collected aboard Thomas G. Thompson from 2005-09-02 to 2005-10-04, Seattle, WA to Seattle, WA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  17. Multibeam collection for KN192-05: Multibeam data collected aboard Knorr from 2007-11-16 to 2007-12-13, Natal, Brazil to Walvis Bay, Namibia

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  18. Multibeam collection for RC3003: Multibeam data collected aboard Robert D. Conrad from 1989-02-10 to 1989-03-12, Fortaleza, Brazil to Recife, Brazil

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  19. Multibeam collection for PLUM04WT: Multibeam data collected aboard Thomas Washington from 1990-03-08 to 1990-04-11, Recife, Brazil to Montevideo, Uruguay

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  20. Multibeam collection for PLUM05WT: Multibeam data collected aboard Thomas Washington from 1990-04-18 to 1990-05-22, Montevideo, Uruguay to Recife, Brazil

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  1. Multibeam collection for KN161L06: Multibeam data collected aboard Knorr from 2000-04-07 to 2000-05-16, Recife, Brazil to Recife, Brazil

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  2. Multibeam collection for RC2515: Multibeam data collected aboard Robert D. Conrad from 1984-12-26 to 1985-01-09, Belem, Brazil to Ascension Island

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  3. Multibeam collection for KN161L07: Multibeam data collected aboard Knorr from 2000-05-21 to 2000-06-04, Recife, Brazil to Norfolk, VA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  4. Multibeam collection for KN192-04: Multibeam data collected aboard Knorr from 2007-10-29 to 2007-11-11, St. John's, Canada to Natal, Brazil

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  5. Multibeam collection for EW9011: Multibeam data collected aboard Maurice Ewing from 1990-12-16 to 1991-01-25, Recife, Brazil to Punta Arenas, Chile

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  6. Multibeam collection for TN153: Multibeam data collected aboard Thomas G. Thompson from 2003-02-09 to 2003-03-05, Guam to Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  7. Multibeam collection for MGL1212: Multibeam data collected aboard Marcus G. Langseth from 2012-07-12 to 2012-07-23, Astoria, OR to Astoria, OR

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  8. Multibeam collection for RR1212: Multibeam data collected aboard Roger Revelle from 2012-09-27 to 2012-10-01, Apia, Samoa to Papeete, French Polynesia

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  9. Multibeam collection for RR1505: Multibeam data collected aboard Roger Revelle from 2015-03-20 to 2015-03-22, Auckland, New Zealand to Auckland, New Zealand

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  10. Multibeam collection for MGL1214: Multibeam data collected aboard Marcus G. Langseth from 2012-07-31 to 2012-08-01, Astoria, OR to Astoria, OR

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  11. Multibeam collection for SKQ201603T: Multibeam data collected aboard Sikuliaq from 2016-03-22 to 2016-03-29, San Diego, CA to San Diego, CA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  12. Multibeam collection for AT28: Multibeam data collected aboard Atlantis from 2015-06-04 to 2015-06-04, Woods Hole, MA to Woods Hole, MA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  13. Multibeam collection for TN242: Multibeam data collected aboard Thomas G. Thompson from 2009-11-02 to 2009-11-06, Seattle, WA to Seattle, WA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  14. Multibeam collection for RR1511: Multibeam data collected aboard Roger Revelle from 2015-08-04 to 2015-08-15, Colombo, Sri Lanka to Colombo, Sri Lanka

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  15. Multibeam collection for RR1407: Multibeam data collected aboard Roger Revelle from 2014-07-02 to 2014-07-13, Trincomalee, Sri Lanka to Trincomalee, Sri Lanka

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  16. Multibeam collection for RR1014: Multibeam data collected aboard Roger Revelle from 2010-10-12 to 2010-10-24, Naha, Japan to Kao-hsiung, Taiwan

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  17. Multibeam collection for RR0904: Multibeam data collected aboard Roger Revelle from 2009-05-20 to 2009-06-23, Fremantle, Australia to Port Darwin, Australia

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  18. Multibeam collection for KM1301: Multibeam data collected aboard Kilo Moana from 2013-01-10 to 2013-02-07, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  19. Multibeam collection for MV1405: Multibeam data collected aboard Melville from 2014-07-03 to 2014-07-26, San Diego, CA to San Diego, CA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  20. Multibeam collection for KN192-07: Multibeam data collected aboard Knorr from 2007-12-31 to 2008-01-17, Natal, Brazil to Ascension Island

