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372 results for “Waveforms”
Seismicity patterns and multi-scale imaging of Krafla (N-E) Iceland with local earthquake tomography: Raw event waveforms for all events used in the inversion and manual picks for temporary network
<p>This Data and Software were used in the submitted paper "Seismicity patterns and multi-scale imaging at Krafla (N-E Iceland) wih local earthquake tomography" by Glück et al.<br>The data and software provided here are used to compute the velocity models with TomoTV.<br>The raw data (.mseed format) can be visualised with the Python package Pyrocko/Snuffler, which was also used for the arrival time picking.<br>For the temporary network the manual picks are provided along with the code to prepare the manual picks as the input files for a localisation with NonLinLoc by weighting and quality checking the data. This resulting localsitations and the weighted traveltimes are then used for the LET.<br>The same workflow was used for the picks from the permanent network.</p> <p>Data:<br>- Raw data (\WaveformsPermanentStations): 7s waveform snippets of the events listed in the ISOR catalogue on http://lv.isor.is:8080/events/browse/ for the years 2021 and 2022.<br>- Raw data (\WaveformsNodes): 5s waveform snippets of the events listed in the ISOR catalogue on http://lv.isor.is:8080/events/browse/2022 recorded with the temporary network of 98 temporary nodes in June and July 2022.<br>- Pickfile (ManualPicks_100Nodes_Kafla2022.txt): Manual picks of the events listed in the ISOR catalogue for the evenst recorded with the temporary network.<br>- Station file (Station_file.txt): The station file includes the coordinates (Lat, Lon, Elevation) of the permanent stations (StationID starting with K...) and of the temporary nodes (StationID starting with N...).</p> <p>Software (Hyp_format.py):<br>- Weighting: The picks are weighted according to their Signal-to-Noise ratio (described in more detail in Section 2.3 in the main text of the paper)<br>- Writing the inputfile for NonLinLoc (with the selecting the mode option "PorS" in line 118), including all picks, also for those stations where not both phases were picked. The file "endfile.txt" is needed to write the picks to the NonLinLoc input format.<br>- Quality check of the picks: Computing a modified Wadati diagram from the traveltime differences of P and S phases for all the events available (with the selecting the mode option "PandS" in line 118)<br>- Python packages needed: numpy, scipy, matplotlib, pandas, obspy</p>
Data sets for 'Mechanical Compliance of Individual Fractures in a Heterogeneous Rock Mass from Production-type Full-waveform Sonic Data', submitted to JGR: Solid Earth
<p>Synthetic and field FWS data sets are supplied for validation of proposed methods for compliance estimation of individual fractures. Each zip contains ‘ReadMe.txt ‘, which illustrates the files and corresponding parameters. More details about the setup in the submitted paper ‘Mechanical Compliance of Individual Fractures in a Heterogeneous Rock Mass from Production-type Full-waveform Sonic Data’.</p>
In-vitro dataset for classification and regression of stenosis: dependence on heart rate, waveform and location
<p><strong>Background</strong></p> <p>This data supplements the paper "Classification and regression of stenosis using an in-vitro pulse wave dataset:<br> dependence on heart rate, waveform and location". It was created at Technische Hochschule Mittelhessen (THM) in Germany and uploaded to Zenodo. Please cite the paper (<a href="https://doi.org/10.1016/j.compbiomed.2022.106224">https://doi.org/10.1016/j.compbiomed.2022.106224</a>) and the Zenodo doi when using this dataset.