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662 results for “Seismicity”
Dataset from Experimental and Nonlinear Finite Element Modeling Investigating an Innovative Buckling Restrained Bracing System for Rehabilitation of Seismic Deficient Structures
<p>The data presented in this paper were collected experimentally and modeled using the finite element method. A total of six BRBs (i.e., duplicates of three types of BRB core bars) specimens were tested experimentally and verified numerically using the finite element method employing the commercial Software ABAQUS. Specific labeling was used to designate each BRB type. Three core bars were used in the tested BRBs: fully-threaded, threaded-notched, and smooth-shaved. The specimens are labeled according to their core bar type and diameter. i.e., BRB-12-Th stands for a full threaded core bar diameter of 12 mm, the threaded notched type was labeled BRB-12-Th-Nd, and the smooth shave one was labeled BRB-12-Sh.</p> <p>Further details of the tested BRBs are included in the excel file called dimensions and properties of BRBs. The worksheet provides details of the BRB components (i.e., core bar, restraining unit, and innovative end units). The dimensions and strength of the materials were obtained from coupon tests. The experimental data are presented in the second excel file labeled hysteresis with three embedded worksheets, one for each type of BRB. The excel sheets provide the cyclic loading data and plots showing the hysteresis behavior of tested BRBs. A sample of the loading protocol included in the second excel file is presented in Fig.1. The third excel file presents the analytical data extracted from experimental data that has two sheets: stiffness and energy dissipation. The sheet labeled stiffness has the secant stiffness versus deformation plot for the push-pull cycles (compression-tension). The second sheet labeled energy dissipation shows the cumulative energy dissipated.</p>
Geometric Control on Seismic Rupture and Earthquake Sequence along the Yingxiu-Beichuan Fault with Implications for the 2008 Wenchuan Earthquak
<p>A 65000 years seismic sequence is numerical simulated using TriBIE on the unplanar fault plane with variation normal stress. The code is now available in an open-source Git-hub project, <a href="https://github.com/daisy20170101/TriBIE/tree/normal_stress_variation">https://github.com/daisy20170101/TriBIE/tree/normal_stress_variation</a>.</p> <p>The modeling will output the bianary format files of normal stress, fault slip velocity, shear stress, slip during the interseismic loading and coseismic rupture stage, respectively. Since it is impossible to output the data at every time step, especially for the large-scale fault model. Thus, during the interseismic loading, we set a constant time interval to output data and the t-inter-***.dat file will record every time, when the data is outputted. During coseimic rupture, t-cos-**.dat file records time of outputing data. So, the size of t-inter-**.dat and t-cos-**.dat file is the number of outputting steps.The fault plane is discretized into 3,1440 elements and the simulation is carried out by parallel computing on 6 servers with 120 CPUs . Each CPU will dispose data of 262 elements. </p> <p> </p> <p> </p> <p> </p> <p> </p>
Data Set : Seismic Wave Propagation Simulations in Indo Gangetic Basin using Spectral Element Method
<p>Indo Gangetic (IG) basin is one of the largest alluvial basins in the world. The surrounding Himalayan topography and the geometry of the basin make the IG basin unique. The analysis of seismic response of the basin is important as the region is seismically active with more than 40% of Indian population residing in it. This online database consists of the input files for performing the spectral finite element simulation for IG basin by incorporating the 3D variation of material properties and basin geometry. The input files consists of mesher, solver and CMTSOLUTION files for SPECFEM3D Cartesian (Version-3) simulation.</p>
Waveforms and results of seismic attenuation in Sumatra subduction zone, Indonesia
