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19 results for “seismic imaging”
Time-lapse electrical resistivity tomography and seismic reflection imaging of a shallow ground-water aquifer (0-50 m): Mississippi River levee seepage across the Duncan Point bar, Baton Rouge, Louisiana, U.S.A.
<p>The electrical resisitivity raw data files are slightly processed to remove bad data points but can be inverted using tomographic inversion code. </p> <p>The seismic data were assembled in Seismic Unix format, a shortened version of the SEG-Y format (Society of Exploration Geophysicists Exchange Format-Y https: //seg. org/Publications/SEG-Technical-Standards), that has the 3200-byte EBCDIC and 400-byte tape header removed. The data uploaded online (<a href="https://zenodo.org/records/14776025">https://zenodo.org/records/14776025</a>) is a CMP brute-stacked seismic section. </p> <p>During data collection, shotpoint location changed proceeding along a 136-degree azimuth (south-easterly direction), and spaced every 1 m.</p> <p>A total of 48, horizontal-component 28-Hz nominal geophones were placed every one meter and shotpoints were located half-way between geophones. Geophones remained fixed at their locations throughout the survey and so the CMP spacing is nominally 0.5-m but fold varies linearly from a value of 1 from either side of the survey to a central maximum of 24. The seismic source consisted of a partially buried 20-lb steel I-beam struck repeatedly on either side three times by an 8-lb sledge hammer. Data of the same striking polarity were added in-phase in the field. Data with opposing polarity at each shotpoint location were subtracted later to enhance SH-wave data and suppress converted SH-to-P waves.</p> <p>Seismic processing is minimal and consists of standard surface-wave muting, elimination of bad seismic traces, normal moveout, bandpass filtering (between 12 Hz and 50 Hz) and preliminary stacking with trace mixing every 3 CMPs. The data were stacked with a single velocity throughout that ranged from 80 m/s (Vs) at 0.2 s, to 100 m/s at 0.35 s and reached 180 m/s at 0.5 s of two-way traveltime.</p> <p> </p>
LASSO coherent seismic wavefield reconstruction and source imaging
<p>Coherent wavefield reconstruction and source imaging has been performed for 4 cataloged seismic events recorded with the Large-N Seismic Survey in Oklahoma (LASSO). The array consists of almost 2,000 densely spaced seismic stations and the corresponding raw time sries data have been made freely accessible by the Incorporated Research Institutions for Seismology (IRIS). The results for the 4 seismic events are accompanied with results gained for controlled seismic simulations for two of these events Reconstruction results and source images are provided in HDF5 and MAT file formats, respectively. File names were giving according to the following pattern: <br> <br> "LASSO_<<em>event name>_<reconstruction mode>_<result type>"</em></p> <p>where <<em>reconstruction mode</em>> refers either to "enhancement" (reconstruction performed for the original station layout) or "regularization" (reconstruction perfomed for a new, sense and regular station layout). <<em>result type</em>> denotes either reconstructed waveforms ("wavefield"), waveform coherence ("coherence"), or spatial source images. For the HDF5 files, mportant meta information like spatial coordinates and temporal sampling parameters are stored in a symbolic dictionary named "META", whereas the time series data is saved as a 2D matrix. Important META fields include "ntrac" (number of traces), "nt" (number of time samples), "dt" (dampling interval), "gx" (stations x coordinates), "gy" (stations y coordinates).<br> <br> The MAT files (result type "images") contain raw waveform and STA/LTA images, which are stored as 3D regular arrays named "recm1z_Enh_5_raw" (enhancement) / "recm1z_Reg5_5_raw" (regularization) and "recm1z_Enh_5_slta" (enhancement) / "recm1z_Reg5_5_slta" (regularization), respectively. For comparison, source images generated for the raw field data (without reconstruction are included in every MAT file and can be accessed through fields "recm1z_Raw_raw" and "recm1z_Raw_slta".</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>
Seismic Imaging and Identification of Cold Seep Plumes in the Shenhu Area, Northern Continental Slope of the South China Sea
<p>The upload <a href="https://zenodo.org/api/records/14786925/draft/files/pstm_final.dat/content" target="_blank" rel="noopener noreferrer">pstm_final.dat</a> is the final pre-stacked time migration section which is used in our paper. <a href="https://zenodo.org/uploads/14786925" target="_blank" rel="noopener noreferrer">read_dat_file.m</a> is a MATLAB script, which is used to read dat file and plot the image. </p>
Seismicity patterns and multi-scale imaging of Krafla (N-E) Iceland with local earthquake tomography: Raw event waveforms for all events used in the inversion and manual picks for temporary network
