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28 results for “microseismicity”
Utah FORGE 2024 stimulations: Microseismic event catalog from seismic surface network
<p>This microseismic catalog of the 2024 stimulations at Utah FORGE is based on the surface monitoring network consisting of 5 permanent seismic stations deployed by the University of Utah Seismograph Stations and a temporary deployment of 144 nodal geophones.<br> <br>The catalog and its compilation are described in:<br>Niemz et al. (2024). Mapping Fracture Zones with Nodal Geophone Patches: Insights from Induced Microseismicity during the 2024 Stimulations at Utah FORGE. Submitted to Seismological Research Letters.<br> <br>The local coordinate system is relative to well 16A(78)-32 (lat=38.50402147, lon=-112.8963897, elev=1650m). The magnitudes in this catalog were calibrated based on the local magnitude provided by the University of Utah Seismograph Stations. They are not authoritative.</p>
Joint Microseismic Event Detection and Location with a Detection Transformer
<p>Synthetic passive seismic data and their corresponding labels used for training and testing the network in the paper "Joint Microseismic Event Detection and Location with a Detection Transformer".</p>
Offshore British Columbia microseismicity
<p>Initial and relocations of microseismicity in the Queen Charlotte triple junction and Explorer microplate region offshore British Columbia, Canada from 1995-2021. These data include detections using the Regressive Estimator (REST) algorithm and those from network analysts at the Geological Survey of Canada (GSC). The files include earthquake locations, origin times, and P and S wave travel/arrival times. Centroid moment tensor solutions from the Geological Survey of Canada.</p> <p> </p> <p>1. comb_all_event_4pr_final.dat<br> All (63,566) 4-pair initial data (Unique GSC, REST, and matched events.)<br> Event header format:</p> <p>YYYY MM DD HH MM SC LAT LON DEP 0 0 0 0 ID</p> <p>2. phase_REST_initial_all.dat<br> Detections and locations for all REST events from 1995-2021. Excludes 9,929 events matched with the GSC.<br> Event header format:</p> <ol> <li>YYYY MM DD HH MM SC LAT LON DEP 0 0 0 0 ID<br> Phase detection format:<br> Station Traveltime Quality Phase</li> </ol> <p>3. phase_matchGSC_initial.dat<br> 9,929 events matched between REST-GSC detections from 1995-2021. Locations and phase detections from the GSC.<br> Event header format:</p> <ol> <li>YYYY MM DD HH MM SC LAT LON DEP 0 0 0 0 ID<br> Phase detection format:<br> Station Traveltime Quality Phase</li> </ol> <p>4. phase_GSC_initial_all_mag_ml.dat<br> 40,839 events from the GSC catalog from 1995-2021. Includes local (ML) magnitudes.<br> Event header format:</p> <ol> <li>YYYY MM DD HH MM SC LAT LON DEP MAG 0 0 0 ID<br> Phase detection format:<br> Station Traveltime Quality Phase</li> </ol> <p>5. hypreloc_fix5_4pr_all.reloc<br> 18,441 final hypoDD relocations in hypoDD file format.</p> <p>6. PGC_CMT.tgz<br> .tgz file includes Geological Survey of Canada centroid moment tensor solutions and associated README file.</p>
Catalog of microseismicity related to the Alto-Tiberina Fault
<p>This template matching catalog contains information about new detected seismicity as the origin time (ot), latitude (lat_temp), longitude (lon_temp), depth (depth_tep), magnitude (mag), origin time of the template that can be used as the event_id (ot_template), the ratio between the average correlation coefficient (CC) and the daily median absolute deviation of the averaged CCs, as an indication for the quality of a detection (cc_mad_ratio), and an indication about the origin as some of the detection's seem to be related to human induced activity (hum_ind). Detailed information about the template matching processing can be gained from <strong>Spatio-temporal evolution of the</strong><strong> Seismicity in the Alto Tiberina Fault System revealed by a High-Resolution Template Matching Catalog</strong> by Essing & Poli (2022)</p> <p> </p>
Ridgecrest, CA 2009-2019 microseismicity catalog
