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203 results for “Seismic data”
Millstätter See seismic and core data for the publication "High-resolution calibration of seismically-induced lacustrine deposits with historical earthquake data in the Eastern Alps (Carinthia, Austria)"
<p>This dataset comprises the core data and the 3.5 kHz seismic data of Millstätter See, a lake in the Eastern European Alps, Austria. Together with a bathymetric dataset (10.5281/zenodo.5875923) and a core/seismic dataset from Wörthersee (10.5281/zenodo.5875576), this is the basis for the publication Daxer et al. "High-resolution calibration of seismically-induced lacustrine deposits with historical earthquake data in the Eastern Alps (Carinthia, Austria)".</p> <p>28 core sections (individual short cores or sections of long cores - see <em>MillstaetterSee_core_data.xlsx</em> for information) were analysed with a multi-sensor core logger (MSCL) and photographed with a smartcube camera image scanner and an ITRAX core scanner. The generated data are available in the folders <em>MSCL.zip</em> and <em>Photos.zip</em>. Some core sections were also analysed with a Malvern Mastersizer 3000 and/or CT scanning. The generated data are provided in the folders <em>Grain Size.zip </em>and<em> CT data MI17-04.zip </em>(as .dcm files).</p> <p>The seismic profiles are provided as .SGY files (<em>Seismic Pinger Data.zip</em>).</p>
Woerthersee seismic and core data for the publication "High-resolution calibration of seismically-induced lacustrine deposits with historical earthquake data in the Eastern Alps (Carinthia, Austria)"
<p>This dataset comprises the core data and the 3.5 kHz seismic data of Wörthersee, a lake in the Eastern European Alps, Austria. Together with a dataset from Millstättersee (core and seismic data: 10.5281/zenodo.5875911; bathymetric data: 10.5281/zenodo.5875923), this is the basis for the publication Daxer et al. "High-resolution calibration of seismically-induced lacustrine deposits with historical earthquake data in the Eastern Alps (Carinthia, Austria)".</p> <p>24 short cores were analysed with a multi-sensor core logger (MSCL) and photographed with a smartcube camera image scanner and an ITRAX core scanner. The generated data are available in the folders <em>MSCL.zip</em> and <em>Photos.zip</em>. Some core sections were also analysed with a Malvern Mastersizer 3000. The generated grain-size data are provided in the folder <em>Grain Size.zip</em>.</p> <p>The seismic profiles are provided as .SGY files (<em>Seismic Pinger Data.zip</em>).</p>
Archive of the analysis results of the microtremor data obtained from a seismic array with a radius of 0.58 m distributed to the participants of the blind prediction experiments for the ESG6 symposium
<p>This is a supplemental material of the paper "Array-size dependency of the upper limit wavelength normalized by array radius for the standard spatial autocorrelation method" by Ikuo Cho, published in Earth, Planets and Space. It consists of the analysis results of the microtremor data observed using a seismic array with a radius of 0.58 m, which were distributed to the participants of the blind prediction experiments in ESG6. It involves all analysis results and script files to draw Figure 1 of the paper. See the "Availability of data and materials" section of the paper to download the original observed data and analysis code. See the main text of the paper for the details of the analysis.</p>
Data Set : Seismic Wave Propagation Simulations in Indo Gangetic Basin using Spectral Element Method
<p>Indo Gangetic (IG) basin is one of the largest alluvial basins in the world. The surrounding Himalayan topography and the geometry of the basin make the IG basin unique. The analysis of seismic response of the basin is important as the region is seismically active with more than 40% of Indian population residing in it. This online database consists of the input files for performing the spectral finite element simulation for IG basin by incorporating the 3D variation of material properties and basin geometry. The input files consists of mesher, solver and CMTSOLUTION files for SPECFEM3D Cartesian (Version-3) simulation.</p>
