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35 results for “Seismology”
Receiver function data from Jammu and Kashmir seismological NETwork
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Local earthquake coda waveform from the Jammu And Kashmir Seismological NETwork (JAKSNET)
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Hales discontinuity in the southern Indian continental lithosphere: seismological and petrological models
<p>We model the shear wave velocity structure of the Hales-discontinuity beneath the Eastern Dharwar Craton and Southern Granulite Terrain in Southern India using P-wave receiver function (P-RF) analysis and joint inversion with Rayleigh wave phase velocity dispersion. For this study we use data from seismological stations HYB, GBA and KOD. We isolated P-RFs where the Hales phase is distinct and model them using joint data analysis. We also use common conversion point stack profiles constructed by depth migrating P-RFs through the velocity model and show that the Hales discontinuity is undulatory in nature. We perform petrological modeling of the Hales discontinuity and fianlly propose a geodynamic model for its evolution. </p>
Seismological observation of Earth's oscillating inner core
<p>This is the dataset for the paper 'Seismological observation of Earth’s oscillating inner core'.</p>
CSRM-1.0: A China Seismological Reference Model
<p>This high-resolution CSRM-1.0 is constructed for the top 100 km of the crust and uppermost mantle in continental China, based on the constraints of the <em>P</em>-wave polarization angle, short-period Rayleigh wave ellipticity from ambient noise, long-period Rayleigh wave ellipticity from earthquake data, receiver function, empirical Green's function, Rayleigh wave phase/group velocity dispersion curves from regional earthquakes, and <em>Pn</em>-wave travel time extracted from seismic data of 4435 seismic stations during 1990 and 2020. The CSRM-1.0 has a spatial crustal resolution of about <strong>60 km</strong> beneath the north-south seismic belt and the trans-north China orogen and about <strong>120 km</strong> beneath the rest of continental China, and a spatial mantle resolution of about <strong>300 km</strong>. The seismic constraints used for the construction of the model consist of the <strong>479,321</strong> polarization angle measurements from the P-wave waveforms of 9361 tele-seismic earthquakes, the short-period (4 - 8 s) Rayleigh wave ellipticity estimated from continuous seismic ambient noise waveforms recorded by <strong>4211</strong> seismic stations, the <strong>622,972 </strong>receiver functions calculated based on the waveforms of 9361 tele-seismic events, the <strong>639,171 </strong>inter-station empirical Green's functions from seismic ambient noise, the long-period (20 - 80 s) Rayleigh wave ellipticity estimated from earthquake waveform data of <strong>4193</strong> seismic stations, the <strong>54,792 </strong>event-staion Rayleigh wave dispersion curves from regional earthquakes, and the <strong>50,867 </strong><em>Pn</em>-wave travel time data. Please click on <a href="https://doi.org/10.5281/zenodo.8103561"><strong>link</strong></a> for the datasets. Click <a href="https://doi.org/10.1029/2024JB029520"><strong>link</strong></a> for the paper. Please click <a href="http://chinageorefmodel.org/csrm-visualizers/csrm-1-0-visualizer">link</a> for online visualization of the CSRM-1.0.</p> <p>高分辨率的 CSRM-1.0 是中国大陆地区地壳和上地幔顶部 100 公里的三维地震学模型。构建该模型的地震学约束提取自 1990 至2020 年期间 <strong>4435 </strong>个台站记录的地震数据,包括远震 <em>P </em>波极化角度,从地震背景噪声提取的短周期瑞利波椭率,远震接收函数,从地震背景噪声提取的经验格林函数,从区域地震提取的瑞利波相/群速度频散曲线,以及 <em>Pn</em> 波走时。CSRM-1.0 模型在南北地震带和华北造山带的地壳中的空间分辨率约为 <strong>60 km </strong>而在中国大陆其他地区的地壳中分辨率约为 <strong>120 km</strong>,并且其在中国大陆上地幔顶部的分辨率约为 <strong>300 km</strong>。用于构建模型的地震学约束包括利用 9361 个远震事件 P 波波形测量得到的 <strong>479,321</strong> 个极化角度测量值,利用 <strong>4211</strong> 个地震台站连续地震背景噪声波形数据测量的短周期(4-8 秒)瑞利波椭率,基于 9361个远震事件波形计算的 <strong>622,972 </strong>条接收函数,利用地震背景噪声数据中计算的 <strong>639,171 </strong>条台站间经验格林函数,利用 <strong>4193 </strong>个台站记录的地震波形数据测量的长周期(20-80 秒)瑞利波椭率,利用区域地震波形计算的 <strong>54,792</strong> 条事件-台站间瑞利波频散曲线,以及 <strong>50,867 </strong>个 Pn 波走时数据。下载上述数据库请点击<strong><a href="https://doi.org/10.5281/zenodo.8103561">链接</a></strong>。查看文章请点击<a href="https://doi.org/10.1029/2024JB029520"><strong>链接</strong></a>。模型在线绘图请点击<a href="http://chinageorefmodel.org/csrm-visualizers/csrm-1-0-visualizer">链接</a>。</p> <p>If you face any problem or issue in the usage of this model, please feel free to communicate with the corresponding author Xiao Xiao (<strong>xiaox.seis@gmail.com</strong>). </p>
Nepal School Seismology Network
