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35 results for “Seismology”
A dataset of published journal papers using neural networks for seismological tasks.
<p>This is a dataset of 637 journal papers applying neural networks for various tasks in seismology spanning January 1988 to January 2022. The dataset mainly includes peer reviewed papers and does not contain duplicated works. It follows a hierarchical classification of papers based on seismological tasks (i.e. category, sub_category_I, sub_category_II, task, and sub_task). For each paper following information are provided: 1) first author's last name, 2) publication year, 3) paper's title, 4) journal 's name, 5) machine learning method used, 6) the type of used neural network, 7) the name of neural network architecture, 8) the number of neurons/kernels in each hidden layer, 9) type of training process, i.e. supervised, semi-supervised, etc, 10) input data into the network, 11) output data, 12) data domain, i.e. time, frequency, feature, etc, 13) the type of data used for training, e.g. synthetic or real data, 14) the size of training set, 15) the metrics used to measure the performance, 16) performance scores, 17) the baseline method used for evaluation, and 18) a short note summarizing the paper's objective, its approach, and its significance. </p> <p>An updating version of the dataset can be find from here: https://smousavi05.github.io/dl_seismology/ and here:https://github.com/smousavi05/dl_seismology/tree/main/docs. </p> <p>An updating glossary of seismological tasks and relevant machine learning techniques and papers are provided here: https://smousavi05.gitbook.io/mlseismology/</p>
Data for the seismological studies on the 2017 MW5.5 Pohang earthquake
<p><strong># written by J.-U. Woo</strong><br> Data includes [1] cut seismogram provided from the Korea Meteorological Administration (KMA), Korea Institute of Geoscience and Mineral Resources (KIGAM), and Korea Hydro & Nuclear Power (KHNP), [2] an example of waveform cross-correlation, [3] phase arrival times, [4] cross-correlation measurements, and [5-7] three earthquake catalogs (1-3).</p> <p><strong>Suggested citation:</strong><br> <strong>1</strong>. J.‐U. Woo, M. Kim, D.‐H. Sheen, T.‐S. Kang, J. Rhie, F. Grigoli, W.L. Ellsworth, D. Giardini, 2019, An In‐Depth Seismological Analysis Revealing a Causal Link Between the 2017 M<sub>W</sub> 5.5 Pohang Earthquake and EGS Project, JGR solid earth, doi:10.1029/2019JB018368.</p> <p><strong>Further suggested citations:</strong></p> <p><strong>2</strong>. W.L. Ellsworth, D. Giardini, J. Townend, S. Ge, and T. Shimamoto, 2019, Triggering of the Pohang, Korea, Earthquake (Mw 5.5) by Enhanced Geothermal System Stimulation. Seismological Research Letters, 90(5), 1844-1858.</p> <p><strong>3</strong>. K.-K. Lee, W.L. Ellsworth, D. Giardini, J. Townend, S. Ge, T. Shimamoto, I.-W. Yeo, T.-S. Kang, J. Rhie, D.-H. Sheen, C. Chang, J.-U. Woo, C. Langenbruch, 2019, Managing injection-induced seismic risks. Science, 364(6442), 730-732.</p> <p><strong>4</strong>. C. Langenbruch, W. L. Ellsworth, J.-U. Woo and D. J. Wald, 2020, Value at Induced Risk: Injection-induced seismic risk from low-probability, high-impact events. Geophysical Research Letters, 47, e2019GL085878. http://doi.org/10.1029/2019GL085878.</p> <p><strong>Descriptions:</strong><br> [1] Cut seismogram ("wf_cut.gz"): Each sac file is named as "(station name).(event ID of catalog1).(component; E/N/Z).sac".<br> - Zero-padding may be applied to some waveforms.