<p>Datasets for the manuscript:</p> <p>Styawan, Y., Kuo, C.-H., Huang, B.-S., Wen, K.-L., Haridhi, H. A., Sianipar, D., Characteristics of seismic attenuation in Sumatra subduction zone, Indonesia (submitted)</p> <p>The attached files include:</p> <p>1) 0.2 Hz Highpass filtered waveforms for Z and T components.</p> <p>2) Results (α, event, station, t*, Q, corner frequency, Ω0, SNR, component (Z or T), category (forearc, mountain, or backarc), and Qp/Qs).</p> <p>3) Additional data (information on events and stations).</p> <p>4) site amplification factors (P and S of all stations in different α).</p>
Machine Learning Ready Induced Seismicity Data
<p>This dataset contains previously published data on induced seismicity that has been processed to be machine learning ready.<br> <br> The data contains time series of the cumulative number of seismic events in certain areas and the corresponding pressures induced from injecting fluids into the ground. The natural task is to forecast future seismicity given past seismicity and pressures. These datasets aim to require as little seismology experience as necessary to prepare the data for forecasting algorithms. <br> <br> Data is provided for different locations. For Decatur Illinois, the seismic data was taken from Williams-Stroud et al., 2018 and the pressure data originated from Luu et al., 2022. Data aggregated over the whole region lies in the temporal_datasets/decatur_illinois/ folder. The region was further subdivided into subregions and the corresponding data stored separately (e.g. in loc1). </p> <p>The Kansas data originated from Cochran et al., 2018 and is further divided into subregions. </p> <p>The Cushing, Oklahoma is adapted from Skoumal et al., 2020. </p> <p>Each seismic file contains the following columns: epoch latitude longitude depth easting northing magnitude. The epoch corresponds to the number of seconds since a certain date (e.g. November 17, 2011 for Decatur). Each seismic event corresponds to one row in the file. <br> <br> Each pressure file contains the following columns: epoch pressure dpdt. dpdt is the derivative of pressure. </p>
Seismic noise interferometry and Distributed Acoustic Sensing (DAS): Inverting for the firn layer S-velocity structure on Rutford Ice Stream, Antarctica
<p>This dataset contains files including continuous DAS and geophone data and a refracted P wave travel time data collected on Rutford Ice Stream, Antarctica. The seismic data is used to perform seismic noise interferometry. The travel time data is used to perform refraction inversion to get the P wave velocity profile.</p> <p><br> 1. 7 hours of continuous DAS data (100 Hz sampling): 2020-01-14T00:00:19.598000Zoffset_****.mseed, with offset referring to the distance from the DAS channel to the interrogator.</p> <p>2. Corresponding 7 hours of vertical component continuous geophone (A000, located at DAS channel offset 570 m) data.</p> <p>3. Refraction P wave travel time from a geophone array refraction survey.</p>
Husmuli Injection and Seismicity Data 2015-2020
<p>This dataset comprises the injection data, and seismic catalogue recorded in the Húsmúli reinjection area (Hellisheiði geothermal field, SW Iceland) between 2015 and 2020.</p> <p>The hydraulic data was acquired and processed by Reykjavik Energy/ON power, the operator of the Hellisheiði geothermal field. The seismic catalogue is curated by the Icelandic Met Office.</p>
A waveform dataset in continuous mode of the Montefeltro seismic network (MF) in central-northern Italy from 2018 to 2020
