We have developed a new three-dimensional seismic velocity model of the central United States (CUSVM) that includes the New Madrid Seismic Zone (NMSZ) and covers parts of Arkansas, Mississippi, Alabama, Illinois, Missouri, Kentucky, and Tennessee. The model represents a compilation of decades of crustal research consisting of seismic, aeromagnetic, and gravity profiles; geologic mapping; geophysical and geological borehole logs; and inversions of the regional seismic properties. The density and P- and S-wave velocities are synthesized in a stand-alone spatial database that can be queried to generate the required input for numerical seismic-wave propagation simulations. The velocity model has been tested and calibrated by simulating ground motions of the 18 April 2008 Mw 5.4 Mt. Carmel, Illinois, earthquake and comparing the results with observed records within the model area (see associated publication).

This dataset contains daily-averaged ocean velocity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Weathered oil in the surf-zone after an oil spill may mix with suspended sediments to form sand and oil agglomerates (SOA). Sand and oil agglomerates may form in mats on the scale of tens of meters (m), and may break apart into pieces between 1 and 10 centimeters (cm) in diameter. These more mobile pieces are susceptible to alongshore and cross-shore transport, and lead to beach re-oiling on the time scale of months to years following a spill. The U.S. Geological Survey (USGS) conducted experiments March 10 - 13, 2014, to expand the available data on sand and oil agglomerate motion; test shear stress based incipient motion parameterizations in a controlled, laboratory setting; and directly observe SOA exhumation and burial processes. Artificial sand and oil agglomerates (aSOA) were created and deployed in a small-oscillatory flow tunnel in two sets of experiments, during which, video and velocity data were obtained. The first experiment, which was set up to help researchers investigate incipient motion, used with an immobile, rough bottom (referred to as false-floor) and the second–testing seafloor interactions–utilized with a coarse grain sand bottom (movable sand bed). Detailed information regarding the creation of the aSOA can be found in Dalyander et al. (2015). More information about the USGS laboratory experiment conducted in collaboration with the Naval Research Laboratory can be found in the associated Open File Report (OFR Number Unknown).

This data set

Kimberlina 1.2 Velocity model and synthetic seismic data, produced in collaboration of teams at the National Energy Technology Laboratory, Los Alamos National Laboratory, and Lawrence Livermore National Laboratory through the National Risk Assessment Partnership. Data is associated with the following publication: Zheng Zhou, Youzuo Lin, Zhongping Zhang, Yue Wu, Zan Wang, Robert Dilmore, and George Guthrie, "A Data-Driven CO2 Leakage Detection Using Seismic Data and Spatial-Temporal Densely Connected Convolutional Neural Networks," International Journal of Greenhouse Gas Control, Vol 90, 2019. The Kimberlina 1.2 Velocity models were produced by Zan Wang, Robert Dilmore, William Harbert, and Lianjie Huang at NETL. The following citations are directly related to the creation of the velocity models: Wang, Z. Harbert, W., Dilmore, R., Huang, L. Modeling of time-lapse seismic monitoring using CO2 leakage simulations for a model CO2 storage site with realistic geology: Application in assessment of early leak-detection capabilities. International Journal of Greenhouse Gas Control. V. 76, September 2018, Pages 39-52. https://doi.org/10.1016/j.ijggc.2018.06.011 Wang, Z., Dilmore, R., Harbert, W. Inferring CO2 saturation from synthetic surface seismic and downhole monitoring data using machine learning for leakage detection at CO2 sequestration sites. International Journal of Greenhouse Gas Control, V. 100, September 2020. https://doi.org/10.1016/j.ijggc.2020.103115 The velocity models were built based on the Kimberlina 1.2 aquifer impact data which is associated with the following publications: Buscheck, T.A., Mansoor, K., Yang, X., Wainwright, H., and Carroll, S. (2019). Downhole pressure and chemical monitoring for CO2 and brine leak detection in aquifers above a CO2 storage reservoir. International Journal of Greenhouse Gas Control. 91. 102812. 10.1016/j.ijggc.2019.102812. Xianjin Yang, Thomas A. Buscheck, Kayyum Mansoor, Zan Wang, Kai Gao, Lianjie Huang, Delphine Appriou, Susan A. Carroll, Assessment of geophysical monitoring methods for detection of brine and CO2 leakage in drinking water aquifers, International Journal of Greenhouse Gas Control, Volume 90, 2019, 102803, ISSN 1750-5836, https://doi.org/10.1016/j.ijggc.2019.102803 The synthetic seismic data was produced by Youzuo Lin and team at LANL, and are associated with the following citations: Jordan, P. D., and J. L. Wagoner. Characterizing Construction of Existing Wells to a CO2 Storage Target: The Kimberlina Site, California. Zheng Zhou, Youzuo Lin, Zhongping Zhang, Yue Wu, Zan Wang, Robert Dilmore, and George Guthrie, "A Data-Driven CO2 Leakage Detection Using Seismic Data and Spatial-Temporal Densely Connected Convolutional Neural Networks," International Journal of Greenhouse Gas Control, Vol 90, 2019.

