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662 results for “seismicity”
Seismic Azimuthal Anisotropy Model for the Juan de Fuca ‐ Gorda Plate System
<p>This dataset is supplementary to:</p> <p>Liu, C., et al. (2024) Seismic Azimuthal Anisotropy Within the Juan de Fuca ‐ Gorda Plate System, GRL</p> <div> <p>DOI: 10.1029/2024GL111835</p> <p>Azimuthal anisotropy model from 10-100 km</p> <p> </p> <p>Model 1: <code>JdFG_Azi_anisotropy_model.nc</code>: contains</p> <ul> <li>Anisotropic lithospheric layer: base of sediments to 20km below the Moho</li> <li>Anisotropic asthenospheric zone: 50 km thick layer beneath the lithosphere layer</li> <li>A complementary deeper asthenosphere layer </li> </ul> <p>Model 2:<code>JdFG_Azi_anisotropy_model_ios_crust.nc</code>: </p> <ul> <li>Anisotropic lithospheric layer: the Moho to 30km below the Moho</li> <li>Anisotropic asthenospheric zone: 50 km thick layer beneath the lithosphere layer</li> <li>A complementary deeper asthenosphere layer </li> </ul> </div> <p>The uploaded file uses NetCDF4 format.</p> <p>File format:</p> <p><code>Longitude</code>, <code>Latitude</code>, <code>Depth</code>, <code>fa</code>,<code>unc_fa</code>,<code>amp</code>,<code>unc_amp</code></p> <ul> <li> <p>Dimensions:</p> <ul> <li><code>Longitude</code>: -130.2° to -125.0° with 0.4° interval.</li> <li><code>Latitude</code>: 40.6° to 49.0° with 0.4° interval.</li> <li><code>Depth</code>: 10 to 100 with 10 km interval</li> </ul> </li> <li>Model variables: <ul> <li><code>fa</code>: depth-dependent fast azimuth (deg)</li> <li><code>unc_fa</code>: uncertainty for depth-dependent fast azimuth (deg)</li> <li><code>amp</code>: depth-dependent anisotropy amplitude (%)</li> <li><code>unc_amp</code>: uncertainty for depth-dependent anisotropy amplitude (%)</li> </ul> </li> </ul>
Geodetic model of the March 2021 Thessaly seismic sequence inferred from seismological and InSAR data
<p>A selection of Sentinel-1 (S1) wrapped and unwrapped measurements used in this study (from "a" to "u" files in tiff format as indicated in the word file attached). S1 data were processed by using our own internally developed InSAR processing chain.<br> <br> Earthquakes data locations.</p> <p><br> </p> <p> </p>
Ground deformation map for the 2011 Hawthorne seismic swarm (Nevada, USA)
<p>This repository contains eight interferograms from RADARSAT-2 and ENVISAT satellites acquired over the March-September, 2011 Hawthorne seismic swarm, Nevada (USA).</p> <p>Five interferograms were obtained by processing the Canadian Space Agency RADARSAT-2 SAR data along one ascending track (incidence angle 35<sup>◦</sup> and heading angle 350<sup>◦</sup>) with GAMMA software.</p> <ul> <li>20110322_HH_20110415_HH.adf.unw.grd</li> <li>20110226_HH_20110415_HH.adf.unw.grd</li> <li>20110322_HH_20110720_HH.adf.unw.grd</li> <li>20110415_HH_20110626_HH.adf.unw.grd</li> <li>20110315_HH_20110526_HH.adf.unw.grd</li> </ul> <p>Three interferograms were obtained by processing the European Space Agency ENVISAT SAR data along one descending track (incidence angle 35<sup>◦</sup> and heading angle -166<sup>◦</sup>) with DORIS software and ISCE software.</p> <ul> <li>20110320_20110618.lld.grd</li> <li>20110419_20110618.lld.grd</li> <li>20110718_20110916_filt_topophase.unw.geo</li> </ul> <p>These interferogram were generated for figures in: Jiang, Y., Samsonov, S. V., and González, P. J. (2021). "Aseismic fault slip nucleation during a shallow normal-faulting seismic swarm constrained using a physically-informed geodetic inversion method. " JGR: Solid Earth.</p>
Seismic radial and azimuthal anisotropy tomography beneath Greenland and surrounding regions
<p>Dear readers,</p> <p>These four files contain P-wave velocity model beneath Greenland and surrounding regions obtained by regional anisotropy tomography (Toyokuni & Zhao, 2021, ESS). </p> <p>P-wave radial anisotropy (RAN) model<br> RAN_DV0.DAT: Isotropic component (DV0) of RAN tomography<br> -FORMAT: Latitude(deg), Longitude(deg), Depth(km), dVp0(%)</p> <p>RAN_AI.DAT: Anisotropic component RAN tomography<br> -FORMAT: Longitude(deg), Latitude(deg), Depth(km), alpha(%)</p> <p>P-wave azimuthal anisotropy (AAN) model<br> AAN_DV0.DAT: Isotropic component (DV0) of AAN tomography<br> -FORMAT: Latitude(deg), Longitude(deg), Depth(km), dVp0(%)</p> <p>AAN_AI.DAT: Anisotropic component AAN tomography<br> -FORMAT: Longitude(deg), Latitude(deg), Depth(km), FVD(deg), beta(%)</p> <p>We note that the tomography was conducted in the transformed coordinates. The details of the coordinate transformation are described in the above paper. We also note that the fast velocity direction (FVD) of the AAN model is in the transformed coordinates. Please contact us if you want any program to convert it back to the geographical coordinates.</p> <p>We hope it is useful to you.</p> <p>Kind wishes,</p> <p>Genti Toyokuni & Dapeng Zhao<br> Tohoku University, Japan<br> E-mail: toyokuni@tohoku.ac.jp</p> <p>Reference:<br> Toyokuni, G. & Zhao, D. (2021).<br> P wave tomography for 3-D radial and azimuthal anisotropy beneath Greenland and surrounding regions.<br> Earth and Space Science, under review.</p>