<p>The Montefeltro seismic network (FDSN Network code: 1S) was deployed in the Apennines area of<br> northern Marche and southern Emilia-Romagna regions (central Italy). The network has been set up<br> starting from December 2018, and the array consists of stations equipped with dynamic digitizers<br> and three component short/extended/broad band seismometers (Guralp 3D/40s, Lennartz 3D/5s,<br> SS20 3D/0.5s sensors). The temporary network records in continuous mode at 100 sps. The data are<br> used to analyse the seismicity and the spatio-temporal evolution of small seismic sequences,<br> occurring in the considered area and surrounding zones, strongly clustered in time and space.<br> Stations (registered in ISC) in this Network:<br> Station code Location Station name Data acquisition<br> Lat(N) Lon(E) Ele(m) Start End<br> MF01 43.82150 12.57190 368 Auditore (PU) 2018-11-29 2020-12-31<br> MF02 43.86540 12.21070 626 Sant'Agata Feltria (RN) 2019-04-19 2020-05-30<br> MF03 43.84860 12.47990 541 Monte Grimano (PU) 2019-10-07 2020-12-31<br> MF04 43.81030 12.05620 1043 Verghereto (FC) 2020-10-09 2020-12-31<br> The data of dataset files are miniseed formatted and subdivided by the following tree:<br> (1) the dataset is divided by years;<br> (2) the divided by years dataset is subdivided by stations;<br> (3) finally, the data are divided by days of each year in every station folder.</p> <p><br> Response information:<br> Station code Sensor Recorder Recorder period<br> Start End<br> MF01 Lennartz 3D/5s Reftek 130 2018-11-29 2019-10-07<br> MF01S Lennartz 3D/5s Sara SL06 2019-10-07 2020-12-31<br> MF02 Guralp CMG/20s Reftek 130 2019-04-19 2020-05-30<br> MF03 Guralp CMG/30s Reftek 130 2019-10-07 2020-12-31<br> MF04 Sara SS02/0.5s Sara SL06 2020-10-09 2020-12-31</p> <p>In this dataset the data recorded by MF01 station are been acquisited by different recorders,<br> the firth record period by Reftek 130 (MF01) and the second record period by Sara SL06 (MF01S).</p> <p>List for the responses of the Seismic Instruments<br> Guralp CMG/20s sensor response: RESP_XX_NS444_BHZ_CMG40T_20_50_800.txt<br> Guralp CMG/30s sensor response: RESP_XX_NS041_BHZ_CMG40T_30_800.txt<br> Lennartz 3D/5s sensor response: RESP_XX_NS484_SHZ_LE-3D5sMkIII_5_800.txt<br> Sara SS02/5s sensor response: RESP_XX_NS505_SHZ_SS02_5.txt<br> Reftek 130 datalogger response: RESP_XX_NR008_HHZ_130_1.txt<br> Sara SL06 datalogger response: RESP_XX_NS000_HHZ_SL06_88_L22x3_100_4.txt</p>
Focal mechanism solutions and relocated earthquake catalog for the Charlevoix Seismic Zone (CSZ)
<p>The relocated catalog for the CSZ (Relocated_earthquakes_CSZ.dat) represent a combination of the catalogs from Yu et al. (2016, BSSA) and Onwuemeka et al. (2018, GRL). The focal mechanism solutions catalog (FMS_CSZ.txt) includes original data combined with the solutions from Mazzotti and Townend (2010, Lithosphere). </p>
Global and regional long-term M4.95+ seismicity forecasts undergoing prospective evaluation
<p>Contains a stationary M5.95+ seismicity forecast derived from the Global Earthquake Activity Rate (GEAR1) model of Bird et al. (2015) and nineteen time-invariant M4.95+ earthquake forecasts participating in forecast experiments conducted by the Collaboratory for the Study of Earthquake Predictability (CSEP) in California, New Zealand, and Italy. Ten additional forecast files are included to properly perform comparative tests.</p> <p>Earthquake rates are expressed as number of M4.95+ earthquakes per 0.1<sup>o</sup> cell per year. Forecasts are stored in tab separated values files with the following fields (the first row is shown as an example):</p> <table> <tbody> <tr> <td>lon_min</td> <td>lon_max</td> <td>lat_min</td> <td>lat_1</td> <td>depth_0</td> <td>depth_1</td> <td>mag_0</td> <td>mag_1</td> <td>rate</td> <td>flag</td> </tr> <tr> <td>-125.4</td> <td>-125.3</td> <td>40.1</td> <td>40.2</td> <td>0.0</td> <td>30.0</td> <td>4.95</td> <td>5.05</td> <td>5.8499e-04</td> <td>1</td> </tr> </tbody> </table> <p>The data, forecasts, and tests are described in detail in the following publications and the references contained therein:</p> <p>Bayona, J.A., Savran, W.H., Iturrieta, P., Gerstenberger, M.C., Marzocchi, W., Schorlemmer, D., and Werner, M.J., Are Regionally Calibrated Seismicity Models more Informative than Global Models? Insights from California, New Zealand, and Italy. <em>in review</em>.</p> <p>Bayona, J.A., Savran, W.H., Rhoades, D.A. and Werner, M.J., 2022. Prospective evaluation of multiplicative hybrid earthquake forecasting models in California. <em>Geophysical Journal International</em>, <em>229</em>(3), pp.1736-1753.