<p>Catalog of microseismicity between 2009-01-01 and 2019-07-04 in the Ridgecrest, California area (latitudes: 35.50 -- 36.00, longitudes: -117.80 -- -117.30). This catalog captures 10 years of seismic activity before the July 2019 Ridgecrest sequence, which started with a M6.4 on 2019-07-04 and was followed by a M7.1 on 2019-07-06.</p> <ul> <li><strong>v1.0.0</strong>: Earthquake catalog from "Enhanced Tidal Sensitivity of Seismicity Before the 2019 Magnitude 7.1 Ridgecrest, California Earthquake" (in press at Geophysical Research Letters). The catalog contains 191,569 events and was built with the BPMF workflow (v2.0.0alpha, <a href="https://github.com/ebeauce/Seismic_BPMF">https://github.com/ebeauce/Seismic_BPMF</a>). v1.0.0 is available at <a href="https://doi.org/10.5281/zenodo.8127880">https://doi.org/10.5281/zenodo.8127880</a>.</li> <li><strong>v2.0.0</strong>: Minorly updated catalog accompanying "BPMF: A BackProjection and Matched-Filtering Workflow for Automated Earthquake Detection and Location" (submitted to Seismological Research Letters). The catalog contains 183,269 events, slightly revised locations and magnitudes with respect to v1, and was built with the BPMF workflow (v2.0.0beta, <a href="https://github.com/ebeauce/Seismic_BPMF">https://github.com/ebeauce/Seismic_BPMF</a>).</li> <li><strong>v2.0.1</strong>: Removal of some regional (particularly at the edges of the study region) and teleseismic events and slightly updated event magnitudes. 155,794 events.</li> </ul> <p>The catalog is a table with 11 columns:</p> <ul> <li>event_id: a unique event identifier,</li> <li>origin_time: origin time, UTC, of the event,</li> <li>longitude: longitude in decimal system,</li> <li>latitude: latitude in decimal system,</li> <li>depth: depth in km,</li> <li>hmax_unc: maximum horizontal uncertainty in km (from uncertainty covariance ellipsoid),</li> <li>hmin_unc: minimum horizontal uncertainty in km (from uncertainty covariance ellipsoid),</li> <li>az_hmax_unc: azimuth (angle from north) of maximum horizontal uncertainty in degrees,</li> <li>vmax_unc: maximum vertical horizontal uncertainty in km (from uncertainty covariance ellipsoid),</li> <li>Mw: moment magnitude, !! this column is mostly empty since most events were not well enough recorded to estimate a moment magnitude !!</li> <li>Ml: local magnitude.</li> </ul>
Supplementary data and code to article "Single-Well Microseismic Focal Mechanism Inversions Using Different Source Models: A Case Study in the Ordos Basin, China"
<p>Supplementary data and code to article "Single-Well Microseismic Focal Mechanism Inversions Using Different Source Models: A Case Study in the Ordos Basin, China".</p><p>Transformations among the parameters of the moment tensor model refer to the code package from Tape and Tape (https://github.com/carltape/mtbeach/; https://github.com/carltape/surfacevel2strain; Tape and Tape, 2009, 2012, 2013, 2015).</p><p>Tape, C., P. Muse, M. Simons, D. Dong, and F. Webb (2009). Multiscale estimation of GPS velocity fields, Geophys. J. Int. 179, no.2, 945-971, doi: 10.1111/j.1365-246X.2009.04337.x.</p><p>Tape, W., and C. Tape (2012). A geometric setting for moment tensors, Geophys. J. Int. 190, no. 1, 476–498, doi: 10.1111/j.1365-246X.2012.05491.x.</p><p>Tape, W., and C. Tape (2013). The classical model for moment tensors, Geophys. J. Int. 195, no. 3, 1701–1720, doi: 10.1093/gji/ggt302.</p><p>Tape, W., and C. Tape (2015). A uniform parametrization of moment tensors, Geophys. J. Int. 202, no. 3, 2074–2081, doi: 10.1093/gji/ggv262.</p>
Datasets and Codes for "Relative Moment Tensor Inversion for Microseismicity: Application to Clustered Earthquakes in the Cascadia Forearc"
<p>This Zenodo record contains the supplementary datasets and code for the paper titled "Relative Moment Tensor Inversion for Microseismicity: Application to Clustered Earthquakes in the Cascadia Forearc."<br><br></p> <p><strong>Datasets</strong></p> <ul> <li>phases.txt<br> Contains phases used for the event location and moment tensor inversion.<br> Format: ID, station, phase, year, month, day, secday<br> ID: Event identifier (same for all files)<br> station: Station name<br> phase: 1 for P-wave or 2 for S-wave<br> secday: Seconds in the day</li> <li>polarity.txt<br> Contains first motion P polarity used in the study.<br> Format: ID, station, polarity, trust, type<br> polarity: 1 for up or -1 for down<br> trust: Value between 0 and 1, indicating confidence level.<br> type: E for emergent or I for impulsive<br> Note that the arrival type has been automatically assigned and not double-checked.</li> <li>relocation.txt<br> HypoDD relocation file (see hypoDD manual for full description).<br> Format: ID, LAT, LON, DEPTH, X, Y, Z, EX, EY, EZ, YR, MO, DY, HR, MI, SC, MAG, NCCP, NCCS, NCTP, NCTS, RCC, RCT, CID</li> <li>MT_soluton.txt<br> Contains all double-couple moment tensor solutions.<br> Format: ID, strike, dip, rake, mag, kagan_std<br> mag: Moment magnitude (Mw); "None" if the event is not considered stable<br> kagan_std: Quality interpretation of the moment tensors using Kagan angle standard deviation, as described in the main paper.</li> </ul> <p> </p> <p><strong>Codes</strong></p> <p>Future development of the relative moment tensor algorithm will be conducted on GitHub as part of the Marie-Sklodowska-Curie Action relMT funded by the European Union (https://github.com/wasjabloch/relMT)</p> <p>Here are the files in Codes.zip:</p> <ul> <li>synthetics.zip<br> Contains codes for performing and testing synthetic moment tensor inversion.</li> <li>relMT.zip<br> Contains the code for performing moment tensor inversion on real data.</li> <li>intrustion.txt<br> Contains instructions for setting up and running the codes.</li> <li>environment_MAC.yml<br> File to create the python environment on a MAC or LINUX machine.</li> <li>environment_WINDOWS.yml<br> File to create the python environment on a WINDOWS machine.</li> </ul>
