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17 results for “3d seismic”
Enhanced 3D velocity structure, seismicity relocation and basement characterization of Changning shale gas and salt mining regions in Sichuan Basin
<p>This repository contains the datasets and results of the joint inversion-based Vp/Vs model consistency constrained double difference seismic tomography carried out for the manuscript titled “Enhanced 3D velocity structure, seismicity relocation and basement characterization of Changning shale gas and salt mining regions in Sichuan Basin.” Included are the following: column descriptions of data files, catalog earthquake information (CX_event.dat), relocated events after inversion (CX_tomoDDMC.reloc), inverted Vp model (CX_Vpmodel.dat), inverted Vs model (CX_Vsmodel.dat) and inverted Vp/Vs model (CX_VpVsmodel.dat). Please consult the manual for tomoDD by Zhang and Thurber (2003) for detailed formats of these files. In addition, an averaged velocity model (MOD_averaged) computed based on the inversion results is included, and the converged model (Vp_model_reinverted) resulting from the reinversion, as well as basement structure data for Figure 14.</p>
Adele 3D seismic survey segy format used in the FORCE 2020 machine learning competition for fault identification
<p>Adele seismic 3D survey segy format used in the FORCE 2020 machine learning competition for fault identification.</p> <p>Dataset is courtesy of GEOSCIENCE Australia who need to be acknowledged in each publication</p> <p> </p>
Experimental Seismic Data Obtained Using a 3D-Printed Model of the Los Angeles Basin Structure
<p>These data were obtained and analyzed by Park et al., (2022) "Seismic wave simulation using a 3D printed model of the Los Angeles Basin" (doi:10.1038/s41598-022-08732-w).</p> <p> </p>
cigKast: A data of 3D synthetic seismic volumes with labeled paleokarsts for deep-learning-based paleokarst interpretation
<p>cigKarst is a dataset created by the <a href="http://cig.ustc.edu.cn/">Computational Interpretation Group (CIG)</a> for the deep-learning-based peleokarst interpretation in 3D seismic images, <a href="http://cig.ustc.edu.cn/xinming/list.htm" target="_blank" rel="noopener">Xinming Wu</a> is the main contributor to the dataset.</p> <p>This dataset contains 120 pairs of synthetic 3D seismic images and the corresponding label images with the ground truth of the paleokarst systems simulated in the seismic images. More detail of building this dataset is discussed in the paper published at the journal of JGR Solid Earth:</p> <p><strong>Wu, X.</strong>, S. Yan, J. Qi, and H. Zeng, 2020, Deep learning for characterizing paleokarst collapse features in 3D seismic images. <strong>JGR, Solid Earth</strong>, Vol. 125(9), 1-23, e2020JB019685. <a href="http://cig.ustc.edu.cn/_upload/tpl/05/cd/1485/template1485/papers/wu2020karst.pdf">[PDF]</a>. doi: 10.1029/2020JB019685</p> <p>Below are some brief description of the dataset:</p> <p>1) The "seismic.zip" contains 120 3D seismic images, each image is with the dimension of 256X256X256;</p> <p> 2) The "karst.zip" contains 120 3D label images of the karsts. Each label image is with the same dimension of 256X256X256. The values in a label image are set with ones in the karst areas while zeros elsewhere, which is why the compressed label images in the karst.zip is much smaller than the seismic images compressed in the seismic.zip</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>
Semi-Supervised Pre-trained Foundation Model for 3D Structural Feature Analysis of Seismic Images
<p>Codes, trained model, and datasets for the paper "Semi-Supervised Pre-trained Foundation Model for 3D Structural Feature Analysis of Seismic Images".</p>
Data products for "3D modeling of long-term slow slip events along the flat-slab segment in the Guerrero Seismic Gap, Mexico"
<p>Data products for '3D modeling of long-term slow slip events along the flat-slab segment in the Guerrero Seismic Gap, Mexico' by A. Perez-Silva, D. Li, A.-A. Gabriel and Y. Kaneko</p>
Experimental dataset on basal stresses and seismic signals generated by granular flows moving on a 3D-printed bumpy substrate