<p>The Nepal School Seismology Network is a low-cost seismological network installed for both educational and observational purposes. After a pilot station installed in 2018, the network operates 22 sensors since April-May 2019 (see map).</p> <p>The sensors are RaspberryShake 1D instruments. The data is freely available to the public through IRIS (http://ds.iris.edu) and RaspberryShake (https://raspberryshake.org/).</p> <p>The online waveforms are visible at: https://raspberryshake.net/stationview/ (then zoom to Nepal).</p> <p>The station are regrouped under the "_NSSN" virtual network code. Full details on the stations can be found here: http://ds.iris.edu/mda/_NSSN/</p> <p>Further details on the Seismology at School in Nepal program can be found here: www.seismoschoolnp.org</p> <p>Both the program and the network operate on a non-profit basis.</p> <p>We are grateful to all participating schools, teachers and students for hosting the instruments.</p>
Dataset used in "Urban Seismic Site Characterization by Fiber-Optic Seismology" by Spica et al. in Journal of Geophysical Research: Solid Earth
<p>Spica et al. (2019, Journal of Geophysical Research: Solid Earth). This repository contains continuous waveforms from DAS for one day and for the stations mentioned in the article (Fig. 8).</p> <p>Files: all traces in miniseed format. Traces stats in the headers and in the article.</p>
The Petrinja 2020 earthquake sequence - Croatian Seismological Network
<p>The data set contains waveforms of the 2020 MW6.4 Petrinja earthquake mainshock and six additional M>4 earthquakes from the same earthquake sequence, recorded on the Croatian Seismological Network. The operation of the VINV, STON, RUJC, RABC, NVLJ, and CERK seismic stations are/were part of the semi-permanent seismic network operated by the Department of Geophysics at the Faculty of Science of the University of Zagreb that is/was financially supported by various seismological projects.</p>
Seismological dataset for 2018 West Bohemia earthquake swarm
<p>Dataset used in the study: T. Eulenfeld (2020), Toward source region tomography with inter-source interferometry: Shear wave velocity from 2018 West Bohemia swarm earthquakes, <em>Journal of Geophysical Research: Solid Earth</em>, 125, e2020JB019931, doi: <a href="https://dx.doi.org/10.1029/2020JB019931">10.1029/2020JB019931</a>.</p> <p>The dataset includes</p> <ul> <li>HYPODD pha file with relocated earthquake catalog</li> <li>HYPODD pha file with relocated earthquake catalog of selected high quality events (Eulenfeld, 2020)</li> <li>Text file with focal mechanisms of 13 largest earthquakes</li> <li>Text file with coordinates of 9 WEBNET stations</li> <li>StationXML file with coordinate and response information of 9 WEBNET stations (prepared from RESP files by T. Eulenfeld)</li> <li>MSEED files of waveforms of earthquakes</li> </ul> <p>Citation for waveforms:</p> <p>Institute of Geophysics, Academy of Sciences of the Czech Republic (1991): West Bohemia Local Seismic Network. International Federation of Digital Seismograph Networks. Dataset/Seismic Network. <a href="https://www.doi.org/10.7914/SN/WB">10.7914/SN/WB</a></p> <p>Citation for earthquake catalog:</p> <p>Bachura M, Fischer T, Doubravová J, Horálek J, From earthquake swarm to a main shock–aftershocks: the 2018 activity in West Bohemia/Vogtland (2021), <em>Geophysical Journal International</em>, 224 (33): 1835–1848, doi: <a href="https://doi.org/10.1093/gji/ggaa523">10.1093/gji/ggaa523</a></p> <p>Citation for focal mechanisms:</p> <p>Plenefisch T and Barth L (2019), The May 2018 earthquake swarm in Vogtland/NW-Bohemia: Spatiotemporal evolution and focal mechanism determinations, in Geophysical Research Abstracts, volume 21, EGU2019–9356</p> <p> </p> <p>Version 2:</p> <ul> <li>Version 1 of the data set included only waveforms for earthquakes with magnitude larger than 1.8, version 2 of the data set includes almost all waveforms for earthquakes listed in the catalog</li> <li>Added StationXML file</li> <li>Added catalog with selected high quality events</li> </ul>
Dataset for Global Reference Seismological Data Sets: Multimode Surface Wave Dispersion