Machine Learning Ready Induced Seismicity Data
<p>This dataset contains previously published data on induced seismicity that has been processed to be machine learning ready.<br> <br> The data contains time series of the cumulative number of seismic events in certain areas and the corresponding pressures induced from injecting fluids into the ground. The natural task is to forecast future seismicity given past seismicity and pressures. These datasets aim to require as little seismology experience as necessary to prepare the data for forecasting algorithms. <br> <br> Data is provided for different locations. For Decatur Illinois, the seismic data was taken from Williams-Stroud et al., 2018 and the pressure data originated from Luu et al., 2022. Data aggregated over the whole region lies in the temporal_datasets/decatur_illinois/ folder. The region was further subdivided into subregions and the corresponding data stored separately (e.g. in loc1). </p> <p>The Kansas data originated from Cochran et al., 2018 and is further divided into subregions. </p> <p>The Cushing, Oklahoma is adapted from Skoumal et al., 2020. </p> <p>Each seismic file contains the following columns: epoch latitude longitude depth easting northing magnitude. The epoch corresponds to the number of seconds since a certain date (e.g. November 17, 2011 for Decatur). Each seismic event corresponds to one row in the file. <br> <br> Each pressure file contains the following columns: epoch pressure dpdt. dpdt is the derivative of pressure. </p>
Husmuli Injection and Seismicity Data 2015-2020
<p>This dataset comprises the injection data, and seismic catalogue recorded in the Húsmúli reinjection area (Hellisheiði geothermal field, SW Iceland) between 2015 and 2020.</p> <p>The hydraulic data was acquired and processed by Reykjavik Energy/ON power, the operator of the Hellisheiði geothermal field. The seismic catalogue is curated by the Icelandic Met Office.</p>
seismic data for solitons
<p>Suplementary data (SD for paper:"<strong>A locally generated high-mode nonlinear internal wave detected on the shelf of the northern South China Sea from marine seismic observations</strong>".</p> <p> </p>
Data & scripts - Estimates on the possible annual seismicity of Venus
<p>Data and scripts to reproduce the work in Van Zelst et al. (2024): 'Estimates on the possible annual seismicity of Venus'. See the file 'description_data&scripts.pdf' for more details. </p>
Partial angle-stacked seismic data for quantitative stress prediction
<p>The uploaded partial angle-stacked seismic data can be used as the input file to our inversion method to estimate the effective stress and other elastic parameters of subsurface reservoirs directly. </p>
Data from: Numerical Simulation of the Atmospheric Signature of Artificial and Natural Seismic Events
<p>This data is related to the seismic hammer experiment discussed in "Numerical Simulation of the Atmospheric Signature of Artificial and Natural Seismic Events" by Martire et al. (2018, DOI will be added upon acceptance of the manuscript).</p> <p>The .zip file contains 3 .mseed files, and 1 .txt file. The .mseed are the raw seismometer signals. The .txt details the position of the sensor.</p> <p>Remaining data used in our paper can be found in the repository related to "Detection of Artificially Generated Seismic Signals using Balloon-borne Infrasound Sensor" by Krishnamoorthy et al. (2018, DOI 10.1002/2018GL077481). That repository has DOI 10.6084/m9.figshare.6137507.</p>
Seismic data (ASCII)
<p>Each file corresponds to a single day of data.</p> <p>Signal amplitude (0± 2048) of the 6 sensors (on each line) at a sampling rate of 250 Hz.</p> <p> </p>
Subpixel optical correlation co-seismic offsets for the Mw 6.4 and Mw 7.1 Ridgecrest, California earthquakes, from Copernicus Sentinel 2 data