<br> [2] An example of waveform cross-correlation ("wf_cc.gz"): See the README.txt in "wf_cc.gz" for details<br> [3] Phase arrival times ("phphase.txt"):<br> - First column: event ID of catalog1<br> - Second column: station name<br> - Third column: yyyymmddHHMMDD (year, month, day of month, hour, minute)<br> - Fourth and fifth columns: P-wave arrivals in second (N/A: non available)<br> - Sixth and seventh columns: S-wave arrivals in second (N/A: non available)<br> [4] Waveform cross-correlation data ("phcc.txt"):<br> - First column: event 1 ID<br> - Second column: event 2 ID<br> - Third column: station name<br> - Fourth column: arrival time difference between event 1 and event2. The arrival time differences should be added to the arrivals of the event 2 to make maximum cross-correlation coefficients. See the details in [2].<br> - Fifth column: waveform cross-correlation coefficient after alignment<br> - Sixth column: two letters for component (E/N/Z) & phase(P/S), repectively<br> [5] Earthquake catalog 1 ("catalog1.txt") for initial locations<br> [6] Earthquake catalog 2 ("catalog2.txt") for final locations<br> [7] Earthquake catalog 3 ("catalog3.txt") regarding to the determined focal mechanisms</p>
Self-supervised learning of seismological data reveals undocumented eruptive sequences at the Mayotte submarine volcano - Supplementary Materials
<p>The following files are shared:<br> - The scripts used to train the model and generate the figures of the article<br> - The input images used to train the model as well as the final outputs (embedding matrix and the associated filenames matrix)<br> - The clusters organization with their associated images</p>
Dataset used in "Marine Sediment Characterized by Ocean-Bottom Fiber-Optic Seismology" by Spica et al., 2020 in Geophysical Research Letters
<p>3000fullhisy: raw data to reproduce Fig. 2<br> ppsdspec.npz: all spectrogram as shown in Fig. 3a<br> AllVelMods: All velocity model shown in Fig. 3b<br> ac.out.final.npz: auto-correlation image in Fig. 3c<br> DAS11_lpf5.stack51.grd: Earthquake wavefield as shown in Fig. 3d<br> </p> <p> </p>
Data set associated to the publication "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology"
<p>Data set of the scientific publication entitled "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology":</p> <p>Seismological sensors</p> <p>Microphones</p> <p>Barometers</p> <p>Accelerometers</p> <p>Detailed test report.</p>
Seismology dataset of Chianti swarm in Tuscan Italy
<p>Seismology data of Chianti swarm with earthquekes and focal mechanism of 2010-2015 period. Data are download by INGV database.</p>
Extreme hydrometeorological events, a challenge for gravimetric and seismology networks
<p>“Data supporting the paper published by Earth's Future: "Extreme hydrometeorological events, a challenge for gravimetry and seismological networks" by Van Camp, de Viron, Dassargues, Delobbe, Chanard, and Gobron, doi: 10.1029/2022EF002737, 2022.</p> <p>The data contains:</p> <p>1) A NETCDF mvc_data_EF.nc file, and the Python mvc_data_EF.py code for reading it. The nc file contains the time series from and around the Membach station, Belgium, as recorded during the July 2021 flood of the Vesdre River. Time in second since 2021-01-01 00:00:</p> <p>-Gravity [nm/s²]: from the superconducting gravimeter (after correcting for tidal and atmospheric pressure effects),</p> <p>To convert it into mm water: use factor -1/0.39 [mm/nm/s²]</p> <p>-Seismology [counts]: from the broadband Guralp CMG-3ESP seismometer.</p> <p>Calibration factor: 838.86 [count/µm/s]</p> <p>-Rain [mm water]: data inferred from the weather radar</p> <p>-Flow [m³/s]: water entering the Eupen reservoir</p> <p>2) An ASCII file SG_C021.TSF contains the complete recording of the superconducting gravimeter GWR#C021, since 1995-10-01. The data are corrected for polar motion, tidal, and atmospheric pressure effects (local admittance of -3.3 nm/s²/hPa), and instrumental drift. This drift is determined by using 323 absolute gravity measurements performed at the Membach station by the FG5#202 gravimeter since January 1996.