</p> <p>Bird, P., Jackson, D.D., Kagan, Y.Y., Kreemer, C. and Stein, R.S., 2015. GEAR1: A Global Earthquake Activity Rate Model Constructed from Geodetic Strain Rates and Smoothed SeismicityGEAR1: A Global Earthquake Activity Rate Model Constructed from Geodetic Strain Rates and Smoothed Seismicity. <em>Bulletin of the Seismological Society of America</em>, <em>105</em>(5), pp.2538-2554.</p> <p>Marzocchi W, Schorlemmer D, Wiemer S. Preface. Ann. Geophys. [Internet]. 2010Nov.5 [cited 2022Sep.16];53(3):III-VIII. Available from: https://www.annalsofgeophysics.eu/index.php/annals/article/view/4851</p> <p>Rhoades, D.A., Christophersen, A., Gerstenberger, M.C., Liukis, M., Silva, F., Marzocchi, W., Werner, M.J. and Jordan, T.H., 2018. Highlights from the first ten years of the New Zealand earthquake forecast testing center. <em>Seismological Research Letters</em>, <em>89</em>(4), pp.1229-1237.</p> <p>Savran, W.H., Bayona, J.A., Iturrieta, P., Asim, K.M., Bao, H., Bayliss, K., Herrmann, M., Schorlemmer, D., Maechling, P.J. and Werner, M.J., 2022. pycsep: A python toolkit for earthquake forecast developers. <em>Seismological Society of America</em>, <em>93</em>(5), pp.2858-2870.</p> <p>Schorlemmer, D., Gerstenberger, M.C., Wiemer, S., Jackson, D.D. and Rhoades, D.A., 2007. Earthquake likelihood model testing. <em>Seismological Research Letters</em>, <em>78</em>(1), pp.17-29.</p> <p>Werner, M.J., Zechar, J.D., Marzocchi, W. and Wiemer, S., 2010. Retrospective evaluation of the five-year and ten-year CSEP-Italy earthquake forecasts. <em>arXiv preprint arXiv:1003.1092</em>.</p> <p>Zechar, J.D., Gerstenberger, M.C. and Rhoades, D.A., 2010. Likelihood-based tests for evaluating space–rate–magnitude earthquake forecasts. <em>Bulletin of the Seismological Society of America</em>, <em>100</em>(3), pp.1184-1195.</p> <p>Zechar, J.D., Schorlemmer, D., Werner, M.J., Gerstenberger, M.C., Rhoades, D.A. and Jordan, T.H., 2013. Regional earthquake likelihood models I: First‐order results. <em>Bulletin of the Seismological Society of America</em>, <em>103</em>(2A), pp.787-798.</p>
Dataset for paper "Mitigating the effect of errors in source parameters on seismic (waveform) inversion"
<p>Dataset corresponding to the journal article "Mitigating the effect of errors in source parameters on seismic (waveform) inversion" by Blom, Hardalupas and Rawlinson, accepted for publication in Geophysical Journal International. In this paper, we demonstrate the effect or errors in source parameters on seismic tomography, with a particular focus on (full) waveform tomography. We study effect both on forward modelling (i.e. comparing waveforms and measurements resulting from a perturbed vs. unperturbed source) and on seismic inversion (i.e. using a source which contains an (erroneous) perturbation to invert for Earth structure. These data were obtained using Salvus, a state-of-the-art (though proprietary) 3-D solver that can be used for wave propagation simulations (Afanasiev et al., GJI 2018).</p> <p>This dataset contains:</p> <ul> <li>The entire Salvus project. This project was prepared using Salvus version 0.11.x and 0.12.2 and should be fully compatible with the latter.</li> <li>A number of Jupyter notebooks used to create all the figures, set up the project and do the data processing.</li> <li>A number of Python scripts that are used in above notebooks.</li> <li>two conda environment .yml files: one with the complete environment as used to produce this dataset, and one with the environment as supplied by Mondaic (the Salvus developers), on top of which I installed basemap and cartopy.</li> <li>An overview of the inversion configurations used for each inversion experiment and the name of hte corresponding figures: inversion_runs_overview.ods / .csv .</li> <li>Datasets corresponding to the different figures. <ul> <li>One dataset for Figure 1, showing the effect of a source perturbation in a real-world setting, as previously used by Blom et al., Solid Earth 2020</li> <li>One dataset for Figure 2, showing how different methodologies and assumptions can lead to significantly different source parameters, notably including systematic shifts. This dataset was kindly supplied by Tim Craig (Craig, 2019).