</p> <p><strong>General description / Dataset structure</strong></p> <p>Each mat-File describes a different measurement (details can be found in the paper). There are 17 pressure signals for different positions, one flow sensor close to the stenosis location and one monitor signal of the proportional valve use to control the input curve. Total duration of each signal is 60s with a sampling rate of 1000 Hz. Each mat-file contains a header structure with metadata and struct array for signals of each sensor. Signals in each mat-File are aligned with respect to a common time axis, but this is not guaranteed between different measurements/files. We did our best to make the beginnings end endings align as close as possible (by removing buffer artefacts and aligning the input signal of the monitor), however algorithms should not rely on a global time axis. This similar to patient measurements without an ekg, this does also not share a global time axis comparable among patients.</p> <p>The file format can either be loaded directly in Matlab or in Python with scipy's loadmat function.</p> <p>The data is structure first by stenosis "state" (or location) then by heart rate and then by heart waveform. The stenosis "states" can devided in 1 subset of 10 folders created for regression and 6 created for classification. Excerpt of the folder structure:</p> <ul> <li>No Stenosis <ul> <li>HR 50 <ul> <li>WaveForm1.mat</li> <li>WaveForm2.mat</li> <li>...</li> </ul> </li> <li>HR 55 <ul> <li>...</li> </ul> </li> <li>...</li> </ul> </li> <li>Regression - Stenosis at Pos01 <ul> <li>HR 50 <ul> <li>...</li> </ul> </li> <li>...</li> </ul> </li> <li>...</li> </ul> <p>The tools also available at this page help with traversing this folder structure and are available for Python and Matlab.</p> <p><strong>Data Fields of each file</strong></p> <table> <caption>headerStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>id</td> <td>internal database id</td> </tr> <tr> <td>name</td> <td>stenosis location</td> </tr> <tr> <td>rate</td> <td>sampling rate in Hz</td> </tr> <tr> <td>description</td> <td>definition of automatic parameter sweep range</td> </tr> <tr> <td>configuration</td> <td>concrete parameters of the trapezoidal input curve (offset and amplitude in mmHg, ascend times and descend times and smoothing window in a fraction the time period (1.2s))</td> </tr> </tbody> </table> <p> </p> <table> <caption>signalStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>nodeId</td> <td>corresponds to numbered nodes at which the sensor is placed, the corresponding location can be found in the technical paper describing the MACSim simulator (node numbering, not sensor numbers) or in the software SISCA in the example database.</td> </tr> <tr> <td>type</td> <td>'p' ... pressure or 'q' ... flow</td> </tr> <tr> <td>data</td> <td>double array, time series of each sensor, unit mmHg for type 'p' and ml/s for type 'q'</td> </tr> <tr> <td>anatomicalPosition</td> <td>name of the corresponding anatomical position</td> </tr> </tbody> </table> <p><strong>Tools:</strong></p> <p>This Tools should make it easier to load the dataset. The usage is documented in the respective code files.</p> <p>Code for the publication is available here:<br> https://gitlab.com/agbernhard.lse.thm/publication_macsim_machinelearning<br> </p> <p> </p> <p> </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>
Full-waveform pulsed LiDAR dataset
<p>A dataset comprised of full-waveform (periodically sampled) pulses emulating LiDAR signals is provided. The waveforms were captured at a sampling rate of 20 Gsample/s and over a dynamic range of 45 dB. A simple Python notebook is also given, showing how to properly load the waveforms and capture parameters from the stored .mat file.</p> <p>The dataset was used to demonstrate a new time-frequency estimation method for pulsed LiDAR systems [1].</p> <p>The dataset was also used to build efficient machine learning (ML) models capable of accurate and precise time-of-flight estimations [2].</p> <p>For full details, please check the Experimental Setup in [1].</p> <p>Please contact Daniel Bastos (d.bastos@ua.pt) for any further questions.</p> <p>References:</p> <p>[1] – D. Bastos, A. Brandão, A. Lorences-Riesgo, P. P. Monteiro, A. S. R. Oliveira, D. Pereira, H. Z. Olyaei and M. V. Drummond, , "Time-Frequency Range Estimation Method for Pulsed LiDAR," in <em>IEEE Transactions on Vehicular Technology</em>, vol. 72, no. 2, pp. 1429-1437, Feb. 2023, doi: 10.1109/TVT.2022.3207588.</p> <p>[2] – Daniel Bastos, Bruno Faria, Paulo P. Monteiro, Arnaldo S. R. Oliveira, and Miguel V. Drummond, "Machine learning-aided LiDAR range estimation," Opt. Lett. <strong>48</strong>, 1962-1965 (2023), doi: 10.1364/OL.487000.</p>