<p>Two strong earthquakes (Mw 6.4 and Mw 7.1) took place near Ridgecrest, California, on July 4 2019 and July 6, respectively.</p> <p><a href="https://earthquake.usgs.gov/earthquakes/eventpage/ci38443183/executive">https://earthquake.usgs.gov/earthquakes/eventpage/ci38443183/executive</a></p> <p><a href="https://earthquake.usgs.gov/earthquakes/eventpage/ci38457511/executive">https://earthquake.usgs.gov/earthquakes/eventpage/ci38457511/executive</a></p> <p>In order to assess surface ruptures and the displacement field from the earthquakes, we used subpixel image correlation with Copernicus Sentinel-2 optical imagery (Band 4). MicMac and CosiCorr software was used to to extract the 2D (East-West and North-South) horizontal co-seismic displacement field.</p> <p>Four high-resolution figures are given per method and component (EW and NS). Road network (white lines - from OpenStreetMap) and Quaternary Faults (black polylines) from USGS (<a href="https://earthquake.usgs.gov/hazards/qfaults/">https://earthquake.usgs.gov/hazards/qfaults/</a>) are used for overlay.</p> <p>Rasters are given per software used (MICMAC_ for MicMac and COSI for CosiCorr), with a pixel resolution of 20m. Final product is corrected with detrending (to remove mostly registration errors) and filtered to remove noise. Stripes resulting from pushbroom scanner and orbit errors were not removed at this product (visible as WNW-ESE and NNE-SSW linear parallel stripes).</p> <p>-North-South displacement: positive values to the North.</p> <p>- East-West displacement: positive values to the East.</p> <p>Raster files are projected in UTM Zone 11North WGS84 ( EPSG:32611)</p> <p> </p> <p>A contribution to <strong>CEOS Working Group Disasters:</strong> Seismic Demonstrator</p> <p><strong>Copyright:</strong> Contains modified Copernicus Sentinel data (2019), OpenStreetMap data (2019), Quaternary Fault and Fold Database of the United States - USGS (2019)</p>
Data products for "3D modeling of long-term slow slip events along the flat-slab segment in the Guerrero Seismic Gap, Mexico"
<p>Data products for '3D modeling of long-term slow slip events along the flat-slab segment in the Guerrero Seismic Gap, Mexico' by A. Perez-Silva, D. Li, A.-A. Gabriel and Y. Kaneko</p>
Data for "The interplay between seismic and aseismic slip along the Chaman fault illuminated by InSAR" submitted to JGR: Solid Earth
<p>This compressed folder contains data presented in Figures of the following paper : <strong>"The interplay between seismic and aseismic slip along the Chaman fault illuminated by InSAR" </strong>by<em> </em>M. Dalaison, R. Jolivet, E. M. van Rijsingen and S. Michel, submitted to <em>JGR: Solid Earth </em>in August 2021.</p> <p>Data are in their final processed version. Raw data used in this study are freely available online ( scihub.copernicus.eu, earthdata.nasa.gov, www.ecmwf.int, pubs.usgs.gov/of/2007/1103 )</p>
InSight's seismic and meteorological data related to the Martian convective vortices
<p><strong>Overview:</strong></p> <p>This repository includes the catalog related to Martian convective vortices observed by NASA's InSight mission. The detailed description is made in the JGR Planet paper entitled "Systematic catalog of Martian convective vortices observed by InSight" by Onodera et al. When you use the information in the catalog, please refer to the following citation.</p> <ul> <li>Onodera, K. et al. (2023), InSight's seismic and meteorological data related to the Martian convective vortices, Zenodo,<em><strong> </strong></em>doi:10.5281/zenodo.7801343<em><strong>.</strong></em></li> </ul> <p><strong>Files:</strong></p> <p>The first numbers in each file name correspond to the ID number included in the catalog file (InSight_CV_Catalog.pickle). All files are in csv format including time in Local Mean True Time in sol, respective observation records. If a number is missing, that means the corresponding data were not available on that sol (at least with the sampling rate we focused on in our paper).</p> <ul> <li><strong>InSight_CV_Catalog.pickle</strong>: It includes all estimated parameters presented by Onodera et al. (2023).</li> <li> <p><strong>PS.zip</strong>: 20 min long pressure data centered at the maximum pressure drop time (LMST, Pressure).</p> </li> <li> <p><strong>VBB_ACC.zip</strong>: 20 min long acceleration data centered at the maximum pressure drop time (LMST, Z comp., N comp., E comp.).</p> </li> <li> <p><strong>WSpeed_WDir_ATemp_calib.zip</strong>: 20 min long calibrated wind & air temperature data centered at the maximum pressure drop time (LMST, Wind speed, Wind direction, Air temperature).</p> </li> </ul>