Stress Drop Catalog for "Spatio-temporal evolution of earthquake static stress drop values in the 2016-2017 Central Italy seismic sequence" - Kemna et al. 2021 JGR - Solid Earth
<p>Catalog with stress drop estimates for "Spatio-temporal evolution of earthquake static stress drop values in the 2016-2017 Central Italy seismic sequence"</p> <p>Kemna et al., 2021, JGR: Solid-Earth, https://doi.org/10.1029/2021JB022566.</p> <p>Description of columns:</p> <p><strong>Earthquake information</strong></p> <ul> <li>ID - INGV Earthquake ID</li> <li>Latitude - Latitude in Degrees</li> <li>Longitude - Longitude in Degrees</li> <li>Depth - Depth in km</li> <li>Magnitude_INGV - Magnitude reported by INGV</li> <li>Origin_UTC - UTC Origin Time in ISO Format</li> <li>Catalog - Catalog source of specific event. See section 2.1 for details</li> <li>Profile_distance_norcia - Distance of earthquake from Norcia Mainshock location projected onto a NW-SE trending line</li> <li>Dayafter_20160101 - Day after start of catalog in float</li> </ul> <p><strong>Single spectra fitting estimates</strong></p> <ul> <li>mw_s_mean - Moment Magnitude averaged over station estimates</li> <li>mw_s_err - 95% error (from delete-one jackknife-mean)</li> <li>m0_s_mean - Seismic Moment in Nm averaged over station estimates</li> <li>m0_s_err - 95 % error(from delete-one jackknife-mean)</li> <li>fc_s_sssa_mean - Corner frequency estimate averaged over station estimates</li> <li>fc_s_sssa_err - 95 % error(from delete-one jackknife-mean)</li> <li>strdrop_s_sssa_mean - Stress drop estimate averaged over station estimates</li> <li>strdrop_s_sssa_err - 95 % error(from delete-one jackknife-mean)</li> <li>sample_size_s_sssa - Number of stations with an estimate</li> <li>azimuthal_gap_s_sssa - Maximum azimuthal gap</li> <li>alpha_vel - P-wave velocity in m/s at Hypocenter</li> <li>beta_vel - S-wave velocity in m/s at Hypocenter</li> </ul> <p><strong>Cluster-event method estimates</strong></p> <ul> <li>fc_s_cema_mean - Corner frequency estimated averaged over clusters</li> <li>fc_s_cema_err - 95 % error(from delete-one jackknife-mean)</li> <li>strdrop_s_cema_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> <li>strdrop_s_cema_err - 95 % error</li> </ul> <p><strong>Spectral Ratio fitting estimates</strong></p> <ul> <li>fc1_s_rsta_mean - Target event corner frequency estimate using automatic source spectra fitting averaged over eGfs</li> <li>fc1_s_rsta_err - 95 % error(from delete-one jackknife-mean)</li> <li>strdrop_s_rsta_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> <li>strdrop_s_rsta_err - 95 % error</li> <li>egf_number_rsta_s - Number of eGfs for each target event</li> <li>fc1_s_rrta_mean - Target event corner frequency estimate using semi-automatic spectral ratiofitting averaged over eGfs</li> <li>fc1_strdrop_s_rrta_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> <li>fc2_s_rrea_mean - eGf event corner frequency estimate using semi-automatic spectral ratiofitting averaged over eGfs</li> <li>fc2_strdrop_s_rrea_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> </ul> <p><strong>Magnitude-normalized stress drop</strong></p> <ul> <li>prio_strdrop_s - Which type of estimate is used</li> <li>magbin_s - Magnitude bin to which event is associated</li> <li>prio_strdrop_s_magbinmean - Stress drop mean for specific magnitude bin</li> <li>prio_strdrop_s_magbinstderr - 95 % error(from delete-one jackknife-mean)</li> <li>prio_strdrop_s_magnitude-normalized - Magnitude-normalized stress drop estimate</li> </ul>
Dataset - seismic data from central-western Italy used in the paper on rapid prediction of ground motion using a Convolutional Neural Network