Enhancing Full Waveform Inversion of Field GPR Data: A Source-Independent Approach with Dynamic Reference Selection via SE-Wave-U-Net
<p>Data presented in the manuscript titled 'Enhancing Full Waveform Inversion of Field GPR Data: A Source-Independent Approach with Dynamic Reference Selection via SE-Wave-U-Net'</p>
numerical data to accompany "Strong asymmetry in near-fault ground velocity during an oblique strike-slip earthquake revealed by waveform particle motions and dynamic rupture simulations"
<p>This is the numerical data to accompany the paper "Strong asymmetry in near-fault ground velocity during an oblique strike-slip earthquake revealed by waveform particle motions and dynamic rupture simulations". Please refer to the README.txt file for information about the individual datasets and archive files. </p>
Dataset and Model Files for Full Waveform Inversion Seismic Earth Model WUS324
<p><strong>Dataset and Model Files for Full Waveform Inversion Seismic Earth Model WUS324</strong></p> <p> </p> <p>Arthur Rodgers</p> <p><em>Geophysical Monitoring Program, Lawrence Livermore National Laboratory, Livermore CA 94551, USA</em>; and</p> <p><em>Department of Earth Sciences, Eidgenössische Technische Hochschule Zürich, Zürich, Switzerland</em></p> <p> </p> <p>rodgers7@llnl.gov</p> <p> </p> <p>10.5281/zenodo.11619519</p> <p> </p> <p><strong>Summary</strong></p> <p>This data set includes the metadata and model for the three-dimensional (3D) seismic Earth model WUS324 (Rodgers et al., 2024). This model describes seismic wavespeeds, density and attenuation for the 3D volume spanning the surface to 400 km depth, latitudes from Mexico to Canada (28 to 52) and longitudes from the Pacific Ocean to the Great Plains (-132 to -100). The metadata tabulates the earthquakes and seismic networks and stations used in the creation and validation of 3D seismic Earth model WUS324.</p> <p> </p> <p>The WUS324 model is provided in NetCDF format (readable by for example, <em>xarray</em>, Hoyer & Hamman, <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0057">2017</a>) and HDF5 format for viewing with <em>ParaView</em> (Ahrens et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0002">2005</a>) and interaction with <em>Salvus</em> (Afanasiev et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0001">2019</a>).</p> <p><strong> </strong></p> <p><strong>References</strong></p> <p>Afanasiev, M, C Boehm, M van Driel, L Krischer, M Rietmann, DA May, MG Knepley, and A Fichtner (2019). Modular and flexible spectral-element waveform modelling in two and three dimensions, Geophys. J. Int., 216(3), 1675–1692, doi: 10.1093/gji/ggy469</p> <p>Ahrens, J., Geveci, B., & Law, C. (2005). Paraview: An end-user tool for large data visualization. The Visualization Handbook, 717(8). https://doi.org/10.1016/b978-012387582-2/50038-1</p> <p>Hoyer, S., & Hamman, J. (2017). Xarray: N-D labeled arrays and datasets in Python. Journal of Open Research Software, 5(1). https://doi.org/10.5334/jors.148</p> <p>Rodgers, A., C. Doody and A. Fichtner (2024). WUS324: Converged Full Waveform Inversion Improves Waveform Fits While Imaging Crustal and Upper Mantle Structure in the Western United States, manuscript in preparation.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>This work was initiated under Laboratory Directed Research and Development project 20-ERD-008 at Lawrence Livermore National Laboratory (LLNL) and continued with support from the National Nuclear Security Administration Ground-based Nuclear Detonation Detection program. AR is grateful to the Eidgenössische Technische Hochschule, Zürich for support as an Academic Guest and to LLNL for Professional Research and Teaching Leave. This work was performed under the auspices of the U.S. Department of Energy by LLNL under Contract DE-AC52-07NA27344. LLNL-MI-865269.