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  1. Multibeam collection for KM1226: Multibeam data collected aboard Kilo Moana from 2012-11-19 to 2012-11-30, Pohnpei, Micronesia to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  2. Multibeam collection for KM0310: Multibeam data collected aboard Kilo Moana from 2003-05-22 to 2003-06-10, Kodiak, AK to Seattle, WA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  3. Multibeam collection for RB0607: Multibeam data collected aboard Ronald H. Brown from 2006-10-02 to 2006-10-27, Charleston, SC to Valparaiso, Chile

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  4. Multibeam collection for RB0709: Multibeam data collected aboard Ronald H. Brown from 2007-10-09 to 2007-11-07, Charleston, SC to Galapagos Islands, Ecuador

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  5. Multibeam collection for RB0505: Multibeam data collected aboard Ronald H. Brown from 2005-09-26 to 2005-10-21, Miami, FL to Arica, Chile

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  6. Multibeam collection for EW9210: Multibeam data collected aboard Maurice Ewing from 1992-09-29 to 1992-11-09, Bridgetown, Barbados to St. George's, Bermuda

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  7. Multibeam collection for SKQ201502T: Multibeam data collected aboard Sikuliaq from 2015-02-12 to 2015-02-18, Ketchikan, AK to Juneau, AK

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  8. Multibeam collection for RC2614: Multibeam data collected aboard Robert D. Conrad from 1985-12-01 to 1985-12-30, Hong Kong, China to Singapore

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  9. Multibeam collection for SO92: Multibeam data collected aboard Sonne from 1993-12-15 to 1993-12-27, Singapore, Singapore to Colombo, Sri Lanka

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  10. Multibeam collection for KN145L12: Multibeam data collected aboard Knorr from 1995-11-01 to 1995-11-09, Singapore, Singapore to Dampier, Australia

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  11. Multibeam collection for MV0909: Multibeam data collected aboard Melville from 2009-05-20 to 2009-05-25, Brisbane, Australia to Papeete, French Polynesia

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  12. Multibeam collection for KM1128: Multibeam data collected aboard Kilo Moana from 2011-10-01 to 2011-10-25, Honolulu, HI to Apia, Samoa

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  13. Multibeam collection for RC2506: Multibeam data collected aboard Robert D. Conrad from 1984-04-18 to 1984-05-19, Nice, France to Piraievs, Greece

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  14. Multibeam collection for ZHNG09RR: Multibeam data collected aboard Roger Revelle from 2005-07-21 to 2005-08-27, Yokohama, Japan to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  15. Multibeam collection for KM0414: Multibeam data collected aboard Kilo Moana from 2004-06-20 to 2004-07-10, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  16. Multibeam collection for KM0401: Multibeam data collected aboard Kilo Moana from 2004-01-07 to 2004-01-14, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  17. Multibeam collection for COOK02MV: Multibeam data collected aboard Melville from 2000-10-02 to 2000-10-14, Arica, Chile to Arica, Chile

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  18. Multibeam collection for COOK14MV: Multibeam data collected aboard Melville from 2001-10-06 to 2001-10-29, Suva, Fiji to Apia, Western Samoa

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  19. Multibeam collection for COOK19MV: Multibeam data collected aboard Melville from 2002-01-25 to 2002-02-26, Lyttelton, New Zealand to Lyttelton, New Zealand

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  20. Multibeam collection for COOK24MV: Multibeam data collected aboard Melville from 2002-06-09 to 2002-06-16, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  1. Multibeam collection for COOK01MV: Multibeam data collected aboard Melville from 2000-08-21 to 2000-09-27, San Diego, CA to Arica, Chile

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  2. Multibeam collection for COOK07MV: Multibeam data collected aboard Melville from 2001-03-04 to 2001-04-12, Apra, Guam to Apra, Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  3. Multibeam collection for COOK11MV: Multibeam data collected aboard Melville from 2001-08-08 to 2001-08-17, Naha, Japan to Lae, Papua New Guinea

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  4. Multibeam collection for COOK09MV: Multibeam data collected aboard Melville from 2001-06-21 to 2001-07-05, Pusan, South Korea to Pusan, South Korea

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  5. Multibeam collection for PLUM08WT: Multibeam data collected aboard Thomas Washington from 1990-06-29 to 1990-07-09, La Guaira, Venezuela to Manzanillo, Mexico

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  6. Multibeam collection for CRUISE10: Multibeam data collected aboard Nikolaj Strakhov from 1990-03-29 to 1990-06-01, Unknown Port to Unknown Port