<p>This dataset provides the data supporting the experimental study of granular flows moving on a 3D-printed bumpy substrate considering the response of basal stresses and seismic signatures.</p> <p>S1_video clips_the side-view of the kinematic behaviors of the granular flows tracked by a high-speed camera.</p> <p>S2_data_stress and seismic signals measured at the instrumented plate</p> <p>S3_data_example of velocity fields downstream and normal to the base<br>S4_data_propagation features of the flow characterized by basal stresses and seismic signals<br>S5_data_variations of the depth-averaged velocity of the granular flows and corresponding nondimensionalized downslope velocity profiles<br>S6_data_profiles of the velocity of the granular flows normal to the base along with their depth-averaged velocities in the basal layers<br>S7_data_ nondimensionalized shear rates of the granular flows for various particle diameters <br>S8_data_depth-averaged shear rates and corresponding inertial numbers for granular flows with different particle sizes<br>S9_data_the mean normal stress and shear stress and stress fluctuations normal and tangential to the base<br>S10_data_characteristics of seismic signals in terms of peak amplitude, mean envelope, seismic deviation factor, and the signal mean frequency<br>S11_data_effective basal friction coefficient and equivalent friction coefficient as functions of particle diameter<br>S12_data_relationships between nondimensionalized normal stress fluctuations and nondimensionalized basal vertical velocity, inertial number, effective basal friction coefficient and equivalent friction coefficient of the flows.<br>S13_data_seismic deviation factor as functions of nondimensionalized normal stress fluctuations, inertial number, effective basal friction coefficient and equivalent friction coefficient</p> <p>S14_data_Comparison of effective basal friction coefficient μ_b and that scaled by the nondimensionalized basal vertical velocity.</p>
Data for Estimation of 3D Moho depths beneath Southern Indian Shield by inverting seismic constraint gravity anomalies
<p>This is a help file for a description of all Data used for the implementation of our present paper<br> 'Estimation of 3D Moho depths beneath Southern Indian Shield by inverting seismic constraint gravity anomalies.' </p> <p> </p>
The High-resolution 3D QP Model of the China Seismic Experiment Site
<p><span>The CSES-Q1.0 is the highest resolution 3D <em>Q</em><sub>P</sub> model in the CSES to date.The first column of the file represents longitude, the second column represents latitude, the third column represents depth, and the fourth column represents QP values.</span></p>
3D spatial distribution of the seismic Gutenberg-Richter parameters (a and b values) in Long Valley Caldera
<p>3D distribution of the <em>a</em> and <em>b</em> parameters of the Gutenberg-Richter (GR) law. The analysis is presented in Gola G., Barone A., Castaldo R., Chiodini G., D'Auria L., Garcia-Hernandez R., Pepe S., Solaro G. & Tizzani P. "A novel multidisciplinary approach for the thermo-rheological study of volcanic areas: The case study of Long Valley Caldera", which has been submitted for possible publication in Journal of Geophysical Research - Solid Earth. For this purpose, we used a dataset consisting of more than 220,000 earthquakes from the NCEDC catalogue (NCEDC, 2014), spanning the interval 1978-2017 and located in the surroundings of Long Valley Caldera. We mapped the <em>a</em> and <em>b</em> values for a volume of 50×50×30 km<sup>3</sup>, over a grid of points having a regular spacing of 0.5 km.</p> <p>Coordinates: North America NAD27 UTM Zone 11N<br> Easting: 310000 - 350000 m.; Northing: 4150000 - 4185000 m</p> <p> </p>
cigChannel: A large-scale 3D seismic dataset with labeled paleochannels for advancing deep learning in seismic interpretation