This dataset includes data from the Sound Velocity Profiler system onboard the UNOLS R/V Thompson ship during the Bering Sea Ecosystem Study-Bering Sea Integrated Ecosystem Research Program (BEST-BSIERP) 2010 TN250 (summer) cruise. BEST-BSIERP together are the Bering Sea project. The data files are collected into one tar file for the cruise.

This compilation of recent ice velocity data of the Antarctic ice sheet is intended for use by the polar scientific community. The data are presented in tabular form (ASCII), containing latitude, longitude, speed, bearing, and error ranges. A metadata header describes the source of the data, the time of measurement, and gives details on measurement accuracy and precision. The tables are available for ftp transfer.

Web pages developed specifically for this data set provide detailed information for viewing and selecting the velocity data. These pages contain large satellite image maps (available as jpeg files). The data sets used to create these images were contributed by several investigators, generally from already published work. Both in situ and image-based methods are used.

References for the data sets are included with the data tables. If you have well-characterized Antarctic ice velocity data you would like to contribute to this site, please contact teds@icehouse.colorado.edu. If you have any questions concerning the relevance of these data to your work please contact NSIDC User Services.

View data of the frequency at which one unit of currency purchases domestically produced goods and services within a given time period.

Preliminary 1D seismic velocity model derived from ambient noise correlation. 28 Green's functions filtered between 4-10 Hz for Vp, Vs, and Qs were calculated. 1D model estimated for each path. The final model is a median of the individual models. Resolution is best for the top 1 km. Poorly constrained with increasing depth.

Ice velocity data from ice stream C, including the body of the ice stream and its area of onset, are available. The investigator calculated velocities from precise ice displacement measurements made with a geodetic-quality Global Positioning System (GPS). These ice displacement measurements accompanied seismic experiments aimed at understanding controls on the flow of ice streams in west Antarctica. An understanding of ice stream flow is essential to predicting the response of the West Antarctic Ice Sheet to future climate change.

Data are available in ASCII format via ftp.

Water velocities were measured at discrete cross-sections along an approximately 1-mile reach of the Kentucky Dam tailwater on September 12 and 17-18, 2020, using a 1200 kHz acoustic Doppler current profiler (ADCP). The data were geo-referenced with an integrated global navigation satellite system (GNSS) smart antenna with submeter accuracy. The ADCP and GNSS antenna were mounted on a marine survey vessel, and data were collected as the survey vessel traversed the tailwater along planned survey lines. There was typically one reciprocal pair (two passes) of data collected per line. There was a total of 53 survey lines equally spaced 100 feet apart and oriented approximately perpendicular to the primary flow direction. Data collection software integrated and stored the velocity and position data from the ADCP and GNSS antenna in real time. Data were processed using the Velocity Mapping Toolbox (Parsons and others, 2013) to derive temporally- and spatially-averaged water velocity val ...

Accurate data and maps of sea floor geology are important first steps toward protecting fish habitat, delineating marine resources, and assessing environmental changes due to natural or human impacts. To address these concerns the U.S. Geological Survey, in cooperation with the Massachusetts Office of Coastal Zone Management (CZM), comprehensively mapped the Cape Cod Bay sea floor to characterize the surface and shallow subsurface geologic framework. Geophysical data collected include swath bathymetry, backscatter, and seismic reflection profile data. Ground-truth data, including sediment samples, underwater video, and bottom photographs were also collected. This effort is part of a long-term collaboration between the USGS and the Commonwealth of Massachusetts to map the State’s waters, support research on the Quaternary evolution of coastal Massachusetts, the influence of sea-level change and sediment supply on coastal evolution, and efforts to understand the type, distribution, and quality of subtidal marine habitats. This collaboration produces high-resolution geologic maps and Geographic Information System (GIS) data that serve the needs of research, management and the public. Data collected as part of this mapping cooperative continue to be released in a series of USGS Open-File Reports and Data Releases (https://www.usgs.gov/centers/whcmsc/science/geologic-mapping-massachusetts-seafloor). This data release provides the geophysical and geologic sampling data collected in Cape Cod Bay during USGS Field Activities 2019-002-FA and 2019-034-FA in 2019.