</p> <p>3) PDF Document "<em>Supplement 1 Other seismic spectra during high flow events</em>" providing moving window spectra of seismic recordings during other significant floods in the Vesdre Valley</p> <p>4) PDF document "<em>Supplement 2_GRACE_GLDAS_GNSS</em>" providing the observations from GNSS and GRACE, and the predictions of the GLDAS hydrological model during the July 2021 event.</p>
Software for performing source, propagation and site convolution in seismology
<p>This repository distributes the software convo.m, written for Matlab/Octave. The goal of the software is to allow students to perform the convolution between a simple seismic source model (Brune model), the propagation of S-waves in a homogeneous half-space (geometric propagation given by the inverse of the distance and anelastic contribution described by the quality factor Q(f)), and considering the site amplification effects in terms of response for a simple 1D homogeneous layer. The instrumental response of a broadband or short-period sensor is also considered. The four terms (source, propagation, site and instrumental response) and their convolution (seismogram) are evaluated in both time and frequency (amplitude spectra) domains. Source and seismograms can be displayed in displacement, velocity and acceleration. Interaction with convo.m is via a GUI and several sliders (seismic moment and stress drop for the source term; distance and quality factor model for propagation; thickness, velocity and damping of the 1D sediment layer). The software is not intended to perform complex simulations, but only to discuss with students the basics of the convolution between source, propagation and site and its impact on the characteristics of the recordings in the time and frequency domains.</p> <p>Software is distributed within the archive convo.tar. </p> <p>THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</p>
Dataset for "Seismological Expression of the Iron Spin Crossover in Ferropericlase in the Earth's Lower Mantle"
<p>The folder contains data related to the publication: Shephard, Houser, Hernlund, Trønnes, Valencia-Cardona, Wentzkovitch (2021) Seismological Expression of the Iron Spin Crossover in Ferropericlase in the Earth’s Lower Mantle. Accepted for publication in Nature Communications.</p> <p>It include numerical grids, a simple plotting script, colour maps, and images (jpg and ps files). There is a README file explaining the content and uses of the these additional data files.</p>
Dataset in "Marine Sediment Characterized by Ocean-Bottom Fiber-Optic Seismology" by Spica et al., 2020 in Geophysical Research Letters
<p>3000fullhisy: raw data to reproduce Fig. 2<br> ppsdspec.npz: all spectrogram as shown in Fig. 3a<br> AllVelMods: All velocity model shown in Fig. 3b<br> ac.out.final.npz: auto-correlation image in Fig. 3c<br> DAS11_lpf5.stack51.grd: Earthquake wavefield as shown in Fig. 3d</p>
Receiver function data from Jammu and Kashmir seismological NETwork
<p>This data constitutes Radial component P-wave receiver functions computed at Gaussian width 2.5 for teleseismic earthquakes recorded at 20 broadband stations of the Jammu and Kashmir Seismological NETwork (JAKSNET). These P-RFs are used for modeling the crustal structure of the Jammu and Kashmir Himalaya.</p>
Local earthquake coda waveform from the Jammu And Kashmir Seismological NETwork (JAKSNET)