<ul> <li><strong>How fast do surface waves travel globally after any earthquake?</strong></li> <li><strong>Do we get the same information from various measurement techniques?</strong></li> <li><strong>Which features in the Earth are robust and can be resolved by a reference model?</strong></li> </ul> <p>Reference data with uncertainties are useful for improving existing measurement techniques, validating models of interior structure, calculating teleseismic data corrections in local or multiscale investigations and developing a 3-D reference Earth model. This study was done in collaboration with 18 scientists from 16 institutions in 7 countries who actively participated in the <a href="http://rem3d.org">REM3D</a> project. The project assimilated, archived, reconciled and modeled big (>200 million measurements) and diverse <a href="https://globalseismology.princeton.edu/data/surface-waves">surface-wave datasets</a> for global subsurface structure.</p> <p>The reference data set summarizes measurements of dispersion of fundamental-mode surface waves and up to six overtone branches from 44,871 earthquakes recorded on 12,222 globally distributed seismographic stations. Dispersion curves are specified at a set of reference periods between 25 and 250 s to determine propagation-phase anomalies with respect to a reference Earth model. Empirically determined observational uncertainties (1 sigma) for each wave type, branch number and period can be found in Table 3. </p> <p><strong>Summary:</strong></p> <p><strong>[I]</strong> <strong>Reconciled large and diverse catalogues</strong> of Love-wave (49.65 million) and Rayleigh-wave dispersion (177.66 million) from eight groups worldwide.<br> <strong>[II]</strong> Retrieved missing station and earthquake <strong>metadata</strong> in several legacy compilations and codified <strong>scalable formats</strong> to facilitate reproducibility, easy storage and fast I/O on HPC systems.<br> <strong>[III]</strong> <strong>Systematic discrepancies </strong>between raw phase anomalies can be attributed to discrepant theoretical approximations, reference Earth models and processing schemes.<br> <strong>[IV]</strong> <strong>Phase-velocity variations</strong> yielded by the inversion of the summary data set are <strong>highly correlated</strong> (R ≥ 0.8) with those from the quality-controlled contributing data sets, especially for long-wavelength variations (up to degree ∼25) in fundamental-mode dispersion (50–100 s).<br> <strong>[IV]</strong> <strong>Only 2ζ azimuthal variations</strong> in phase velocity of <strong>fundamental-mode Rayleigh waves</strong> are <strong>required</strong>; maps of 2ζ azimuthal variations are highly consistent between catalogues ( R = 0.6–0.8).</p> <p><strong>Feedback/Questions?</strong> Please contact Raj Moulik (<a href="https://rajmoulik.com">rajmoulik.com</a>) at <a href="mailto:moulik@caa.columbia.edu?subject=Query%20from%20Zenodo">moulik@caa.columbia.edu</a> </p> <p><strong>Reference:</strong></p> <p><em>Please cite the following work if you use this data or software.</em></p> <ul> <li>Moulik, P. <em>et al., </em>(2022) Global reference seismological data sets: multimode surface wave dispersion. <em>Geophys J Int</em> <strong>228</strong>, 1808–1849, doi: <a href="https://doi.org/10.1093/gji/ggab418">10.1093/gji/ggab418</a>. <em><a href="https://rajmoulik.com/Publications/Moulik_Reference_Surface_Waves_GJI2022.pdf">pdf</a></em></li> </ul> <p><em>You can also cite the dataset and software from this Zenodo page (Optional).</em></p> <ul> <li> <p>Moulik, P. (2022) Dataset for Global Reference Seismological Data Sets: Multimode Surface Wave Dispersion. In Geophys. J. Int. (v1.0, Vol. 228, pp. 1808–1849). Zenodo. doi: <a href="https://doi.org/10.5281/zenodo.8371228">10.5281/zenodo.8371228</a></p> </li> </ul> <p><strong>HDF5 Container Format</strong></p> <ul> <li><strong>Reference Love waves </strong>(<a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.SW.Love.data.h5">Download All Periods and Branches as Summary.SW.Love.data.h5</a>)</li> <li><strong>Reference Rayleigh waves </strong>(<a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.SW.Rayl.data.h5">Download All Periods and Branches as Summary.SW.Rayl.data.h5</a>)</li> </ul> <p>Summary (reference) data between pairs of 2562 evenly-spaced knot points with an average spacing of 4.33◦. These files store the data in the RSDF HDF5 container format. These can be read using standard HDF5 modules (e.g. h5py) or using <a href="http://avni.globalseismology.org/">AVNI</a>. For example, to read the reference data for fundamental mode R1 waves at 100s into a Pandas Dataframe containing data (df['data']) and a dictionary with the metadata (df['metadata']), and thereafter write contents to an ASCII text file, enter the following in Python:</p> <ul> <li><em>from avni.data.SW import readSWhdf5,writeSWascii</em></li> <li><em>df=readSWhdf5(query='0/100.0/R1/REM3D',hdffile='Summary.SW.Rayl.data.h5',datatype='summary')</em></li> <li><em>writeSWascii(df,'test.txt')</em></li> </ul> <p><strong>ASCII (text) Format</strong></p> <p>These files contain the same reference data as the HDF5 files above but in gzipped ASCII files. The files are named according to the overtone branch, wave type and period as <em>Summary.