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7 results for “moment tensor inversion”
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>The dataset includes waveform data for centroid moment tensor solutions inferred using Hamiltonian Monte Carlo and a 3-D Earth model in the Japanese islands. The data are provided as Green's strains at the maximum-likelihood location (indicated in the title of each text file) for all study events inverted at different periods. Inversion period is also indicated in the title. All the data are filtered between 15 s and 80 s. Additionally we provide a Python code to obtain displacement from strains given a moment tensor.</p>
List of VLFEs obtained in the paper "Influence of a subducted oceanic ridge on the distribution of shallow VLFEs in the Nankai Trough as revealed by moment tensor inversion and cluster analysis"
<p>List of VLFEs obtained in Toh et al., (2020, GRL).</p> <p>"Influence of a subducted oceanic ridge on the distribution of shallow VLFEs in the Nankai Trough as revealed by moment tensor inversion and cluster analysis" by Akiko Toh, Wan-Jou Chen, Nozomu Takeuchi, Douglas Dreger, Wu-Cheng Chi, and Satoshi Ide. </p> <p> </p>
Datasets and Codes for "Relative Moment Tensor Inversion for Microseismicity: Application to Clustered Earthquakes in the Cascadia Forearc"
<p>This Zenodo record contains the supplementary datasets and code for the paper titled "Relative Moment Tensor Inversion for Microseismicity: Application to Clustered Earthquakes in the Cascadia Forearc."<br><br></p> <p><strong>Datasets</strong></p> <ul> <li>phases.txt<br> Contains phases used for the event location and moment tensor inversion.<br> Format: ID, station, phase, year, month, day, secday<br> ID: Event identifier (same for all files)<br> station: Station name<br> phase: 1 for P-wave or 2 for S-wave<br> secday: Seconds in the day</li> <li>polarity.txt<br> Contains first motion P polarity used in the study.<br> Format: ID, station, polarity, trust, type<br> polarity: 1 for up or -1 for down<br> trust: Value between 0 and 1, indicating confidence level.<br> type: E for emergent or I for impulsive<br> Note that the arrival type has been automatically assigned and not double-checked.</li> <li>relocation.txt<br> HypoDD relocation file (see hypoDD manual for full description).<br> Format: ID, LAT, LON, DEPTH, X, Y, Z, EX, EY, EZ, YR, MO, DY, HR, MI, SC, MAG, NCCP, NCCS, NCTP, NCTS, RCC, RCT, CID</li> <li>MT_soluton.txt<br> Contains all double-couple moment tensor solutions.<br> Format: ID, strike, dip, rake, mag, kagan_std<br> mag: Moment magnitude (Mw); "None" if the event is not considered stable<br> kagan_std: Quality interpretation of the moment tensors using Kagan angle standard deviation, as described in the main paper.</li> </ul> <p> </p> <p><strong>Codes</strong></p> <p>Future development of the relative moment tensor algorithm will be conducted on GitHub as part of the Marie-Sklodowska-Curie Action relMT funded by the European Union (https://github.com/wasjabloch/relMT)</p> <p>Here are the files in Codes.zip:</p> <ul> <li>synthetics.zip<br> Contains codes for performing and testing synthetic moment tensor inversion.</li> <li>relMT.zip<br> Contains the code for performing moment tensor inversion on real data.</li> <li>intrustion.txt<br> Contains instructions for setting up and running the codes.</li> <li>environment_MAC.yml<br> File to create the python environment on a MAC or LINUX machine.</li> <li>environment_WINDOWS.yml<br> File to create the python environment on a WINDOWS machine.</li> </ul>
Moment tensor inversion and uncertainty analysis for 40 Uttarakhand Earthquakes (2010-2022)
<p>This repository provides detailed descriptions of the files that were used for the Moment tensor and uncertainty analysis study of earthquakes in the Uttarakhand Himalayas. These files contain the Moment Tensor (MT) estimation results and uncertainty quantification of 40 earthquakes using different networks.</p> <p> </p> <p><strong>Contents:</strong></p> <p>1. waveform_fits.docx - Waveform fits for MT estimation</p> <p>2. confidence_plots.docx - The confidence parameters associated with each MT</p> <p>3. depth_vs_misfit_plot.docx - The confidence in the MT solution for each depth against the misfit values</p> <p>4. weight_files.zip - Weight files for 40 events read by the MTUQ package</p> <p>5. CMT_solution_files.zip - Centroid Moment Tensor (CMT) solutions for 40 events</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>
Grond reports for the seismic moment tensor inversions done for "The January 2022 Hunga Volcano explosive eruption from the multi-technological perspective of CTBT monitoring"
<p>This are the Grond reports of the seismic moment tensor inversion done for the manuscript submitted to GJI titled:</p> <p>"The January 2022 Hunga Volcano explosive eruption from the multi-technological perspective of CTBT monitoring"</p> <p>You can view the summary figures of the inversions in the subfolders for each event manually if you wish.</p> <p>However to view the reports interactively you need to have the pyrocko and grond softwares installed. See here for installation instruction for pyrocko: https://pyrocko.org/ and here for grond https://pyrocko.org/grond/docs/current/</p> <p>After correct installation you can view the reports in any browser by executing the command "grond report --so" in the folder which contains the unpacked "report" folder.</p>
High-rate GNSS data in seismic moment tensor inversion. The study of anthropogenic earthquakes - GNSS displacement time series
<p>The dataset of GNSS displacement time series with duration of 30 seconds before and 90 seconds after the origin time of the mining tremors. The dataset was used in the research on High-rate GNSS data in seismic moment tensor inversion. The study of anthropogenic earthquakes.</p> <p>Further description of the HR-GNSS processing can be found in the paper by Kudlacik et al. (2021) and in the research paper "High-rate GNSS data in seismic moment tensor inversion. The study of anthropogenic earthquakes".</p>
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