</li> <li>A number of datasets (stored as pickled Pandas dataframes) derived from the Salvus project. We have computed: <ul> <li>travel-time arrival predictions from every source to all stations (df_stations...pkl)</li> <li>misfits for different metrics for both P-wave centered and S-wave centered windows for all components on all stations, comparing every time waveforms from a reference source against waveforms from a perturbed source (df_misfits_cc.28s.pkl)</li> <li>addition of synthetic waveforms for different (perturbed) moment tenors. All waveforms are stored in HDF5 (.h5) files of the ASDF (adaptable seismic data format) type</li> </ul> </li> </ul> </li> </ul> <p>How to use this dataset:</p> <ul> <li>To set up the conda environment: <ol> <li>make sure you have anaconda/miniconda</li> <li>make sure you have access to Salvus functionality. This is not absolutely necessary, but most of the functionality within this dataset relies on salvus. You can do the analyses and create the figures without, but you'll have to hack around in the scripts to build workarounds.</li> <li>Set up Salvus / create a conda environment. This is best done following the instructions on the Mondaic website. Check the changelog for breaking changes, in that case download an older salvus version.</li> <li>Additionally in your conda env, install basemap and cartopy: <pre><code class="language-bash">conda-env create -n salvus_0_12 -f environment.yml conda install -c conda-forge basemap conda install -c conda-forge cartopy</code></pre> </li> <li> <p>Install LASIF (https://github.com/dirkphilip/LASIF_2.0) and test. The project uses some lasif functionality.</p> </li> <li> <p> </p> </li> <li> <p> </p> </li> </ol> </li> <li>To recreate the figures: This is extremely straightforward. Every figure has a corresponding Jupyter Notebook. Suffices to run the notebook in its entirety. <ul> <li>Figure 1: separate notebook, Fig1_event_98.py</li> <li>Figure 2: separate notebook, Fig2_TimCraig_Andes_analysis.py</li> <li>Figures 3-7: Figures_perturbation_study.py</li> <li>Figures 8-10: Figures_toy_inversions.py</li> </ul> </li> <li>To recreate the dataframes in DATA: This can be done using the example notebook Create_perturbed_thrust_data_by_MT_addition.py and Misfits_moment_tensor_components.M66_M12.py . The same can easily be extended to the position shift and other perturbations you might want to investigate.</li> <li>To recreate the complete Salvus project: This can be done using: <ul> <li>the notebook Prepare_project_Phil_28s_absb_M66.py (setting up project and running simulations)</li> <li>the notebooks Moment_tensor_perturbations.py and Moment_tensor_perturbation_for_NS_thrust.py</li> <li>For the inversions: using the notebook Inversion_SS_dip.M66.28s.py as an example. See the overview table inversion_runs_overview.ods (or .csv) as to naming conventions.</li> </ul> </li> </ul> <p> </p> <p>References:</p> <ul> <li>Michael Afanasiev, Christian Boehm, Martin van Driel, Lion Krischer, Max Rietmann, Dave A May, Matthew G Knepley, Andreas Fichtner, Modular and flexible spectral-element waveform modelling in two and three dimensions, <em>Geophysical Journal International</em>, Volume 216, Issue 3, March 2019, Pages 1675–1692, <a href="https://doi.org/10.1093/gji/ggy469">https://doi.org/10.1093/gji/ggy469</a></li> <li>Nienke Blom, Alexey Gokhberg, and Andreas Fichtner, Seismic waveform tomography of the central and eastern Mediterranean upper mantle, <em>Solid Earth</em>, Volume 11, Issue 2, 2020, Pages 669–690, 2020, <a href="https://doi.org/10.5194/se-11-669-2020">https://doi.org/10.5194/se-11-669-2020</a></li> <li>Tim J. Craig, Accurate depth determination for moderate-magnitude earthquakes using global teleseismic data. <em>Journal of Geophysical Research: Solid Earth</em>, 124, 2019, Pages 1759– 1780. <a href="https://doi.org/10.1029/2018JB016902">https://doi.org/10.1029/2018JB016902</a></li> </ul> <p> </p>
seismic data for solitons