$overtone.$wave.$period.REM3D.gz</em> Table A1 from the paper describes the various columns in the surface-wave RSDF ASCII format files.</p> <ul> <li><strong>Love waves </strong>(<a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.SW.Love.data.zip">Download All Periods and Branches as Summary.SW.Love.data.zip</a>) <ul> <li>Fundamental Modes <ul> <li>Minor Arc Arrivals (L1) at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.25s.REM3D.gz">25s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.27s.REM3D.gz">27s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.30s.REM3D.gz">30s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.32s.REM3D.gz">32s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.35s.REM3D.gz">35s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.60s.REM3D.gz">60s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.75s.REM3D.gz">75s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.100s.REM3D.gz">100s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.125s.REM3D.gz">125s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.250s.REM3D.gz">250s</a></li> <li>Major Arc Arrivals (L2) at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L2.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L2.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L2.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L2.250s.REM3D.gz">250s</a></li> <li>Higher Obit Arrivals - L3 at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L3.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L3.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L3.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L3.250s.REM3D.gz">250s</a>; L4 at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L4.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L4.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L4.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L4.250s.REM3D.gz">250s</a>; L5 at at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L5.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L5.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L5.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L5.250s.REM3D.gz">250s</a>.</li> </ul> </li> <li>I<sup>st</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.60s.REM3D.gz">60s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.75s.REM3D.gz">75s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.100s.REM3D.gz">100s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.125s.REM3D.gz">125s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.175s.REM3D.gz">175s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.200s.REM3D.gz">200s</a></li> <li>II<sup>nd</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.60s.REM3D.gz">60s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.75s.REM3D.gz">75s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.100s.REM3D.gz">100s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.125s.REM3D.gz">125s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.150s.REM3D.gz">150s</a></li> <li>III<sup>rd</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.L1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.L1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.L1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.L1.60s.REM3D.gz">60s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.L1.75s.REM3D.gz">75s</a></li> <li>IV<sup>th</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.L1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.L1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.L1.50s.REM3D.gz">50s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.L1.60s.REM3D.gz">60s</a></li> <li>V<sup>th</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.5.L1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.5.L1.45s.REM3D.gz">45s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.5.L1.50s.REM3D.gz">50s</a></li> </ul> </li> <li><strong>Rayleigh waves </strong>(<a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.SW.Rayl.data.zip">Download All Periods and Branches as Summary.SW.Rayl.data.zip</a>) <ul> <li>Fundamental Modes <ul> <li>Minor Arc Arrivals (R1) at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.25s.REM3D.gz">25s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.27s.REM3D.gz">27s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.30s.REM3D.gz">30s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.32s.REM3D.gz">32s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.35s.REM3D.gz">35s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.60s.REM3D.gz">60s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.75s.REM3D.gz">75s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.100s.REM3D.gz">100s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.125s.REM3D.gz">125s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.250s.REM3D.gz">250s</a></li> <li>Major Arc Arrivals (R2) at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R2.