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  7. Multibeam collection for CRUISE5: Multibeam data collected aboard Nikolaj Strakhov from 1987-05-18 to 1987-07-05, Unknown Port to Unknown Port

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  8. Multibeam collection for RC2909: Multibeam data collected aboard Robert D. Conrad from 1988-09-15 to 1988-10-14, Cape Verde to Ponta Delgada, Azores

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  9. Multibeam collection for KN159L7: Multibeam data collected aboard Knorr from 1998-11-17 to 1998-12-16, Recife, Brazil to Cape Verde

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  10. Multibeam collection for AT26-12: Multibeam data collected aboard Atlantis from 2014-03-14 to 2014-03-26, New Orleans, LA to Gulfport, MS

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  11. Multibeam collection for AT26-19: Multibeam data collected aboard Atlantis from 2014-08-28 to 2014-09-11, Astoria, OR to Astoria, OR

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  12. Multibeam collection for KM0417: Multibeam data collected aboard Kilo Moana from 2004-09-09 to 2004-10-17, Suva, Fiji to Suva, Fiji

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  13. Multibeam collection for KM0718: Multibeam data collected aboard Kilo Moana from 2007-09-10 to 2007-10-08, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  14. Multibeam collection for MV0904: Multibeam data collected aboard Melville from 2009-03-23 to 2009-03-28, Manila, Philippines to Kao-hsiung, Taiwan

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  15. Multibeam collection for EW9302: Multibeam data collected aboard Maurice Ewing from 1993-05-27 to 1993-06-21, Woods Hole, MA to Reykjavik, Iceland

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  16. Multibeam collection for EW9403: Multibeam data collected aboard Maurice Ewing from 1994-03-21 to 1994-03-27, Cayenne, French Guiana to Martinique

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  17. Multibeam collection for AT07L01: Multibeam data collected aboard Atlantis from 2001-09-22 to 2001-09-30, New York, NY to Charleston, SC

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  18. Multibeam collection for KN162L13: Multibeam data collected aboard Knorr from 2001-03-30 to 2001-05-01, Mauritius to Mauritius

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  19. Multibeam collection for MV1011: Multibeam data collected aboard Melville from 2010-09-08 to 2010-09-19, San Diego, CA to San Diego, CA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  20. Multibeam collection for HLY06TI: Multibeam data collected aboard Healy from 2006-08-30 to 2006-09-03, Kodiak, AK to Seattle, WA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  1. Multibeam collection for HLY07TI: Multibeam data collected aboard Healy from 2007-09-26 to 2007-09-30, Juneau, AK to Seattle, WA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  2. Multibeam collection for B00086: Multibeam data collected aboard Surveyor from 1986-10-25 to 1986-11-03, Seattle, WA to Seattle, WA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  3. Multibeam collection for AT25: Multibeam data collected aboard Atlantis from 2013-05-03 to 2013-05-06, Woods Hole, MA to Woods Hole, MA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  4. Multibeam collection for DANA07RR: Multibeam data collected aboard Roger Revelle from 2004-03-11 to 2004-04-01, Mazatlan, Mexico to San Diego, CA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  5. Multibeam collection for HLY12TC: Multibeam data collected aboard Healy from 2012-09-24 to 2012-09-27, Barrow, AK to Dutch Harbor, AK

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  6. Multibeam collection for KM1405: Multibeam data collected aboard Kilo Moana from 2014-01-30 to 2014-01-31, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  7. Multibeam collection for MV1304: Multibeam data collected aboard Melville from 2013-02-25 to 2013-03-17, Yokohama, Japan to Yokohama, Japan

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  8. Multibeam collection for EX0901: Multibeam data collected aboard Okeanos Explorer from 2009-03-29 to 2009-04-03, Seattle, WA to Seattle, WA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  9. Multibeam collection for EX0801: Multibeam data collected aboard Okeanos Explorer from 2008-09-08 to 2008-09-22, Seattle, WA to Seattle, WA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  10. Multibeam collection for MV1005: Multibeam data collected aboard Melville from 2010-03-25 to 2010-04-03, Valparaiso, Chile to Puntarenas, Costa Rica

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  11. Multibeam collection for B00076: Multibeam data collected aboard Discoverer from 1986-10-03 to 1986-10-13, Seattle, WA to Seattle, WA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  12. Multibeam collection for KM1109: Multibeam data collected aboard Kilo Moana from 2011-03-04 to 2011-03-10, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  13. Multibeam collection for Tecfluc: Multibeam data collected aboard Ocean Alert from 1998-05-23 to 1998-05-26, Unknown Port to Unknown Port