<p>This dataset contains local earthquake coda waveform and pre-signal noise waveforms from the Jammu And Kashmir Seismological NETwork (JAKSNET), a joint endeavor between the Indian Institute of Science Education and Research Kolkata (IISER-K), Shri Mata Vaishno Devi University (SMVD) and the University of Cambridge, UK. The network was initiated in July 2013 and comprised 24 broadband seismograph systems deployed across the J&K Himalaya. A total of 696 vertical component coda waveforms, from 121 small-to-moderate local earthquakes of magnitude between 3.0 and 5.5, within the epicentral distance of 200 km are provided in this database. Pre-signal representative noise from 22 stations, which recorded these earthquakes, have been provided for computing signal-to-noise ratio for the coda signal. The waveform data is sampled at 100 samples per second (sps), are corrected for instrument response, and filtered in the frequency band of 0.02 to 30 Hz. Coda waveforms start from twice the S-wave arrival time and are of 90 s duration. This data has been used to compute the seismic coda-wave attenuation of the Jammu and Kashmir Himalaya. The manuscript is submitted for review in JGR Solid Earth and this dataset complements the manuscript. </p>
Supplemental Dataset: Seismological evidence for girdled olivine lattice-preferred orientation in oceanic lithosphere and implications for mantle deformation processes during seafloor spreading
<p>This repository contains supplementary datasets for the manuscript titled "Seismological evidence for girdled olivine lattice-preferred orientation in oceanic lithosphere and implications for mantle deformation processes during seafloor spreading", published in G-Cubed. All files are Microsoft Excel tables containing olivine fabric data.</p> <p>ds01_strain_data_ol60.xlsx: Anisotropy magnitude and fast directions for sample data shown in Figure 3 of the main text, assuming 60% olivine and 40% pyroxene (see methods for details).</p> <p>ds02_strain_data_ol100.xlsx: Anisotropy magnitude and fast directions for sample data shown in Figure 3 of the main text, assuming pure olivine.</p> <p>ds03_fabric_data_ol75.xlsx: Anisotropy fabric data shown in Figure 5 of the main text, assuming 75% olvine and 25% pyroxene (see methods for details).</p> <p>ds04_fabric_data_ol100.xlsx: Anisotropy fabric data shown in Figure 5 of the main text, assuming pure olivine.</p> <p> </p>
Merger seismology: distinguishing massive merger products from genuine single stars using asteroseismology (online data)
<h1><strong>Input and output files for "Merger seismology: distinguishing massive merger products from genuine single stars using asteroseismology" (Henneco et al. 2024b)</strong></h1> <p>This repository contains the MESA and GYRE input files required to reproduce the models used in Henneco et al. (2024b), as well as some of the output files.</p> <p><strong>[MESA version]</strong><br>MESA r12778<br>MESA SDK 20.3.2</p> <p><strong>[GYRE version]</strong><br>GYRE 7.0<br>MESA SDK 22.6.1</p> <p> </p> <h2><strong>input_files</strong></h2> <p>This directory contains the template input files for the MESA and GYRE models.</p> <h3><strong>gyre</strong></h3> <p>- gyre_nonrot_template.in: GYRE inlist for computations without rotation<br>- gyre_rot_template.in: GYRE inlist for computations, including rotation using the TAR<br>- gyre_rot_pert_template: GYRE inlist for computations including rotation using the perturbative approach</p> <h3><strong>mesa</strong></h3> <p>- <strong>genuine_single</strong>: MESA work directory for genuine single stars<br>- <strong>merger_product</strong>: MESA work directory for merger products via the fast accretion method<br>- <strong>zams_z0142_y2703.data</strong>: ZAMS models used to start all MESA computations from</p> <h2> </h2> <h2><strong>output</strong></h2> <h3><strong>mesa</strong></h3> <p>In this directory, we provide the MESA history and profile (GYRE format only) output for the MESA models used in our work.