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R2.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R2.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R2.250s.REM3D.gz">250s</a></li> <li>Higher Obit Arrivals - R3 at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R3.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R3.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R3.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R3.250s.REM3D.gz">250s</a>; R4 at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R4.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R4.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R4.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R4.250s.REM3D.gz">250s</a>; R5 at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R5.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R5.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R5.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R5.250s.REM3D.gz">250s</a>.</li> </ul> </li> <li>I<sup>st</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.60s.REM3D.gz">60s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.75s.REM3D.gz">75s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.100s.REM3D.gz">100s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.125s.REM3D.gz">125s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.175s.REM3D.gz">175s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.200s.REM3D.gz">200s</a></li> <li>II<sup>nd</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.60s.REM3D.gz">60s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.75s.REM3D.gz">75s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.100s.REM3D.gz">100s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.125s.REM3D.gz">125s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.150s.REM3D.gz">150s</a></li> <li>III<sup>rd</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.R1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.R1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.R1.60s.REM3D.gz">60s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.R1.75s.REM3D.gz">75s</a></li> <li>IV<sup>th</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.R1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.R1.50s.REM3D.gz">50s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.R1.60s.REM3D.gz">60s</a></li> <li>V<sup>th</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.5.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.5.R1.45s.REM3D.gz">45s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.5.R1.50s.REM3D.gz">50s</a></li> <li>VI<sup>th</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.6.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.6.R1.45s.REM3D.gz">45s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.6.R1.50s.REM3D.gz">50s</a></li> </ul> </li> </ul> <p><strong>Other Data Products:</strong></p> <ul> </ul> <ul> <li><strong>ReferenceSW_Moulik2022_Figures(<a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/ReferenceSW_Moulik2022_Figures.zip">.zip</a> or <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/ReferenceSW_Moulik2022_Figures.pdf">.pdf</a>)</strong> - contains all figures from the paper in .png format</li> <li><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Scatter_Plots.zip"><strong>Scatter_Plots.zip</strong></a> - contains scatter plots similar to Figure 5 in the paper, which compares measurements between two sets of techniques. The files with the suffix *raw.png are comparisons for original raw datasets, while those with the suffix *.clean.png are comparisons after the entire workflow is completed to create the clean datasets (e.g. Figure 13, bottom row).