<p>This Data and Software were used in the submitted paper "Seismicity patterns and multi-scale imaging at Krafla (N-E Iceland) wih local earthquake tomography" by Glück et al.<br>The data and software provided here are used to compute the velocity models with TomoTV.<br>The raw data (.mseed format) can be visualised with the Python package Pyrocko/Snuffler, which was also used for the arrival time picking.<br>For the temporary network the manual picks are provided along with the code to prepare the manual picks as the input files for a localisation with NonLinLoc by weighting and quality checking the data. This resulting localsitations and the weighted traveltimes are then used for the LET.<br>The same workflow was used for the picks from the permanent network.</p> <p>Data:<br>- Raw data (\WaveformsPermanentStations): 7s waveform snippets of the events listed in the ISOR catalogue on http://lv.isor.is:8080/events/browse/ for the years 2021 and 2022.<br>- Raw data (\WaveformsNodes): 5s waveform snippets of the events listed in the ISOR catalogue on http://lv.isor.is:8080/events/browse/2022 recorded with the temporary network of 98 temporary nodes in June and July 2022.<br>- Pickfile (ManualPicks_100Nodes_Kafla2022.txt): Manual picks of the events listed in the ISOR catalogue for the evenst recorded with the temporary network.<br>- Station file (Station_file.txt): The station file includes the coordinates (Lat, Lon, Elevation) of the permanent stations (StationID starting with K...) and of the temporary nodes (StationID starting with N...).</p> <p>Software (Hyp_format.py):<br>- Weighting: The picks are weighted according to their Signal-to-Noise ratio (described in more detail in Section 2.3 in the main text of the paper)<br>- Writing the inputfile for NonLinLoc (with the selecting the mode option "PorS" in line 118), including all picks, also for those stations where not both phases were picked. The file "endfile.txt" is needed to write the picks to the NonLinLoc input format.<br>- Quality check of the picks: Computing a modified Wadati diagram from the traveltime differences of P and S phases for all the events available (with the selecting the mode option "PandS" in line 118)<br>- Python packages needed: numpy, scipy, matplotlib, pandas, obspy</p>
Seismic acquisition parameters to improve imaging beneath mafic igneous units: Case study from Australia's Northwest Shelf; supplementary material
<p>This dataset comprises two supplementary materials. Supplementary Materials A includes seismic processing workflows conducted by industry on the seismic lines used in this study. The seismic processing workflows are not the property of the author but are publicly available on the NOPIMS and WAPIMS databases. Collating these workflows into supplementary materials provides a simple method for readers to access material important for this research paper. Supplementary Materials B is a collection of 2D seismic lines the author conducted stratigraphic horizon mapping on for this study as viewed in 3D. More details on this dataset can be found throughout the research paper "Seismic acquisition parameters to improve imaging beneath mafic igneous units: Case study from Australia’s Northwest Shelf".</p>
Imaging subsurface structure of an urban area based on Diffuse-Field Theory concept using seismic ambient noise
<p>Ambient noise data for a small urban area of NER India. The data set constitute all the raw files that were used for figures in the paper by Bora et al.</p>
Kirchhoff pre-stack depth migration images of the multi-channel seismic data, SO190, RV. SONNE
<p>The dataset consists of four newly processed 2-D pre-stack depth migrated multi-channel seismic lines (BGR06_303, BGR06_305, BGR06_311 and BGR06_313) collected by GEOMAR and BGR in 2006. The dataset reveals the subducted oceanic reliefs and detailed accretionary wedge structure offshore eastern Java, Bali, Lombok, and Sumbawa islands, along the Sunda arc. The dataset is saved in standard SEGY format and could be loaded in open-source or commercial software. </p>
Seismically imaged mantle exhumation in the Southwest Taiwan Basin of the northeastern South China Sea margin
<p>Dataset for submission of Geochemistry, Geophysics, Geosystems.</p>
Micro-seismic and image dataset acquired at Matterhorn Hörnligrat, Switzerland
<p>This dataset contains annotated micro-seismic recordings and images acquired in the context of project X-Sense2 (financed by nano-tera.ch, ref. no. 530659) at the Matterhorn Hörnligrat fieldsite on 3500 m a.s.l.. Additionally, secondary data is provided which can used for further analysis.</p> <p>This dataset comprises a selection of measurements. The <em>data</em> folder contains two datasets and additional data (secondary data). Please refer to the following publication for further information:</p> <p> </p> <p>Meyer, M., Weber, S., Beutel, J., Thiele, L.: Systematic Identification of External Influences in Multi-Year Micro-Seismic Recordings Using Convolutional Neural Networks, Earth Surface Dynamics, in review, 2018.</p> <p> </p> <p><strong>dataset</strong></p> <p>The <em>dataset/</em> folder contains micro-seismic data, images, and annotations.