</p> <p> </p> <p> </p> <p>Files contained in this data set.</p> <table> <tbody> <tr> <td> <p><strong>Filename</strong></p> </td> <td> <p><strong>Description </strong></p> </td> </tr> <tr> <td> <p>WUS324_all_events_project.csv</p> </td> <td> <p>Table of all 216 events considered during the creation of WUS324. This table includes the origin date and time, location and moment tensor parameters.</p> </td> </tr> <tr> <td> <p>WUS324_all_networks.txt</p> </td> <td> <p>Table of all seismic networks and that contributed to WUS324.</p> </td> </tr> <tr> <td> <p>WUS324_inversion_events.txt</p> </td> <td> <p>Table of the 126 event names used in the inversions that created WUS324.</p> </td> </tr> <tr> <td> <p>WUS324_inversion_all_paths.csv</p> </td> <td> <p>Table of all event-station paths that contributed to the creation of WUS324.</p> </td> </tr> <tr> <td> <p>WUS324_validation_events.txt</p> </td> <td> <p>Table of the 65 event names used in the validation of WUS324.</p> </td> </tr> <tr> <td> <p>WUS324_validation_all_paths.csv</p> </td> <td> <p>Table of all event-station paths that contributed to the validation of WUS324.</p> </td> </tr> <tr> <td> <p>WUS324_16sec.h5</p> </td> <td> <p>WUS324 model for simulating waveforms with minimum period of 16 seconds in Salvus HDF5 format.</p> </td> </tr> <tr> <td> <p>WUS324_16sec.xdmf</p> </td> <td> <pre>Auxiliary file for WUS324_16sec.h5, used to import model into Paraview.</pre> </td> </tr> <tr> <td> <p>WUS324.nc</p> </td> <td> <pre>WUS324 model in netCDF format following the metadata standards of the Incorporated Research Institutions for Seismology Earth Model Collaboratory</pre> </td> </tr> </tbody> </table>
Trained Random Forest Model for PNW Seismic Event Classification Trained on 150s waveforms (P-50, P+100), 50 Hz, and 1-10 Hz BP Filtered
<p>This dataset contains three trained random forest models named as following - </p> <ul> <li>P_10_100_F_1_10_50.joblib - This is a model trained on 110s long waveforms (origin time - 10, origin time +100) in case of earthquakes and explosions and (first arrival pick -10, first arrival pick + 100) in case of surface events, the waveforms are tapered using 10% cosine taper, bandpass filtered between 1-10 Hz using Butterworth four corner filter, normalized and resampled to 50 Hz. </li> <li>P_50_100_F_1_10_50.joblib </li> <li>P_10_30_F_1_15_50.joblib. </li> </ul> <p>And also the standard scaler parameters for each features that will be used to normalize them. </p>
Stacked multiband receiver function waveforms for global study of mantle transition zone discontinuities using dense arrays
<p>Please see the readme for a full explanation. This dataset includes the following 1) the nine preferred parameters and their uncertainties 2) the group and stacked observed and best modeled receiver function waveforms and 3) a list of the network codes and stations. </p>
Physical and Tsfel Features for 40s Waveforms (Updated 15 July, 2024)
<p>Physical and Tsfel Features for four classes of events that dominate the seismicity in the pacific northwest. Before extracting the features, following processing was applied - </p> <ul> <li>trimming (first arrival - 10, first arrival +100)</li> <li>detrending</li> <li>resampling to 100 Hz</li> <li>cosine taper (10%)</li> <li>bandpass filtered (1-10 Hz)</li> <li>normalizing by maximum</li> <li>resampling to 50 Hz</li> </ul>
Physical and Tsfel Features for 150s waveforms (Updated 15 July, 2024)
<p>Physical and Tsfel Features for four classes of events that dominate the seismicity in the pacific northwest. Before extracting the features, following processing was applied - </p> <ul> <li>trimming (first arrival - 50, first arrival +100)</li> <li>detrending</li> <li>resampling to 100 Hz</li> <li>cosine taper (10%)</li> <li>bandpass filtered (1-10 Hz)</li> <li>normalizing by maximum</li> <li>resampling to 50 Hz</li> </ul> <p> </p>