</li> <li><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Half_cycle.zip"><strong>Half_cycle.zip</strong></a> and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Cycle_skips.zip"><strong>Cycle_skips.zip</strong></a> - contains list of source-station paths where discrepancies were found between pairs of techniques. Half (±0.9–1.1 · π ) or full-cycle discrepancies (±0.9–1.1 · 2π ) identified in Section 4.5 are used during outlier analysis (Section 5.3) to create the clean summary dataset. Half- and full-cycle discrepancies identified in these files indicate potential polarity reversals and cycle skips respectively. Note that all of these discrepancies have not been checked for specific causes manually. </li> <li><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/vflip-table.REM3D"><strong>vflip-table.REM3D</strong></a> - an ASCII file containing station names and start/end times where polarity reversal issues have been confirmed through manual analysis. This is in contrast to the automated half-cycle discrepancies identified in <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Half_cycle.zip"><strong>Half_cycle.zip</strong></a> above.</li> <li><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/M1442"><strong>M1442</strong></a> and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/B2562"><strong>B2562</strong></a> - Files containing the knot locations of evenly-spaced points on the surface. B2562 has an average knot spacing of 4.33◦ and is used as the underlying grid for the homogenization process to get summary data (Section 5.1). In order to obtain 2-D variations in local phase slowness or velocity, we use 1442 splines with an average knot spacing of 5.77◦ (Section 6.1)</li> <li><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Cleanhomo.SW.Love.data.h5"><strong>Cleanhomo.SW.Love.data.h5</strong></a> and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Cleanhomo.SW.Rayl.data.h5"><strong>Cleanhomo.SW.Rayl.data.h5</strong></a> - Clean homogenized data for each research group obtained at the end of our workflow (Figure 2). The ASCII files containing the same data are provided in <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Cleanhomo.SW.Love.data.zip"><strong>Cleanhomo.SW.Love.data.zip</strong></a> and <strong><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Cleanhomo.SW.Rayl.data.zip">Cleanhomo.SW.Rayl.data.zip</a>. </strong>The summary dataset listed earlier represents the reconciled measurements, and should be preferred over those from individual groups in most applications.</li> <li><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Inversion_Example.zip"><strong>Inversion_Example.zip</strong></a> - Contains an example of a 2D slowness map inversion with 2ζ azimuthal variations using the reference summary dataset at 100s for fundamental-mode minor-arc Rayleigh waves (R1). Also provided are plots for anistropic variation (<em>Anisotropy_Plots</em>), spline coeffients of 1442 evenly-spaced spherical splines (<em>Spline_Coefficients</em>), and corresponding values at every 1X1 degree pixel in extended pixel format (<em>Maps_epix</em>). The aim of this study is to provide dispersion measurements of surface-wave arrivals, not to provide detailed 2D phase velocity/slowness models. </li> </ul> <p><strong>Note about Data Format</strong></p> <p>The underlying philosophy and format of data files are discussed in the <a href="https://globalseismology.princeton.edu/rsdf">reference seismic data format (RSDF) project</a>. Table A1 from the GJI paper describes the various columns in the surface-wave RSDF format files above.</p>
Hales discontinuity in the southern Indian continental lithosphere: seismological and petrological models
Open the record for dataset details and reuse information.
Relocating the Full-Ocean-Depth Multifunctional Landers Seafloor Landing Point in the Challenger Deep Based On A Seismological Approach: A Precise Noninversive US-DTM Method Challenges the Traditional Inversive MC Method
<p>The SEGY format raw OBS-Lander data</p>
VIKING LANDER 2 MARS SEISMOLOGY RESTORED DATA V1.0
The dataset comprises records in Event and High Rate modes for the three axes of the Viking Lander 2 Seismology Experiment. High rate data are raw velocity signals sampled at 20.2 Hz. Event mode data are 1.01 second records of the envelope (amplitude) and zero-crossings (frequency). Summary files of the key characteristics of these records, together with the nearest wind measurement data, are also given.
InSight On-Deck Seismology
InSight On-Deck Seismology
GNSS-seismology for anthropogenic earthquakes: first feasibility demonstration
<p>The dataset of GNSS time series with duration of 150 seconds before and 95 seconds after the origin time of the mining tremors calculaed with PPP and variometric approach. The dataset was used in the research on "GNSS-seismology for anthropogenic earthquakes: first feasibility demonstration."</p> <p>Further description of the HR-GNSS processing will be found in the paper "GNSS-seismology for anthropogenic earthquakes: first feasibility demonstration."</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.