Legacy seismic data for large repeating earthquakes in Mexico
<p>In this dataset we include, in two different compressed files:1) a set of images of scanned Galitzin legacy seismograms recorded at the De Bilt Seismological Station, The Netherlands, corresponding to different large Mexican earthquakes (M>7.0) occurred between 1928 and 1978, and 2) the corresponding vectorized and corrected data.</p> <p><br> Description of compressed files:</p> <p>1. DBN-Vectorized-Seismograms.zip , that contains the vectorized data.</p> <p>2. DBN-Scanned-Galitzin-Seismograms.zip , that contains the scanned images of seismograms.</p> <p> </p> <p>Description of the files:</p> <p>1. Naming of Data Files.<br> Data are archived in XY ASCII files. The files are named as: STN_yyyy_mm_dd_C_p?.txt. Here:</p> <p>STN refers to the Station identification code<br> yyyy stands for the four-digit year<br> mm designates the two-digit month<br> dd signifies the two-digit day<br> C indicates the Component (either Z, NS, or EW)<br> p? represents the segment (0, 1, or 2) when applicable. Seismograms are sometimes divided in more than one segment (up to 3). The first segment is labeled P0. Subsequent segments are named P1 and P2. </p> <p> </p> <p>2. Naming of Scanned images.<br> Data are archived in JPG images. The files are named as: STN_yyyy_mm_dd_C.jpg. Here:</p> <p>STN refers to the Station identification code<br> yyyy stands for the four-digit year<br> mm designates the two-digit month<br> dd signifies the two-digit day<br> C indicates the Component (either Z, NS, or EW)<br> </p>
Knowledge-Graphs-for-Seismic-Data-and-Metadata: Data
<p><strong>Abstract</strong></p> <p>The increasing scale and diversity of seismic data, and the growing role of big data in seismology, has raised interest in methods to make data exploration more accessible. This paper presents the use of knowledge graphs (KGs) for representing seismic data and metadata to improve data exploration and analysis, focusing on usability, flexibility, and extensibility. Using constraints derived from domain knowledge in seismology, we define semantic models of seismic station and event information used to construct the KGs. Our approach utilizes the capability of KGs to integrate data across many sources and diverse schema formats. We use schema-diverse, real-world seismic data to construct KGs with millions of nodes, and illustrate potential applications with three big-data examples. Our findings demonstrate the potential of KGs to enhance the efficiency and efficacy of seismological workflows in research and beyond, indicating a promising interdisciplinary future for this technology.</p> <p><strong>Methods</strong></p> <p>The data here consists of, and was collected from:</p> <ul> <li>Station metadata, in StationXML format, acquired from IRIS DMC using the fdsnws-station webservice (https://service.iris.edu/fdsnws/station/1/).</li> <li>Earthquake event data, in NDK format, acquired from the Global Centroid-Moment Tensor (GCMT) catalog webservice (https://www.globalcmt.org) [1,2]. </li> <li>Earthquake event data, in CSV format, acquired from the Northern California Seismic Network (NCSN) catalog using the NCEDC's Northern California Earthquake Catalog Search webservice (doi.org/10.7932/NCEDC) [3].</li> <li>Earthquake event data, in CSV format, acquired from the USGS earthquake catalog webservice (doi.org/10.5066/F7MS3QZH) [4].</li> </ul> <p>A complete description of the StationXML file format can be found at https://www.fdsn.org/xml/station/.</p> <p>A complete description of the NDK file format can be found at https://www.ldeo.columbia.edu/~gcmt/projects/CMT/catalog/allorder.ndk_explained. </p> <p>A complete description of the NCEDC file format can be found at https://ncedc.org/pub/doc/cat1/catlist.txt.</p> <p>A complete description of the USGS file format can be found at https://earthquake.usgs.gov/data/comcat/#event-terms.</p> <p>Also provided are conversions from NDK and StationXML file formats into JSON format. </p> <p><strong>Usage Notes</strong></p> <p>No special programs or software is reqired to open the data files included here.