description: Velocity data are being provided in an Access database and Excel spreadsheets. The database summarizes the velocity data, site location and description, vegetative characteristics, and water quality parameters. The spreadsheet filters and averages the complete raw velocity data set per measurement. This data was collected at two minute intervals for 10 minutes. The file naming convention is as follows: (example) E07T31197.xls Column 1: E or W East or west from Taylor Slough Airboat Trail Column 2-3: Sequential numbers from the center of the transect Column 4-5: Sequential transect number Column 6-9: Month and Year data was collected; abstract: Velocity data are being provided in an Access database and Excel spreadsheets. The database summarizes the velocity data, site location and description, vegetative characteristics, and water quality parameters. The spreadsheet filters and averages the complete raw velocity data set per measurement. This data was collected at two minute intervals for 10 minutes. The file naming convention is as follows: (example) E07T31197.xls Column 1: E or W East or west from Taylor Slough Airboat Trail Column 2-3: Sequential numbers from the center of the transect Column 4-5: Sequential transect number Column 6-9: Month and Year data was collected

This traffic data has been summarized to the hour for the last 180 days. See also https://data.mesaaz.gov/Planes-Trains-Automobiles/Velocity-Individual-Traffic-Match/7dbt-yfru.

As part of the Great Lakes Restoration Initiative (GLRI) project template 774-18 entitled “Development of monitoring and response methodologies, and implementation of an Adaptive Management Framework to work towards Eradication of Grass Carp in Lake Erie” an integrated bathymetric/hydrodynamic/water-quality survey of the Maumee River (Ohio) was completed by the U.S. Geological Survey (USGS) in the summer of 2019. These data were collected to inform the development of a one-dimensional hydraulic model and associated Fluvial Egg Drift Simulator (FluEgg) model of the Maumee River downstream from Defiance, Ohio. The data contained in this data release were collected by the USGS Ohio-Kentucky-Indiana Water Science Center to inform the development of these models by the USGS Central Midwest Water Science Center. The survey was completed over two periods of time: June 24–28, 2019, and July 29 to August 1, 2019. The first survey period concentrated on the reach between Grand Rapids, Ohio, and Lake Erie, while the second period concentrated on the reach between Defiance, Ohio, and Grand Rapids, Ohio. Survey data include bathymetry (depth and bed elevation), three-dimensional water velocity, discharge, and basic water-quality properties. A total of 251 cross sections were surveyed (141 upstream from and 110 downstream from Grand Rapids Dam, respectively) and data were also collected along streamwise transits between sections. Due to rapids, high-water, access, and safety concerns, no data were collected in the 23.9-kilometer reach downstream from the dam at Grand Rapids, Ohio. The upstream-most cross section is 280 meters downstream from the low-head dam approximately 6.6 kilometers downstream from Defiance, Ohio. The downstream-most cross section is located 290 meters downstream from the U.S. Coast Guard Station at Toledo, Ohio (3900 N Summit St, Toledo, Ohio, 43611). All data were collected by a manned survey vessel with a two-person survey crew of trained hydrographers. All data were georeferenced using a Trimble R10 Global Navigation Satellite System (GNSS) receiver mounted on the survey vessel and connected to the Ohio Department of Transportation (ODOT) real-time virtual reference station (VRS) network. This component of the data release consists of water velocity and water-quality data measured in the Maumee River between Defiance, Ohio, and the river mouth at Lake Erie at Toledo, Ohio. Velocity data were collected using a 1200 kilohertz Teledyne RD Instruments RiverPro acoustic Doppler current profiler (ADCP) deployed on a fixed mount from the survey vessel. The GNSS receiver was mounted directly above the ADCP. The sampling frequency varied slightly with the dynamic configuration of the ADCP but was generally between 1 to 2 Hertz. Data have been post-processed using the Velocity Mapping Toolbox v4.09 (VMT; Parsons and others, 2013) and its GIS Table Creation Utility with temporal averaging of 5 seconds. Both layer- and depth-averaged velocities are included in the data files and files are included for both the depth from surface (DFS) reference and height above bottom (HAB) reference. Layers are defined in 1-meter intervals for both references across the full water column and 0.5-meter intervals for points within 2 meters of the water surface or bottom. Water-quality data include two-dimensional, near-surface point measurements of basic water-quality properties in the Maumee River between Defiance, Ohio, and the river mouth at Lake Erie at Toledo, Ohio. Water-quality properties include temperature, specific conductance, pH, dissolved oxygen, turbidity, total chlorophyll, and phycocyanin concentration (the latter two properties were only collected upstream of Grand Rapids, Ohio). These data were collected using a Xylem EXO2 sonde (SN 16J103377) equipped with a temperature/conductivity sensor (SN 17A103858), pH sensor (SN 18G103338), optical dissolved oxygen sensor (SN 17A103549), turbidity sensor (SN 16K102514), total algae phycocyanin smart sensor (SN 12M100504), and central wiper. The sonde was deployed off the side of a manned survey vessel using a fixed mount at a depth of approximately 0.3 meters below the water surface. All properties were sampled at 2-second intervals as the vessel completed the survey (for both cross sections and streamwise profiles) and a 6-second moving average was applied in post-processing. References: Parsons, D.R., Jackson, P.R., Czuba, J.A., Engel, F.L., Rhoads, B.L., Oberg, K.A., Best, J.L., Mueller, D.S., Johnson, K.K. and Riley, J.D., 2013, Velocity Mapping Toolbox (VMT): a processing and visualization suite for moving-vessel ADCP measurements. Earth Surface Processes and Landforms, v. 38, no. 11, p. 1244-1260. [Also available at https://doi.org/10.1002/esp.3367.]