<p>The dataset published here is the central-western Italy dataset used in the paper "<em>Transfer learning: Improving neural network based prediction of earthquake ground shaking for an area with insufficient training data"</em> (<a href="https://arxiv.org/abs/2105.05075">https://arxiv.org/abs/2105.05075</a>). The code for the paper is available at <a href="https://github.com/djozinovi/TLpredIM">https://github.com/djozinovi/TLpredIM</a>. The abstract of the paper:</p> <blockquote> <p>In a recent study (Jozinović et al, 2020) we showed that convolutional neural networks (CNNs) applied to network seismic traces can be used for rapid prediction of earthquake peak ground motion intensity measures (IMs) at distant stations using only recordings from stations near the epicenter. The predictions are made without any previous knowledge concerning the earthquake location and magnitude. This approach differs from the standard procedure adopted by earthquake early warning systems (EEWSs) that rely on location and magnitude information. In the previous study, we used 10 s, raw, multistation waveforms for the 2016 earthquake sequence in central Italy for 915 events (CI dataset). The CI dataset has a large number of spatially concentrated earthquakes and a dense station network. In this work, we applied the CNN model to an area around area near Pisa, Italy. In our initial application of the technique, we used a dataset consisting of 266 earthquakes recorded by 39 stations. We found that the CNN model trained using this smaller dataset performed worse compared to the results presented in the original study by Jozinović et al. (2020). To counter the lack of data, we adopted transfer learning (TL) using two approaches: first, by using a pre-trained model built on the CI dataset and, next, by using a pre-trained model built on a different (seismological) problem that has a larger dataset available for training. We show that the use of TL improves the results in terms of outliers, bias, and variability of the residuals between predicted and true IMs values. We also demonstrate that adding knowledge of station positions as an additional layer in the neural network improves the results. The possible use for EEW is demonstrated by the times for the warnings that would be received at the station PII.</p> </blockquote>
Dataset of "Triggering and propagation of exogeneous sediment pulses in mountain channels: insights from flume experiments with seismic monitoring"
<p>Dataset of "Triggering and propagation of exogeneous sediment pulses in mountain channels: insights from flume experiments with seismic monitoring".</p>
Saskatchewan seismic data set 2
<p>Seismic data from Saskatchewan glacier. Includes the waveform data, the log files, and the instrument response file.</p>
Travel times of temporary seismic arrays and 3-D Vp and Vs models in the mid-to-south segment of the Red River fault, China
<p>The travel times of the temporary seismic arrays are manually picked. The 3-D Vp and Vs models beneath the mid-to-south segment of the Red River fault are obtained based on the improved double-difference tomography method and abundant data. </p>
Woerthersee sediment core data for the publication "Validation of seismic hazard curves using a calibrated 14 ka lacustrine record in the Eastern Alps, Austria"
<p>This dataset comprises sediment core data of Wörthersee, a lake in the Eastern European Alps, Austria. Together with a dataset comprising the seismic data (10.5281/zenodo.6479186), this is the basis for the publication Daxer et al. "Validation of seismic hazard curves using a calibrated 14 ka lacustrine record in the Eastern Alps, Austria".</p> <p>The files contain the following data:</p> <ul> <li>Core images Long Cores.zip: Core images of the Wörthersee Kullenberg-type long cores acquired with an ITRAX core scanner</li> <li>Core images Short Cores.zip: Core images of the Wörthersee gravity short cores (hammer-coring or trigger cores of the Kullenberg system) acquired with an ITRAX core scanner; provided as .tif files</li> <li>CT data WOER18-L5-X-Dicom.zip: X-ray computed tomography data acquired with a Siemens SOMATOM Definition AS (voxel size 0.2 x 0.2 x 0.3 mm); provided in DICOM format</li> <li>MSCL data.zip: Data acquired with a Geotek Multi-sensor core logger (e.g. magnetic susceptibility and gamma density); provided as Excel spreadsheets</li> <li>XRF data.zip: X-ray fluorescence data acquired with a ITRAX core scanner; provided in .csv format</li> </ul>
Seismic Magnitude Clustering is Prevalent in Field and Laboratory Catalogs [DATA]