</p> <p><strong>References</strong></p> <p>[1] Dziewonski, A. M., Chou, T. A., & Woodhouse, J. H. (1981). Determination of earthquake source parameters from waveform data for studies of global and regional seismicity. <em>Journal of Geophysical Research: Solid Earth</em>, <em>86</em>(B4), 2825-2852.</p> <p>[2] Ekström, G., Nettles, M., & Dziewoński, A. M. (2012). The global CMT project 2004–2010: Centroid-moment tensors for 13,017 earthquakes. <em>Physics of the Earth and Planetary Interiors</em>, <em>200</em>, 1-9.</p> <p>[3] NCEDC (2014), Northern California Earthquake Data Center. UC Berkeley Seismological Laboratory. Dataset. doi:10.7932/NCEDC.</p> <p>[4] U.S. Geological Survey, Earthquake Hazards Program, 2017, Advanced National Seismic System (ANSS) Comprehensive Catalog of Earthquake Events and Products: Various, https://doi.org/10.5066/F7MS3QZH.</p> <p> </p>
DATA SET FOR: Active faulting, submarine surface rupture and seismic migration along the Liquiñe-Ofqui fault system, Patagonian Andes
<p>Data description: These data corresponde to high-resolution bathymetry and seismic reflection profiles obtained in the inner fjord west of Puerto Aysén (between 73.13°- 72.68°W and 45.32°-45.47°S; Figs. 1 and 2). The data set was obtained during a geophysical study as part of the DETSUFA project (Deslizamientos Tsunamigénicos en el Fiordo de Aysén; Lastras et al., 2013), which took place between March 4th and 17th, 2013, aboard the R/V BIO Hésperides.<br> <br> KONGSBERG SIMRAD multibeam EM-1002S was used to obtain bathymetric data, and it works with 111 beams at a 96 kHz sonar frequency and with a maximum ping rate of >10 Hz. Equidistant mode was used for swath bathymetry acquisition. This array maximized the number of beams facilitating data acquisition and obtaining a homogenized final grid with improved resolution, with tracks separated every 150 m. The swath thickness was the same regardless of width, generating a 50% overlap between each track, with the exception of areas located near the coast. Expendable Bathythermograph (XBT) probes were used at specific sites to measure changes in water sound velocity due to eventual changes in fresh water circulation, tides, and sediment.<br> <br> Seismic reflection data were acquired using an array of two BOLT air guns (165 and 175 inches3), which were towed behind the vessel stern. The configuration used in the seismic sources was 2,000 psi, a depth of 3 m for the gun, with a firing rate of 15 m over the seafloor. A 100 m long mini-streamer with a 25 m active section, corresponding to one single channel, recovered the shots. The seismic data were recorded by using the DELPH SEISMICPLUS system with a recording length of 4.0 s and a preamplifier gain of 8 Hz. The raw seismic data were processed aboard the SMT Kingdom Suite, including the navigation and standard processes of electrical noise removing (50 Hz filter), gain amplifier and bandpass filtering, to improve data visualization. Postprocessing included the migration of the sea bottom diffractions and the muting of the water column performed in Seismic-Unix.</p> <p>Files:</p> <p>Raw Seismic reflection data for lines 05, 06 and 07 (SU & SEG files)</p> <p>Masked Seismic profiles for lines 05, 06 and 07 (SU, PDF & PS files)</p> <p>Bathymetry of inner and outer Aysén Fjord (ASCII file)</p>
Model data repository of "How sediment thickness influences subduction dynamics and seismicity"
<p>This repository provides the code and data to run the Seismo-Thermo-Mechanical model with a sediment thickness T<sub>sed</sub> of 4 km on a cluster using executables.</p>
Data for the paper "An Inversion Algorithm for Deriving Shallow Structure using Co-located Wind, Pressure, and Seismic Data"
<p>Wind, pressure, and seismic data used in the paper "An Inversion Algorithm for Deriving Shallow Structure using Co-located Wind, Pressure, and Seismic Data"</p>
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International Brain Laboratory public data
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OpenNeuro
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