The datasets consist of basic well information and of digitized sonic velocity data from commercially run well logs.

This submission contains processed datasets from a long-term deployment of 3 moorings and a transect survey of the proposed tidal energy site off the East Forelands in Cook Inlet, AK.

The long-term mooring datasets were created from 8 instruments mounted on a Terrasond High Energy Oceanographic Mooring (THEOM) bottom lander and two Mid-Water Mooring (MWM) Stablemoor buoys from 1 July 2021 to 31 August 2021 (60 days). The west-most mooring (MWM1) was deployed at 60.720225 N, 151.436196 W in ~50 m of water. The middle mooring (THEOM) was deployed at 60.720703 N, 151.429500 W in ~52 m of water. The east-most buoy (MWM2) was deployed at 60.720081 N, 151.420896 W in ~50 m of water.

Each Stablemoor carried three instruments:

1. A Nortek Vector acoustic Doppler velocimeter (ADV) mounted at the Stablemoor's nose. Data were recorded at 8 Hz on a 5 minute duty cycle every 20 minutes. Data was motion-corrected using the internal IMU and external ADCP bottom-track data and then bin-averaged into 4 minute bins and converted to the Principal (streamwise, cross-stream, vertical) coordinate system. (Note: 30 seconds were trimmed from the beginning and end of each 5 minute duty cycle to account for the filter end-effects from turning on and turning off the IMU.)

2. A down-looking Nortek Signature 1000 kHz acoustic Doppler current profiler (ADCP) mounted in the first Stablemoor instrument well. Data were recorded in 2 Hz with 5-beam burst and bottom-track enabled. Processed data has been averaged into 10 minute bins and converted into the Principal coordinate system.

3. An up-looking Nortek Signature 1000 kHz acoustic Doppler current profiler (ADCP) mounted in the second Stablemoor instrument well. Data were recorded at 4 Hz with 5 beam burst enabled. Processed data has been averaged into 10 minute bins and converted into the Principal coordinate system.

Note: the down-facing ADCP on MWM1 failed on July 10th, 2021, only recording 9 days of data. Because ADV motion-correction required bottom track, the ADV from MWM1 also only has 9 days processed. Additionally, only 25 days of data were processed from the MWM2 ADV because it appeared to have been impacted by debris on 7/25.

Two instruments were mounted on the THEOM (see MHKDR link further below for THEOM raw data):

1. A Nortek Vector acoustic Doppler velocimeter (ADV). Data were recorded at 8 Hz on a 5 minute duty cycle every 20 minutes. Data was bin-averaged into 5 minute bins, and converted to the Principal coordinate system.

2. A Nortek Signature 500 kHz acoustic Doppler current profiler (ADCP). Data were recorded in 4 Hz in the beam coordinate system from all 5 beams. Processed data has been averaged into 10 minutes bins and converted to the Principal coordinate system.

velocity-engg/stt-audio-data dataset hosted on Hugging Face and contributed by the HF Datasets community

Sonic velocity and density well logs in the Sacramento Valley in California were digitized by hand. These logs are available as scanned files (pdfs and tiffs) on the California Division of Oil, Gas, and Geothermal Resources website and the data consist of transit times and bulk density measured downhole in oil and gas wells in the region. Sonic velocity and density data were also compiled from a number of sources. A summary table also provides basic information of these wells, available on the California Division of Oil, Gas, and Geothermal Resources website.