</p> <p>A list of every datapoint including annotations and start/end time is provided in two csv files in the <em>annotations/</em> folder.</p> <p> </p> <p><strong>event_dataset</strong></p> <p>The <em>event_dataset/</em> folder contains micro-seismic data and annotations.</p> <p>A list of every datapoint including annotations and start/end time is provided in two csv files in the <em>annotations/</em> folder. For a selected time period bounding boxes are provided in the <em>bounding_box.csv</em> file.</p> <p> </p> <p><strong>Micro-seismic data</strong></p> <p>The data was recorded using a Nanometrics Centaur digital recorder and Lennartz electronic low-noise seismometer LE-3Dlite MKIII (1−100 Hz). The Nanometrics Centaur digital recorder aquires data with 24-bit resolution with a sampling rate of 1000 sps. The data is stored in .miniseed-format.</p> <p> </p> <p><strong>Image data</strong></p> <p>Images where acquired with a remote controlled high-resolution camera (Nikon D300, 24 mm fixed focus).</p> <p> </p> <p><strong>Annotations</strong></p> <p>The data was annotated manually by the authors.</p> <p> </p> <p><strong>Secondary data</strong></p> <p>The secondary data comprises data from different sensors, including wind speed, rock temperature and radiation. Additionally, weekly aggregated hut occupancy of the Hörnlihut from the years 2016/2017 and event timestamps from running a STA/LTA trigger on an internal version of the micro-seismic data are provided.</p>
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>
Derived seismic data products from paper: "Seismic Imaging of the Reykjanes Peninsular, Iceland: Crustal-scale context of geothermal areas and ongoing volcano-tectonic unrest"
<p>Receiver Functions (RF) and Dispersion Curves derived from seismic stations throughout the Reykjanes Peninsular SW Iceland. </p> <p>RF are in Smurfpy python3 PICKLE format - details on format and scripts to read in here: https://github.com/sannecottaar/smurfpy. Folder <strong> </strong>has the following file structure:</p> <p><strong>RF_Data_Zenodo</strong> --> contains station folders: <strong>Network.Station</strong> --> contains folders <strong>goodRF_crust2</strong> and <strong>goodRF_crust6 </strong>where 2 and 6 refer to Gaussian pulse widths used to produce RF via iterative deconvolution --> contains RF files in format <strong><em>NETWORK.STATION_BAZ_EPIDIST_EVTIME.PICKLE</em></strong> and subfolders of identified highly similar waveform subsets in the format <strong>BAZ_NNN_NNN </strong>used in data inversions<strong><br></strong></p> <p>Dispersion curves are in Fast Marching Surfacewave Tomography FMST input format (Rawlinson and Sambridge, 2005), details on format in FMST manual: https://iearth.edu.au/codes/FMST/instructions.pdf. Folder <strong>FMST_Dispersion_Inputs_Zenodo </strong>contains the following files:</p> <ul> <li>otimes0.25Hz.dat</li> <li>otimes0.2Hz.dat</li> <li>otimes0.35Hz.dat </li> <li>otimes0.3Hz.dat</li> <li>otimes0.45Hz.dat</li> <li>otimes0.4Hz.dat</li> <li>otimes0.5Hz.dat</li> <li>sources.dat </li> <li>receivers.dat</li> </ul> <p> </p> <p><em>References</em>:</p> <div>Rawlinson, Nick. "FMST: fast marching surface tomography package–Instructions." <em>Research School of Earth Sciences, Australian National University, Canberra</em> 29 (2005): 47.</div>
Dataset for "Fiber-Seismometer Hybrid Sensing for Seismic Imaging and Monitoring"
<p>The ambient noise dataset was collected on May 8, 2023, from a fiber-seismometer hybrid sensing deployment positioned along the Qiantang River in Hangzhou. The DAS data, Z-component and R-component data of seismometers are all stored in MAT format. Please refer to our study for detailed information on the dataset.</p> <p>Abstract about this study:</p> <p>Extreme climate events and geological disasters have intensified the urgency for advancing seismic imaging and monitoring. Despite developments in seismic instrumentation, particularly with seismometers and Distributed Acoustic Sensing (DAS), fine-scale observations remain challenging due to their inherent limitations and deployment configurations. This study introduces a novel hybrid sensing interferometry method that enhances multi-component signal extraction—especially poor horizontal components—through a two-step cross-correlation of DAS and seismometers. A field application near the Qiantang River in Hangzhou illustrates how our proposed framework retrieves high-quality multi-component empirical Green’s functions and advances ultra-short duration ambient noise seismic imaging techniques, including surface wave dispersion measurements and horizontal-to-vertical spectral ratio assessments. Our approach also facilitates monitoring of near-surface seismic velocity changes, dv/v, with an unprecedented 10-minute resolution, shedding light on shallow dynamic hydraulic responses. This innovative hybrid sensing framework offers new perspectives and methodologies for transforming future research in seismological observation, imaging, and monitoring. </p>
Fine seismic imaging of the Lianhuashan fault zone, South China, and tectonic implications – Constrained by ambient noise adjoint tomography