Southern Alps, New Zealand microseismicity earthquake catalog
<p>Earthquake catalog of the microseismicity in the central Alpine Fault, New Zealand (2008 - 2017) in QuakeML format. The information included for each event contains location, phase pick, local magnitude information. For more details refer to the Gcubed publication in the related <a href="http://doi.org/10.1029/2018GC007743">link</a>.</p> <p> </p>
Microseismic source parameters from induced seismicity in the Horn river basin (British Columbia) [Dataset]
<p>This is a database of results linked to the associated manuscript (Klinger and Werner, 2021) which has been submitted to Geophysical Journal International and is currently in review. </p> <p>We report magnitudes, corner frequencies and stress drops of microseismic events linked to fault reactivation during hydro-fracturing operations in the Horn river basin (British Columbia), as well as the corresponding uncertainties for these parameters. Stress drops are calculated using a Brune model (Brune, 1970) and uncertainties are calculated using a bootstrapping technique. </p> <p>Prior to publication please cite this database using the following two references:</p> <p>Klinger, A.G., Werner, M.J. (2021). Stress drops of hydraulic fracturing induced microseismicity in the Horn River basin: Challenges at high frequencies recorded by borehole geophones. <em>Manuscript submitted to Geophysical Journal International . </em></p> <p>Klinger, A.G., Werner, M.J. (2021). Microseismic source parameters from induced seismicity in the Horn river basin (British Columbia) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5603835.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
A high-resolution earthquake catalog for the Tony Creek dual Microseismic Experiment (ToC2ME)
<p>Location/detection method: In this study, we revisit the continuous recordings that are acquired by 69 three-component nodes at a Hydraulic Fracturing (HF) site in Alberta, Canada, taking advantage of a machine learning-based seismic detection and location workflow (LOC-FLOW). This workflow integrates four key steps to generate earthquake catalogs, including phase picking using neural network (PhaseNet), rapid association of seismic phases (REAL), seismic location (e.g., VELEST) as well as relocation (e.g., hypoDD). The catalog contains 21,619 earthquakes with magnitudes down to -2 and achieves a high location accuracy at meter scale. The improved earthquake locations allow us to better delineate the fractures and faults activated during the HF. Our results further reveal the complexity of the triggering mechanism of HF-induced earthquakes according to the statistical analysis of the frequency-magnitude distribution of earthquakes. Our study suggests that multiple factors, including pore fluid pressure due to fluid injection and perturbation in the stress field caused by earthquakes, can jointly control the spatiotemporal variations of regional seismicity during the multi-stage HF operation. </p>
Microseismic catalogue from seismicity on the Jericho Fault (Dead Sea) [Dataset]
<p>This is a database of results linked to the associated manuscript (Klinger et al., 2024) which has been submitted to Geophysical Journal International and is currently in review. </p> <p>We report locations, magnitudes, timing and location errors for microseismic events linked to seismic acitivity on the Jericho Fault.</p> <p>Prior to publication please cite this database using the following two references:</p> <p>Klinger, A.G., Kurzon, I., Sagy, A. (2024). Microseismic and damage-zone characteristics of a fully locked fault segment on the Dead Sea Transform. <em>Manuscript submitted to Geophysical Journal International . </em></p> <p>Klinger, A.G., Werner, M.J. (2021). Microseismic catalogue from seismicity on the Jericho Fault (Dead Sea) [Data set]. Zenodo. https://zenodo.org/uploads/11653666.</p>
Data Availibility for "Susceptibility of microseismic triggering to small sinusoidal stress perturbations at the laboratory scale"