Cross-correlations of Days before the strike: precursory waveform decoherence preceding major strike-slip earthquakes
<p>Repository of </p> <h1>Days before the strike: precursory waveform decoherence preceding major strike-slip earthquakes</h1> <p>This dataset contains the cross-correlation functions of station pairs used for Ridgecrest, CA, Turkiye (EAFZ), and Aegean Sea (NAT). The dataset is compressed in a tar.gz. To decompress, use tar -xzvf filename.tar.gz. Download and decompress the cross-correlations dataset named WF_DEC_DAT_CC.tar.gz.</p> <h2>Description of the data and file structure</h2> <p>The dataset is organized by study area as follows: ZONE (Aegean, Ridgecrest, Turkey) with sub-directories CC (cross-correlation functions).</p> <p>Sub-directories CC contain sub-directories named according to station pairs (e.g., XXX_YYY) storing cross-correlation functions in numpy arrays organized by stacking (5, 10, and 20 days) and different frequency bands, adapted to use in the Ambient-Noise Seismology Package NoisePy <a href="https://github.com/noisepy/NoisePy">https://github.com/noisepy/NoisePy</a> (Jiang and Denolle, 2020).</p> <p>Data was derived from the following sources:</p> <ul> <li> <p>The raw data can be downloaded from <a href="https://ds.iris.edu/mda/CI/">https://ds.iris.edu/mda/CI/</a> (for Ridgecrest dataset) and <a href="https://www.orfeus-eu.org/data/">https://www.orfeus-eu.org/data/</a> for (Turkey and Aegean Sea datasets). The cross-correlation functions computed using MSNoise <a href="http://msnoise.org/doc/index.html">http://msnoise.org/doc/index.html/</a> Lecocq et al (2014).</p> </li> </ul> <h2>Code/Software</h2> <p>To reproduce the results and conclusion of the manuscript, use the Jupyter notebooks available at <a href="https://github.com/fjmunozb/EQ_Precursors">https://github.com/fjmunozb/EQ_Precursors</a>. Find also python environments to install MSNoise and NoisePy.</p>
Stretching results of Days before the strike: precursory waveform decoherence preceding major strike-slip earthquakes
<p>Repository of </p> <h1>Days before the strike: precursory waveform decoherence preceding major strike-slip earthquakes. Stretching results</h1> <p>This dataset contains the stretching results of cross-correlation functions using station pairs for Ridgecrest, CA, Turkiye (EAFZ), and Aegean Sea (NAT). The dataset is compressed in a tar.xz. To decompress, use tar -xvJf filename.tar.xz. Download and decompress the cross-correlations dataset named WF_DEC_DATA.tar.xz.</p> <h2>Description of the data and file structure</h2> <p>The dataset is organized by study area as follows: ZONE (Aegean, Ridgecrest, Turkey) each zone contains a directory named "stretching" (stretching results).</p> <p>Sub-directories "stretching" contain sub-directories named according to station pairs (e.g., XXX_YYY) storing *.npz files organized by stacks and frequency bands corresponding to results after applying the stretching technique. </p> <p>The directories pre_postZZ present in each main region directory (e.g., WF_DEC_DATA/RIDGECREST/stretching/CCC_TOW2/pre_postZZ), can be used to plot dv/v and waveform coherence variations before and after the mainshocks.</p> <p>To reproduce the results and conclusion of the manuscript, use the Jupyter notebooks available at https://github.com/fjmunozb/EQ_Precursors (opens in new window). Find also python environments to install MSNoise and NoisePy.</p>
Ultrasonic compressional-wave waveform dataset in siltstones, carbonates and a sandstone
<p>These data are the compressional-wave waveform data obtained by Ba et al. (2012-2018) through ultrasonic experimental measurements on the three sets of rock samples, including twelve siltstone samples, thirteen carbonates, and one tight sandstone.</p> <p>Details are given in the uploaded introduction document regarding the format of dataset.</p>
High-rate GNSS displacement waveforms for large earthquakes version 2.0