<p>The Rayleigh wave group velocity dispersion and the cross-correlation functions used in the study "Fine seismic imaging of the Lianhuashan fault zone, South China, and tectonic implications – Constrained by ambient noise adjoint tomography".</p>
Supplementary Dataset for the Report "Imaging the Western Edge of the Aegean Shear Zone: The South Evia 2022-2023 Seismic Sequence"
<p>Supplementary Dataset for the Fast Report "<strong>Imaging the Western Edge of the Aegean Shear Zone: The South Evia 2022-2023 Seismic Sequence</strong>" by Christos P. Evangelidis and Ioannis Fountoulakis.</p> <p>The Fast Report can be found in <em><strong>Seismica Vol. 2 No. 1 (2023)</strong></em>.</p> <p><strong>DOI:</strong> <a href="https://doi.org/10.26443/seismica.v2i1.1032">https://doi.org/10.26443/seismica.v2i1.1032</a></p> <p><em><strong>Abstract:</strong></em></p> <p>This report presents the 2022-2023 South Evia island seismic sequence, in the western Aegean sea. An automated workflow, undergoing testing for efficient observatory monitoring in the wake of dense aftershock sequences, was employed to enhance the seismic catalog. It includes a deep-learning phase picker, absolute and relative hypocenter relocation, and moment tensor automatic calculations. The relocated catalog reveals a concentration of earthquake epicenters in a narrow NW-SE zone, with sinistral strike-slip fault movement. The findings of the study indicate the occurrence of an asymmetric rupture within conjugate fault structures in the western Aegean region. These fault structures, although not necessarily both active, play a significant role in marking the transition from dextral (SW-NE) to sinistral (NW-SE) strike-slip ruptures, connecting the Aegean shear zone with normal faulting in mainland Greece. The South Evia 2022-2023 seismic sequence has revealed the activation of this NW-SE strike-slip structure, contrary to previous assumptions of low seismicity in the region. The study highlights the importance of reassessing seismic hazard maps and considering the potential activation of similar zones further south in the future. It also emphasizes the need for the expansion and the densification of seismic networks within the Aegean.</p> <p><em><strong>Files:</strong></em></p> <p><strong>Relocated_Catalog.txt</strong>: Relocated earthquake catalog for the South Evia sequence. Included in the document are details concerning the origin time, the hypocentral positions, errors in determining the location, and the local magnitude of each earthquake.</p>
Imaging seismic and aseismic plate coupling with interferometric radar (InSAR) in the Hikurangi subduction zone
<p>Data associated with 'Imaging seismic and aseismic plate coupling with interferometric radar (InSAR) in the Hikurangi subduction zone published in GRL.</p>
Passive seismic imaging of near vertical structures around the SAFOD site, California, jointly using scattered P and SH waves
<p>1, The P and S velocity model are in the velocity folder which written by binary format (float). The size of the velocity model is 101*101*56(Nx Ny Nz) with a 200 meter grid. The upleft corner is (-10km -10km -1km). The unit of the velocity is km/s.</p> <p>2, The stations and events are in the geometry folder:</p> <p>the format of the file is</p> <p>Number of the stations/events</p> <p>Name of the stations/events, X(km), Y(km), Z(km). </p> <p>the origin of the coordinates system is the bole hole SAFOD, and the local coordinates was rotated by 40.5 degree.</p> <p>The name of the events was the origin time (precise to the minute) of the events.</p> <p>For example:</p> <p>the 1227042400 indicates that </p> <p>Year JulianDay hour minute second</p> <p>2001 227 04 24 00</p> <p>You can get the details and download the waveform at the website https://www.fdsn.org/networks/detail/XN_2000/ and https://www.fdsn.org/networks/detail/BP/</p> <p>2.1) the "events_3.7km_56" includes 56 events that was at the same depth~3.7km, The common station (The TANK Station) gather was shown in figure 6. </p> <p>2.2) "events_all" indicates 560 events that shown in Figure 4, and there are about 10 events not in the SAF, we did not use them, the "events_rm_outlier" are the 546 events we lastly used.</p> <p>2.3) "stations.dat.65" is the combination of the PASO and HRSN that in 20km square area as shown in Figure1.</p>
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>
Multi-channel seismic reflection profiles MP06b and INS-Line1 (INSIGHT cruises) and diffraction imaging code
<p>This archive contains sections of multi-channel seismic reflection profiles MP06b and INS-Line1 (INSIGHT cruises) (pre-stack gathers, unmigrated water velocity stacks and migration velocities in SEG-Y format) and an archive containing code and processing horizons needed to generate conventional and diffraction images. Requires Madagascar 3.0, Python 3.7+, pdflatex. Please contact the author (Jonathan Ford, jford@inogs.it) if assistance is required to run the code.</p> <p> </p>
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