<p>Suplementary data (SD for paper:"<strong>A locally generated high-mode nonlinear internal wave detected on the shelf of the northern South China Sea from marine seismic observations</strong>".</p> <p> </p>
Accelerogram (raw, msd), GLT4 seismic station (Galati, Romania), Vrancea (Romania) earthquake, 2020-04-25 01:04:18 ML=5.0 h=21.6 km
<p>Accelerogram (raw, msd) recorded at GLT4 seismic station (Galati, Romania).</p> <p>Vrancea (Romania) earthquake, 2020-04-25 01:04:18 (local time), ML=5.0, h=21.6 km.</p> <p>Recorded on a GeoSIG GMS-18 instrument.</p>
SLABSTRESS: SLAB STructural RESponse for Seismic European Design
<p><span>The dataset reports results from the full-scale testing of a two-storey flat slab structure, undertaken in the SlabSTRESS research project; the construction and testing were planned and carried out at the ELSA laboratory of the European Commission’s Joint Research Centre. The dimensions are three bays by two, spans 4.5 and 5 m, slab thickness 0.2 m, interstorey height 3.2 m. Two different longitudinal reinforcement details were considered; welded studs shear reinforcement was provided only in the second floor slab. The testing program included seismic tests for service and ultimate actions, using the pseudo-dynamic technique with virtual walls. To this aim, a building structure was designed with primary walls and the flat slab frame as secondary element. Cyclic loading tests followed up to ultimate drift capacity of the structure. The sequence of tests included strengthening of a set of damaged connections using bolted bars in holes drilled through the slab, followed by cyclic testing to failure. The instrumentation was provided for the global response and the connections with local rotations in the columns and slab; cracking around the columns was measured with through-crack sensors; a measurement system for internal forces and moments was included within the columns. The results show the response with deformations and damage for the different loading conditions up to failure. The results obtained on a full-scale structure extend and confirm the knowledge in the literature, mainly based on isolated connections and/or small-scale samples.</span></p>
Catalog of 3D DD Locations of the Irpinia Micro-Seismicity from 2008 to 2022
<p>The micro-seismicity catalog was obtained by analyzing a 89 data set consisting of about 2400 micro-earthquakes, with local magnitude (ML) ranging 90 between 0.5 and 3.2. These events were recorded by 42 ISNet and INGV stations from August 2005 to December 2022. We used manually picked first P- and S-wave arrival times from ISNet bulletin (http://isnet-bulletin.fisica.unina.it/cgi-bin/isnet-events/isnet.cgi), and integrated manually picks from INGV stations. Initially, we located the events with a probabilistic method (NLLoc, Lomax et al. 2009) and a 3D velocity model optimized for the area (De Landro et al., 2022), which allowed to obtain a first location catalog with an average RMS residual of 0.15 s and location errors smaller than 2 km. Successively, we refined the absolute 3D location with the double-difference approach (HypoDD, Waldauser & Ellswort, 2000) by using catalog (CT) and cross-correlation (CC) differential times (Schaff & Waldauser, 2005). The final residual RMS was 0.008 s for CC data and 0.03 s for CT data. The final location errors were within 100 meters for most of the events.</p> <p>Moreover, we provided the composite focal mechanisms of four clusters of this new micro-seismicity catalog. Similarly to Muzellec et al. (2023), we evaluated, for selected hypocenter clusters, the composite focal mechanisms by usinf FPFIT (Reasenberg, 1985) and integrating the polarities of co-located events for the construction of more constrained mechanisms. Furthermore, we integrated four additional focal mechanisms of single events. These were selected among the focal mechanisms available from the ISNet bulletin and refined by using the 3D location. To validate this selection, we compared them with those obtained by De Matteis et al. (2012), in which the author performed an extensive analysis of focal mechanisms and refined the stress field of the Irpinia region.</p>