<h2>Assorted channel subset of the cigChannel dataset (V1.0)</h2> <h2>Expansion package of folded and faulted structures</h2> <div> <p>This version includes the last 100 samples (400 samples in total) of the assorted channel subset. Each sample features folded and faulted structures. Explanations of the uploaded zip files are listed below: </p> </div> <ul> <li>Assorted_Channel_Ip_xx-xx.zip: Seismic impedance volumes of sample No.xx to No.xx.</li> <li>Assorted_Channel_Label_xx-xx.zip: Binary-class label volumes of sample No.xx to No.xx, where the value 0 represents the background (non-channel areas) and 1 represents the channel.</li> <li>Assorted_Channel_Seismic_xx-xx.zip: Seismic (amplitude) volumes of sample No.xx to No.xx.</li> </ul> <h3>Portal to the assorted channel subset (main package, the first 300 samples):</h3> <ul> <li>Assorted channel subset: <a href="https://doi.org/10.5281/zenodo.11044512" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11044512</a></li> </ul> <h3>Portals to the other subsets:</h3> <ul> <li>Submarine canyon subset: <a href="https://doi.org/10.5281/zenodo.11079950" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11079950</a></li> <li>Meandering channel subset: <a href="https://doi.org/10.5281/zenodo.11078794" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11078794</a></li> <li>Tributary channel network subset: <a href="https://doi.org/10.5281/zenodo.11073030" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11073030</a></li> </ul>
Netherlands F3 block and New Zealand Kerry-3D seismic attributes
<p>The dataset comprises sub-volumes of seismic attributes, which include structural smoothed seismic data, chaos, variance, semblance, similarity, and the most positive curvature from the F3 block and Kerry-3D seismic data. The selected sub-volumes are from regions with a high occurrence of faults.</p>
Harbour porpoise (Phocoena phocoena) reaction to a 3D seismic airgun survey in the North Sea
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The Magnetotelluric data, 3D resistivity model, and the reprocessed Seismic data for Timmins, Canada
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Model files and output seismograms for Seismic Wavefield Modelling of Enceladus: challenges and opportunities presented by a 3D ice shell (Dapré & Irving)
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Dataset and 3D Vs Model for "Crustal velocity images of north-western Türkiye along the North Anatolian Fault Zone from transdimensional Bayesian ambient seismic noise tomography"
<p>Final 3D Vs model and dispersion data for the paper entitled "Crustal velocity images of north-western Türkiye along the North Anatolian Fault Zone from transdimensional Bayesian ambient seismic noise tomography".</p> <p>In the vel_files folder, there are 10 files for each depth for 1-15 km. The format of each velocity file is as follows:</p> <p>Column Value<br> 1 Lattitude (°)<br> 2 Longitude (°)<br> 3 Vs (km/s)</p> <p>The format of the dispersion data is as follows (See <a href="https://www.eas.slu.edu/eqc/eqc_cps/TUTORIAL/EMPIRICAL_GREEN/example1.html">Computer Programs in Seismology Tutorials - do_mft</a> for more information on the format):</p> <p>Column Value<br> 1 Type of file, MFT96<br> 2 Wave type: R for Rayleigh <br> 3 Dispersion type: U for group velocity<br> 4 Mode: 0 represents the fundamental mode<br> 5 Filter period, T, in seconds<br> 6 Dispersion value, either group or phase<br> 7 Error in dispersion. This is just a place holder since there is no way to estimate an error from a single trace. The group velocity error is determined from the ratio of the filter period to travel time<br> 8 Distance in km<br> 9 Azimuth from the source to the receiver<br> 10 Spectral amplitude. <br> 11 Epicenter latitude <br> 12 Epicenter longitude<br> 13 Station latitude<br> 14 Station longitude<br> 15 control flag<br> 16 control flag<br> 17 Instantaneous period if this is preferred. This differs from the ilter period because the signal spectram is not flat.<br> 18 Comment: keyword<br> 19 Station <br> 20 Component<br> 21 Year<br> 22 Day of year<br> 23 Hour<br> 24 Minute - these identify the event origin time </p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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