<br>The more detailed regular profile files are left out because of storage constraints, but these can be transferred upon reasonable request.</p> <p>- mXX_plus_mYY_at_rZZ: XX + YY Msol merger product model where the fast accretion method was invoked when the HG star had a radius of ZZ Rsol<br>- mXX: genuine single-star model of XX Msol</p> <h3><strong>gyre</strong></h3> <p>This directory contains the GYRE summary files and input files (with the frequency ranges specific to these models). The detail files are left out because of storage constraints, but these can be transferred upon reasonable request. </p> <p><strong>[suffixes]</strong><br>NAD: nonadiabatic computations<br>pmodes: computations in frequency ranges appropriate for pressure modes<br>Om20_PERT: computations including rotation (20% of critical) using the perturbative approach<br>Om20_TAR: computations including rotation (20% of critical) using the TAR</p> <p>Except for the computations in `m6.0_plus_m2.4_at_r9.0`, the GYRE computations have been made only for a specific MESA profile (the profile at the time when the models were seismically compared).<br>These are:</p> <p>-<strong> m6.0_plus_m2.4_at_r9.0</strong>: profile 38<br>- <strong>m7.8</strong>: profile 14<br>- <strong>m9.0_plus_m6.3_at_r10.4</strong>: profile 62<br>- <strong>m13.6</strong>: profile 16</p> <p>For the `m6.0_plus_m2.4_at_r9.0` model, GYRE computations have been made for profiles 27 -- 52 (see Section 4.2).</p>
Western Peloponnese Seismological Data Acquisition - July 2016 to May 2017
<p><strong>2016_Data_Temporary_Network:</strong><br> Station Type: <em>3-Components Short Period</em><br> Format: <em>MSEED </em><br> Location: <em>Western Peloponnese</em><br> Deployment Time: <em>July to December 2016</em></p> <p><strong>2017_Data_Temporary_Network:</strong><br> Station Type: <em>3-Components Short Period</em><br> Format: <em>MSEED </em><br> Location: <em>Western Peloponnese</em><br> Deployment Time: <em>January to May 2017</em></p> <p><strong>Stations_Positions_DDMM: </strong>Stations Locations in Degree Minute format.<br> <br> <strong>Resp_Files_Temporary_Network:</strong> Response files for all stations 3 Components</p>
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>
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>
China Seismological Reference Model (CSRM) - Models and Datasets
<p><strong>Reference Models:</strong></p> <ul> <li>CSRM-1.0<strong> </strong>[<a href="https://doi.org/10.5281/zenodo.11098135">Link</a>]</li> </ul> <p><strong>Pre-CSRM and CSRM-derived models:</strong></p> <ul> <li>Seismic model of the shallow crust in continental China [<a href="https://doi.org/10.5281/zenodo.8099245">Link</a>]</li> <li>An uppermost mantle seismic Pn-velocity in continental China [<a href="https://doi.org/10.5281/zenodo.8112759">Link</a>]</li> <li>Crustal average <em>Vp</em>/<em>Vs</em> model in continental China [<a href="../records/12188026">Link</a>]</li> <li>A composition model of crust beneath continental China [<a href="https://doi.org/10.5281/zenodo.11107654">Link</a>]</li> </ul> <p><strong>Database (Level 2):</strong></p> <p>Level 2 database contains processed seismic data and seismic constraints used for the construction of the "China Seismological Reference Model". The Level 2 data are for personal use only. They should not be redistributed and should not be used for any commercial purposes.