<p>Catalogs for Nature Communications article: Seismic Magnitude Clustering is Prevalent in Field and Laboratory Catalogs.</p> <p> </p> <p>Update: In DataVariableExplanation, two catalogs from University of Minnesota</p> <p>Mixed mode and mode I bending data description needs to show that the third column is in seconds.</p>
A 2D seismic reflection dataset of the Caosiyao giant porphyry Mo deposit in the shallow coverage area in Jining, Inner Mongolia, China
<p>Our paper, '<strong>A 2D seismic reflection</strong> <strong>dataset of the Caosiyao giant porphyry Mo deposit in the shallow coverage area in Jining, Inner Mongolia, China</strong>' in <strong>Geoscience Data Journal, </strong>provides a new open dataset for the Caosiyao deposit area to address the paucity of publicly accessible geophysical datasets in shallow covered areas or hardrock areas.</p> <p>Three two-dimensional (2D) seismic reflection exploration profiles (profile 3, 8 and 16) were completed in the Caosiyao deposit area with a total length of 20 km, 488 shots were stimulated by the combination of double-couple seismic sources. In the Caosiyao deposit area, three two-dimensional (2D) seismic reflection exploration profiles (profiles 3, 8 and 16) totaling 20 km in length were completed. The combination of double-couple seismic sources triggered 488 shots. Briefly, the files consist of 488 shots in Caosiyao deposit area: ‘Caosiyao - seismic profile 3’ contains 88 single shot records; ‘Caosiyao - seismic profile 8’ and ‘Caosiyao - seismic profile 16’ both contains 200 single shot records. In addition, all 2D reflection seismic single-shot data are in SEGD format. </p> <p>The international advanced super detector and double controllable seismic source combined excitation instruments were used to guarantee acquisition quality and acquisition depth. The controllable seismic source combination of a 2013 French Nomad-65, weighing 65000 pounds (28 tons), was employed for excitation, and a Sercel 428XL digital seismic acquisition equipment was used for recording.</p> <p>We thoroughly analyze the seismic profile 8, and our dataset afterwards contributes to a better comprehension of the deep geological structures of the research area and delineate the location of the shallow and deep rock masses.</p> <p>Note: The datasets given are protected with password, and the password is available in our published paper.</p>
Ice-floe based records of seismic events at 85°E Gakkel Ridge, Arctic Ocean
<p>This dataset contains seismic events recorded by seismometers on drifting ice<br> floes at 85°E volcano Gakkel Ridge, Arctic Ocean. The experiment is described in<br> Korger & Schlindwein (2014). After a reanalysis and selection of suitable<br> earthquakes, the data were reused by Koulakov et al.(2022) for a seismic<br> tomography. The data set contains waveform files and phase picks of the selected<br> events in Nordic Format along with the station coordinates.</p>
Seismic efficiency and seismic moment for small craters on Mars formed in layered uppermost crust
<p>Input files used in the paper submitted to JGR: Planets, November 2022.</p> <p>Table with location and sizes of all mapped craters.</p>
Waveform data for centroid moment tensor solutions presented in publication "Bayesian seismic source inversion with a 3-D Earth model of the Japanese islands"
<p>This dataset contains waveform data for centroid moment tensor solutions inferred using Hamiltonian Monte Carlo sampling algorithm and a 3-D Earth model of the Japanese islands. Specifically, it includes processed observed waveforms from the Full Range Seismograph Network of Japan (F-Net, http://www.fnet.bosai.go.jp) and synthetic waveforms for the maximum-likelihood solutions as well as Global Centroid Moment Tensor (GCMT) solutions for all study events inverted at different periods. Detailed description of the dataset is included in the README file. </p>
Seismic Inverted Impact force and Digital Elevation Models before and after the 2018 Baige Landslide
<p>For the data of inverted seismic force, the seismic signals recorded by the broadband seismic stations were prepared for the inversion of the force-time function by the following series of actions:</p> <ul> <li>Removing the instrumental response</li> <li>Resampling to 0.5 s</li> <li>Integrating the signals from velocity to displacement</li> <li>Rotating the horizontal components to the radial and transverse direction</li> <li>Filtering the signals between periods of 30 and 140 s</li> </ul> <p>For the digital elevation models (DEMs) of the Baige landslide, the pre-failure DEM with 10 m grid spacing was obtained from the Sichuan Bureau of Surveying Mapping and Geoinformation (SBSMG). At the same time, the post-failure DEM was derived from the UAV-based photogrammetry.</p>