<p>This dataset accompanies <strong>A Global Database of Strong Motion Displacement GNSS Recordings and an Example Application to PGD Scaling</strong> published in <em>Seismological Research Letters</em> by Ruhl et al. (2018). The data is structured as follows:</p> <p>Once expanded the data within the archive are structured as follows: Inside the archive there is one folder per event clearly labeled with the event names in Table 1 from the main text. Inside each event folder is a text file (EVENT_disp.chan) with station metadata (station codes, coordinates, and gain values), importantly because the waveforms are provided as mini-SEED files with integer values the gain must be applied to convert to physical displacement units. There is a “disp” folder which contains files named using the convention STA.LXE.mseed, STA.LXN.mseed and STA.LXZ.mseed where “STA” is the station code and LXE, LXN, and LXZ are east, north, and up waveforms, respectively. The sampling rate for each waveform is indicated inside the miniSEED header of each waveform as well as in the corresponding metadata channel file. The data are provided in UTC time with leap seconds fully corrected for so no further processing is necessary. There is also a "_plots" folder with a plot of the three-component record section for each event.</p>
Waveforms from Mentawai 2010 Event Strong-Motion Stations
<p>Miniseed files (.mseed), and .chan file for the waveforms of the 2010 M7.8 Mentawai tsunami earthquake, used in the study of Sahakian et al. (2019). This includes one miniseed (.mseed) file per channel of a station recording the event that was used here, from beginning to end of the event. The units of the data inside each file are cm/sec<sup>2</sup>,and the time in seconds. Inside the .zip file is the .chan file, describing the station’s information (network, station, location, channel, longitude/latitude/elevation, sampling rate, gain, units). The gain is 1 (the units in the miniseed file are the same as described in the .chan file). </p> <p> </p> <p>If you use these data, please cite the associated publication:</p> <p>V.J. Sahakian, D. Melgar, M. Muzli (2019). Weak Near-Field Behavior of a Tsunami Earthquake: Towards Real-Time Identification for Local Warning, <em>Geophysical Research Letters</em>, doi:10/1029/2019GL083989.</p>
Fine Three-Dimensional VHF Lightning Mapping Using Waveform Cross-Correlation TOA Method
<p>In a manuscript entitled “Fine Three-Dimensional VHF Lightning Mapping Using Waveform Cross-Correlation TOA Method”, the lightning data obtained by an improved VHF 3-D location system were analyzed. The data supports the aforementioned manuscript and can be used freely for scientific purposes with appropriate citation.</p>
GPS, InSAR, and seismic waveform data for study of 2014 South Napa, California, earthquake
<p>GPS_Brocher_et_al2015.txt : Observed static offsets at CGPS and SGPS sites, respectively, presented by Brocher et al. (2015) determined using GPS time series up to several days after the event</p> <p>napa_CSK_20140619_20140903_asc.grd : Observed unwrapped COSMO-SkyMed ascending interferogram spanning June 19 - September 3, 2014</p> <p>napa_CSK_20140726_20140827_desc.grd : Observed unwrapped COSMO-SkyMed descending interferogram spanning July 26 - August 27, 2014</p> <p>napa_sentinel_20140807_20140831_desc.grd : Observed unwrapped Sentinel descending interferogram spanning August 7 - August 31, 2014</p> <p>seismic_waveforms.tar.gz : Three-component seismic waveforms in (time (s after origin time), velocity (m/s)) format for 16 stations bandpass filtered between 0.067 and 1.5 Hz. Filenames indicate which velocity component (East, North, or Up=Z) and station name.</p> <p>Study: "Coseismic slip and early after slip of the M6.0 August 24, 2014 South Napa, California, earthquake" by Fred F. Pollitz, Jessica R. Murray, Sarah E. Minson, Charles W. Wicks, and Jerry L. Svarc. Journal of Geophysical Research, <em>in press</em></p>
japan-seismic-waveform-synthetics
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