</p> <ul> <li>P-wave polarization angle [<a href="https://doi.org/10.5281/zenodo.8099308">Link</a>]</li> <li>Short-period Rayleigh wave ellipticity (4 - 8 s) [<a href="https://doi.org/10.5281/zenodo.8101105">Link</a>]</li> <li>Stacked receiver function [<a href="https://doi.org/10.5281/zenodo.8101006">Link</a>]</li> <li>Pn travel time [<a href="https://doi.org/10.5281/zenodo.8112291">Link</a>]</li> <li>Pn travel time in the crust beneath station [<a href="https://doi.org/10.5281/zenodo.8121110">Link</a>]</li> <li>Pn travel time in the crust beneath seismic event [<a href="https://doi.org/10.5281/zenodo.8269721">Link</a>]</li> <li>Epicenter location of event initiation point [<a href="https://doi.org/10.5281/zenodo.8121146">Link</a>]</li> <li>Long-period Rayleigh wave ellipticity (20 - 80 s) [<a href="https://doi.org/10.5281/zenodo.11098003">Link</a>]</li> <li>Isotropic receiver function (2 - 12 s) [<a href="https://doi.org/10.5281/zenodo.11096746">Link</a>]</li> <li>Inter-station empirical Green's functions [<a href="https://doi.org/10.5281/zenodo.13764584">Link</a>]</li> <li>Event-station Rayleigh wave phase and group velocity dispersion curves [<a href="https://doi.org/10.5281/zenodo.11107377">Link</a>]</li> <li>Rayleigh wave phase/group velocity maps (8 - 70 s) [<a href="https://doi.org/10.5281/zenodo.11107525">Link</a>]</li> <li>Raw receiver functions (0 - 35 s) [<a href="../records/12179171">Link</a>]</li> <li>Seismometer orientation measurements [<a href="https://zenodo.org/records/14170435">Link</a>]</li> </ul> <p><strong>Database (Level 1) [<a href="http://chinageorefmodel.org/wp-content/uploads/2017/04/%E4%B8%AD%E5%9B%BD%E5%8C%BA%E5%9F%9F%E5%9C%B0%E9%9C%87%E5%AD%A6%E5%8F%82%E8%80%83%E6%A8%A1%E5%9E%8B%E8%B5%84%E6%96%99%E7%AE%A1%E7%90%86%E8%A7%84%E5%88%99.pdf">Policy of data usage</a>]</strong></p> <p>Level 1 database contains the original seismic data that Level 2 datasets were derived from.</p> <ul> <li>P-wave polarization angle <a href="https://www.cenc.ac.cn/">[Link</a>]</li> <li>Short-period Rayleigh wave ellipticity (4 - 8 s) [<a href="https://www.cenc.ac.cn/">Link</a>]</li> <li>Stacked receiver function [<a href="https://www.cenc.ac.cn/">Link</a>]</li> <li>Pn travel time [<a href="https://www.cenc.ac.cn/">Link</a>]</li> <li>Pn travel time in the crust beneath station [<a href="https://www.cenc.ac.cn/">Link</a>]</li> <li>Pn travel time in the crust beneath seismic event [<a href="https://www.cenc.ac.cn/">Link</a>]</li> <li>Epicenter location of event initiation point [<a href="https://www.cenc.ac.cn/">Link</a>]</li> <li>Long-period Rayleigh wave ellipticity (20 - 80 s) [<a href="https://www.cenc.ac.cn/">Link</a>]</li> <li>Isotropic receiver function (2 - 12 s) [<a href="https://www.cenc.ac.cn/">Link</a>]</li> <li>Inter-station empirical Green's functions [<a href="https://www.cenc.ac.cn/">Link</a>]</li> <li>Event-station Rayleigh wave phase and group velocity dispersion curves [<a href="https://www.cenc.ac.cn/">Link</a>]</li> <li>Rayleigh wave phase/group velocity maps (8 - 70 s) [<a href="https://www.cenc.ac.cn/">Link</a>]</li> <li>Raw receiver functions (0 - 35 s) [<a href="https://www.cenc.ac.cn/">Link</a>]</li> <li>Seismometer orientation measurements [<a href="https://www.cenc.ac.cn/">Link</a>]</li> </ul> <p><strong>Citation of the Level 2 database:</strong></p> <p>Wen, L. X., and Yu, S. (2023). The China Seismological Reference Model project. <em>Earth Planet. Phys.</em>, <em>7</em>(5), 521–532. doi: <a href="http://dx.doi.org/10.26464/epp2023078" target="_blank" rel="noopener">10.26464/epp2023078</a></p> <p>_______________________________________________________________________________________________________________________</p> <p><strong>参考模型:</strong></p> <ul> <li>CSRM-1.0<strong> </strong>[<a href="https://doi.org/10.5281/zenodo.11098135">链接</a>]</li> </ul> <p><strong>预构建模型和导出模型:</strong></p> <ul> <li>中国大陆浅层地壳地震学模型 [<a href="https://doi.org/10.5281/zenodo.8099245">链接</a>]</li> <li>中国大陆上地幔顶部 Pn 速度模型 [<a href="https://doi.org/10.5281/zenodo.8112759">链接</a>]</li> <li>中国大陆地壳平均 <em>Vp</em>/<em>Vs</em> 模型 [<a href="../records/12188026">链接</a>]</li> <li>中国大陆地壳成分模型 [<a href="https://doi.org/10.5281/zenodo.11107654">链接</a>]</li> </ul> <p><strong>Level 2 数据库:</strong></p> <p>Level 2 数据库包含经过处理的地震数据和用于构建 “中国区域地震学参考模型” 的地震学约束。