cross-correlations of seismic data nodes Lipari 2018
<p>Stacked cross-correlation functions of the continuous seismic data recorded at the nodes installed at Lipari in 2018. Data in SAC format. All details about stations (name, latitude longitude, elevation) and data ( sampling rate, starting and ending time, etc) are in the HEADER of each file.</p>
Dataset associated with article: Self-sufficient seismic boxes for monitoring glacier seismology in Greenland
<p>Dataset associated with article: Self-sufficient seismic boxes for monitoring glacier3 seismology in Greenland</p> <p>Contains:<br> - Seismic data of both SG-boxes and regular geophones from Gornergletscher fieldtest, 2021( Seismic_Data_Gorner_Fieldtest.zip) <br> --> SG-box data naming: GO"station_number"SG <br> --> Geophone data naming: GO"station_number"GP<br> <br> - Weather data Gornergletscher fieldtest from Monte Rosa, Meteo Swiss Weather station ( Weather_data_Gorner_Fieldtest_2021.zip) <br> --> 1hr wind averages <br> --> 1hr temperature averages<br> <br> - MSR logger data from SG-boxes from Gornergletscher fieldtest, 2021. Every 5 min these log battery power, tilt (along three axes, temperature and humidity inside the box and light strength on two sides of the SG-box. ( MSR_logger_data_SGboxes_Gorner_Fieldtest.zip )<br> <br> - Seismic data of SG box (Sensor code BSM) next to weather station first acquisition Greenland 2021 ( Seismic_Data_SG_Box_first_acquisition_Greenland_2021.zip) <br> --> .pri0 is East component, .pri1 is North component, .pri2 is Vertical component.<br> <br> - Weather data from weather station next to SG-box (sensor code BSM) during first acquisition Greenland 2021 ( Weather_station_data_Greenland_2021.zip) <br> --> The weather station logs a value every two hours.</p> <p> </p>
GNSS position time series (.neu files) and seismic velocity strcture (Profil_lat2822_Vp_Vs.dat) used in the manuscript
<p>These are the raw GNSS time series files (in .neu format) and seismic velocity strcture file (Profil_lat2822_Vp_Vs.dat) we used in the manuscript. These data are not allowed to use before the manuscript is accepted.</p>
Training, Validation and Test Sets for paper 'A Little Data goes a Long Way: Automating Seismic Phase Arrival Picking at Nabro Volcano with Transfer Learning'
<p>Training, Validation and Test Data for model presented in paper 'A Little Data Goes A Long Way: Automating Seismic Phase Arrival Picking at Nabro Volcano with Transfer Learning', submitted to Journal of Geophysical Research: Solid Earth.</p> <p>Files:</p> <p>- train_events_2498.h5 = training set of seismic waveforms (events with P-/S-wave labelled arrivals only, i.e., no noise waveforms)</p> <p>- train_events_2498.pkl = event training set metadata (UTC P-/S-wave phase arrival times)</p> <p>- train_noise_2498.h5 = training set of seismic waveforms (noise sections only, i.e., no event waveforms)</p> <p>- train_noise_2498.pkl = noise training set metadata (UTC time for training noise waveforms)</p> <p>- val_events.h5 = validation set of seismic waveforms (events with P-/S-wave labelled arrivals only, i.e., no noise waveforms)</p> <p>- val_events.pkl = event validation set metadata (UTC P-/S-wave phase arrival times)</p> <p>- val_noise.h5 = validation set of seismic waveforms (noise sections only, i.e., no event waveforms)</p> <p>- val_noise.pkl = noise validation set metadata (UTC time for validation noise waveforms)</p> <p>- test.h5 = test set of seismic waveforms (events and noise)</p> <p>- test_events.pkl = event test set metadata (UTC P-/S-wave phase arrival times for test event waveforms)</p> <p>- test_noise.pkl = noise test set metadata (UTC time for test noise waveforms)</p> <p>- nabro_2011-247.mseed = 24 hours seismic data from Nabro Urgency Array (2011-09-04), saved in mseed format (e.g., can be read with obspy)</p> <p>- nabro_2011-269.mseed = 24 hours seismic data from Nabro Urgency Array (2011-09-26), saved in mseed format (e.g., can be read with obspy)</p> <p> </p> <p>Further details and code for reading and using these files can be found at the GitHub repo for this paper: <a href="https://github.com/sachalapins/U-GPD">https://github.com/sachalapins/U-GPD</a></p> <p> </p>
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