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  14. Multibeam collection for KM1316: Multibeam data collected aboard Kilo Moana from 2013-09-16 to 2013-09-28, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  15. Multibeam collection for KNOX05RR: Multibeam data collected aboard Roger Revelle from 2007-05-07 to 2007-06-14, Phuket, Thailand to Phuket, Thailand

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  16. Multibeam collection for MGL1208: Multibeam data collected aboard Marcus G. Langseth from 2012-04-30 to 2012-05-26, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  17. Multibeam collection for AT1L2: Multibeam data collected aboard Atlantis from 1997-03-29 to 1997-04-06, Fort Lauderdale, FL to Norfolk, VA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  18. Multibeam collection for KM0317: Multibeam data collected aboard Kilo Moana from 2003-10-19 to 2003-10-23, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  19. Multibeam collection for GEOMETEP: Multibeam data collected aboard Sonne from 1985-12-17 to 1986-01-07, Unknown Port to Unknown Port

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  20. Multibeam collection for RR1116: Multibeam data collected aboard Roger Revelle from 2011-11-06 to 2011-12-11, Phuket, Thailand to Phuket, Thailand

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  1. Multibeam collection for KRUS03RR: Multibeam data collected aboard Roger Revelle from 2004-07-10 to 2004-08-16, Dutch Harbor, AK to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  2. Multibeam collection for KM0818: Multibeam data collected aboard Kilo Moana from 2008-09-08 to 2008-09-22, Port Hueneme, CA to Port Hueneme, CA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  3. Multibeam collection for SUM0108A: Multibeam data collected aboard Sumner from 2001-06-17 to 2001-06-22, Unknown Port to Unknown Port

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  4. Multibeam collection for MGL1218: Multibeam data collected aboard Marcus G. Langseth from 2012-11-25 to 2012-11-25, Astoria, OR to Newport, OR

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  5. Multibeam collection for B00289: Multibeam data collected aboard Surveyor from 1991-07-24 to 1991-08-03, Seattle, WA to Seattle, WA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  6. Multibeam collection for EW9701B: Multibeam data collected aboard Maurice Ewing from 1997-04-18 to 1997-04-29, Newark, NJ to Savannah, GA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  7. Multibeam collection for EW9703: Multibeam data collected aboard Maurice Ewing from 1997-05-19 to 1997-05-25, Port Canaveral, FL to Jacksonville, FL

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  8. Multibeam collection for EW9702: Multibeam data collected aboard Maurice Ewing from 1997-05-03 to 1997-05-16, Savannah, GA to Port Canaveral, FL

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  9. Multibeam collection for KN195-13: Multibeam data collected aboard Knorr from 2009-08-14 to 2009-09-10, Honolulu, HI to Woods Hole, MA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  10. Multibeam collection for KN195L03: Multibeam data collected aboard Knorr from 2009-01-12 to 2009-02-23, Puntarenas, Costa Rica to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  11. Multibeam collection for EX1505: Multibeam data collected aboard Okeanos Explorer from 2015-10-05 to 2015-10-16, Honolulu, HI to Alameda, CA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  12. Multibeam collection for TUNE04WT: Multibeam data collected aboard Thomas Washington from 1991-10-05 to 1991-10-16, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  13. Multibeam collection for KM1106: Multibeam data collected aboard Kilo Moana from 2011-02-19 to 2011-02-23, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  14. Multibeam collection for KM1113: Multibeam data collected aboard Kilo Moana from 2011-04-10 to 2011-04-14, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  15. Multibeam collection for KM0824: Multibeam data collected aboard Kilo Moana from 2008-12-15 to 2008-12-16, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  16. Multibeam collection for EW9507: Multibeam data collected aboard Maurice Ewing from 1995-07-28 to 1995-08-02, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  17. Multibeam collection for TN264: Multibeam data collected aboard Thomas G. Thompson from 2011-05-21 to 2011-05-24, Honolulu, HI to Seattle, WA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  18. Multibeam collection for EX1504L1: Multibeam data collected aboard Okeanos Explorer from 2015-07-10 to 2015-07-24, Honolulu, HI to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  19. Multibeam collection for MGL1002: Multibeam data collected aboard Marcus G. Langseth from 2010-05-07 to 2010-05-19, Astoria, OR to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...

  20. Multibeam collection for KIWI02RR: Multibeam data collected aboard Roger Revelle from 1997-08-11 to 1997-08-21, San Francisco, CA to Honolulu, HI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found at...