<p>List of data contained within this repository.</p> <p>Total weight = ~ 40GB</p> <p>--- Raw data :</p> <p> </p> <p>-For all experiments (control and oscillation experiments):</p> <p>Mechanical data files (460 MB);<br> Acoustic emission waveforms (40 GB).</p> <p>--- Derived data:</p> <p> </p> <p>-For the control experiment:</p> <p>RSFit3000 Rate and State parameters (30 MB);</p> <p> </p> <p>-For oscillation experiments:</p> <p>RMS magnitudes of acoustic emissions ; (1 MB)<br> Phase of arrival of acoustic emissions (500 KB);<br> Response amplitudes of fused and unfused steps (1 KB);<br> Susceptibility to Pc oscillation amplitude (1 KB);<br> b-values as a function of stress oscillation phase (100 KB).</p>
Data for microseismic wave attenuation calculation in two rock engineering
<p>Microseismic monitoring data for two rock engineering are included. These are the Hanjiang-to-Weihe River Diversion Project (HWRDP) in Shaanxi Province, China and the Yebatan Hydropower Station (YHS) which is located in the upper reaches of the Jinsha River at the junction of Sichuan Province and Tibet, China.</p> <p>The dataset consists of the raw waveform data used for attenuation calculations, as well as some necessary parameter datasheets, such as phase arrival time, sensor coordinates, etc.</p>
Microseismic Catalog for ToC2ME dataset
<p>Microseismic catalog for the Tony Creek dual Microseismic Experiment (ToC2ME) dataset, a passive microseismic dataset collected near Fox Creek, Alberta, Canada, in 2016. </p> <p>Number of events: 18040</p> <p>Location/detection method: Absolute locations based on P- and S-waveforms from 3C sensor. Method involves stacking waveforms along ray-tracing travel times within a volume. Includes all events that exceeded quality control criteria from a larger catalog of detected events from multi-channel 3D cross-correlation. High resolution in x and y, but with a large uncertainty in depth. Method developed by James Verdon at the University of Bristol. Magnitudes obtained from 3C geophones using the standard Brune formula. </p> <p>See https://github.com/ToC2ME for more information.</p>
Data Set: The influence of roughness on experimental fault mechanical behaviour and associated microseismicity
<p>This is the data set used to create the figures for the submitted manuscript, The influence of roughness on experimental fault mechanical behaviour and associated microseismicity. Submitted July 2022 to the Journal of Geophysical Research: Solid Earth. Article DOI: 10.1029/2022JB025113</p>
Deep Learning for Efficient Microseismic Location using Source Migration-based Imaging
<p>The uploaded .rar file contains data and network of mircoseismic location program.</p> <p>'network.py' : network based on U-Net. The input is diffraction stacking images with the size of 1*64*64*64*32. The output has the same size as the input.</p> <p>'Model_SeisLoca_Unet.hdf5' : The model of the network trained by 800 samples.</p> <p>'ValidationSample_SNR1.mat' is corresponding to the validation sample showed in paper, and it includes 'input', 'label', and 'pred'. The 'pred' can be obtained by running 'prediction.py' with correct setups.</p> <p>'TestSample_SNR0.5.mat' is corresponding to the test sample with the SNR equals 1/2 showed in paper, and it includes 'input', 'label', and 'pred'. The 'pred' can be obtained by running 'prediction.py' with correct setups.</p> <p>'Draw_Synthetic_input_label_prediction.m' is a script to draw the input, label, and prediction.</p>
microseismic dataset
<p>data —— train</p> <p>val_data —— predict</p>
Microseismic datasets
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Seismic dataset in "Source-Independent Passive Seismic Reverse-time Structure Imaging with Grouping Imaging Condition: Method and Application to Microseismic Events Induced by Hydraulic Fracturing"
<p>This dataset contains the seismic data and the velocity model used in the manuscript entitled "Source-Independent Passive Seismic Reverse-time Structure Imaging with Grouping Imaging Condition: Method and Application to Microseismic Events Induced by Hydraulic Fracturing" submitted to Journal of Geophysical Research-Solid Earth.</p>
Microseismic source imaging using physics-informed neural networks with hard constraints
<p>Locating subsurface seismic sources is crucial to both seismic monitoring and seismology. We propose a novel direct source imaging framework based on physics-informed neural networks with hard constraints. In this letter, we present the relevant dataset to the paper. </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)
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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.