Level 2 数据库仅限于个人使用,不得分发和用于任何商业目的。</p> <ul> <li>P 波偏振角度 [<a href="https://doi.org/10.5281/zenodo.8099308">链接</a>]</li> <li>短周期瑞利波椭率 (4 - 8 s) [<a href="https://doi.org/10.5281/zenodo.8101105">链接</a>]</li> <li>叠加接收函数 [<a href="https://doi.org/10.5281/zenodo.8101006">链接</a>]</li> <li>Pn 走时 [<a href="https://doi.org/10.5281/zenodo.8112291">链接</a>]</li> <li>台站下方壳内 Pn 波走时 [<a href="https://doi.org/10.5281/zenodo.8121110">链接</a>]</li> <li>震源下方壳内 Pn 波走时 [<a href="http://doi.org/10.5281/zenodo.8269721">链接</a>]</li> <li>地震起破点水平位置 [<a href="https://doi.org/10.5281/zenodo.8121146">链接</a>]</li> <li>长周期瑞利波椭率 (20 - 80 s) [<a href="https://doi.org/10.5281/zenodo.11098003">链接</a>]</li> <li>各向同性接收函数 (2 - 12 s) [<a href="https://doi.org/10.5281/zenodo.11096746">链接</a>]</li> <li>台站间经验格林函数 [<a href="https://doi.org/10.5281/zenodo.13764584">链接</a>]</li> <li>事件-台站间瑞利波相/群速度频散曲线 [<a href="https://doi.org/10.5281/zenodo.11107377">链接</a>]</li> <li>瑞利波相/群速度图 (8 - 70 s) [<a href="https://doi.org/10.5281/zenodo.11107525">链接</a>]</li> <li>原始接收函数 (0 - 35 s) [<a href="../records/12179171">链接</a>]</li> <li>地震计方位测量 [<a href="https://zenodo.org/records/14170435">链接</a>]</li> </ul> <p><strong>Level 1 数据库 [<a href="http://chinageorefmodel.org/wp-content/uploads/2017/04/%E4%B8%AD%E5%9B%BD%E5%8C%BA%E5%9F%9F%E5%9C%B0%E9%9C%87%E5%AD%A6%E5%8F%82%E8%80%83%E6%A8%A1%E5%9E%8B%E8%B5%84%E6%96%99%E7%AE%A1%E7%90%86%E8%A7%84%E5%88%99.pdf">数据使用政策</a>]</strong></p> <p>Level 1 数据库包含 Level 2 数据库中用到的原始地震数据。</p> <ul> <li>P 波偏振角度 [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>短周期瑞利波椭率 (4 - 8 s) [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>叠加接收函数 [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>Pn 走时 [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>台站下方壳内 Pn 波走时 [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>震源下方壳内 Pn 波走时 [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>地震起破点水平位置 [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>长周期瑞利波椭率 (20 - 80 s) [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>各向同性接收函数 (2 - 12 s) [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>台站间经验格林函数 [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>事件-台站间瑞利波相/群速度频散曲线 [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>瑞利波相/群速度图 (8 - 70 s) [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>原始接收函数 (0 - 35 s) [<a href="https://www.cenc.ac.cn/">链接</a>]</li> <li>地震计方位测量 [<a href="https://www.cenc.ac.cn/">链接</a>]</li> </ul> <p><strong>Level 2 数据库引用:</strong></p> <p>Wen, L. X., and Yu, S. (2023). The China Seismological Reference Model project. <em>Earth Planet. Phys.</em>, <em>7</em>(5), 521–532. doi: <a href="http://dx.doi.org/10.26464/epp2023078" target="_blank" rel="noopener">10.26464/epp2023078</a></p>
Matrix Profile of Seismological Data Repository
<p>The Computed Matrix Profile (MP) repository for seismic data.</p> <p>Please see below the archived manuscript of Shakibay Senobari et al., (Submitted to JGR, under review) for more information regarding the Matrix Profile and data set:</p> <p>essopenarchive.org/604953/p1vlDMs-5b0zsrs7C033Ug</p> <p>https://doi.org/10.1002/essoar.10512525.1</p>
Jammu and Kashmir Seismological NETwork (JAKSNET) earthquake catalogue (2014-2017)
Open the record for dataset details and reuse information.
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