Exposure and fragility of a virtual oil refinery testbed for seismic risk assessment
<p><span>Α</span><span> </span><span>virtual mid-size oil refinery, located in a high-seismicity region of Greece, is offered as a testbed for developing and testing system-level assessment methods. The dataset includes (a) a full geolocated exposure model with all pertinent critical assets, namely tanks, pressure vessels, process towers, chimneys, equipment-supporting buildings, and a flare; (b) the corresponding record-wise asset demands and summarized fragilities derived via nonlinear dynamic analyses on reduced-order numerical models.</span></p>
Template matching catalog of the seismicity induced by hydraulic fracturing operations at Preston New Road (UK) in 2019
<p>The file PNR2_TMcatalog.csv contains the catalog of seismicity induced by the hydraulic fracturing operations carried out at Preston New Road (UK) in 2019. The catalog was created with template matching using a single downhole sensor located in a monitoring well.</p> <p>Here is the description of the columns found in the file:</p> <ul> <li>ARRTIME: P-wave arrival times at the reference station.</li> <li>X, Y, Z: hypocentral coordinates (easting, northing and depth). Northing and easting are expressed in the United Kingdom Ordnance Survey coordinate system (EPSG:27700). Depth is expressed in meters below sea level.</li> <li>MW: moment magnitudes.</li> <li>CLUSTER: cluster ID. Earthquakes with the same cluster ID were detected by the same template.</li> </ul>
Data & scripts - Estimates on the possible annual seismicity of Venus
<p>Data and scripts to reproduce the work in Van Zelst et al. (2024): 'Estimates on the possible annual seismicity of Venus'. See the file 'description_data&scripts.pdf' for more details. </p>
Partial angle-stacked seismic data for quantitative stress prediction
<p>The uploaded partial angle-stacked seismic data can be used as the input file to our inversion method to estimate the effective stress and other elastic parameters of subsurface reservoirs directly. </p>
Supplementary Datasets and Movies for the Paper "Mapping finite-fault earthquake slip using spatial correlation between seismicity and point-source Coulomb failure stress change"
<p>Supplementary Datasets and Movies for the Paper <br><strong>Mapping finite-fault earthquake slip using spatial correlation between seismicity and point-source Coulomb failure stress change </strong><br>by Anthony Lomax</p> <p>DOI: <a href="https://doi.org/10.48550/arXiv.2404.05437" target="_blank" rel="noopener">https://doi.org/10.48550/arXiv.2404.05437</a></p> <p> </p> <p><strong>Movie S1 Animation of the 2018, Mw 7.1 Anchorage, Alaska sequence and background seismicity 2014-2022.</strong> Relocated seismicity shown for: 2014 – 2018 mainshock (light blue), 2018 mainshock – 1 month after mainshock (green), 1 month after mainshock through 2022 (light orange); large black dot indicates the Mw 7.1 mainshock hypocenter. See figure caption in main paper for more details.</p> <p><strong>Movie S2 Animation of seismicity-stress, 3D finite-faulting potential slip results the 2018 Mw 7.1 Anchorage, Alaska earthquake sequence.</strong> The high-potential portion of the seismicity-stress finite-faulting field is shown in red for west-dipping reciever faults inferred from the first 1 day of aftershocks (blue dots) after the 2018 mainshock (large black dot). See figure caption in main paper for more details.</p> <p> </p> <p><strong>CSV (.csv) and NLL-Hypocenter (.hyp) format catalogs of NLL-SSST-coherence relocations used in this study:</strong></p> <p>Parkfield_2022_NLL-SSST-coherence_20231201A.csv<br>Parkfield_2022_NLL-SSST-coherence_20231201A.hyp</p> <p>AntelopeValley_2021_NLL-SSST-coherence_20231223A.csv<br>AntelopeValley_2021_NLL-SSST-coherence_20231223A.hyp</p> <p>Anchorage_2018_NLL-SSST-coherence_20231125A.csv<br>Anchorage_2018_NLL-SSST-coherence_20231125A.hyp</p> <p> </p>
ScienceDex guides
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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.