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20 results for “seismic monitoring”
Seismic monitoring of Hans glacier (Svalbard) using dedicated local network
<p>Seismic dataset registered during monitoring of Hans glacier (Svalbard) using dedicated local network in Hornsund 10/2017-04/2018 carried by Wojciech Gajek and coworkers financed by an internal grant of Institute of Geophysics Polish Academy of Sciences.</p> <p>Dataset can be used for analyzing the glacier seismicity. More on that topic in Svalbard can be find in Seismology chapter of SESS 2019 report <a href="https://sios-svalbard.org/SESS_Issue2">https://sios-svalbard.org/SESS_Issue2</a></p> <p>Project log in ResearchGate:</p> <p><a href="https://www.researchgate.net/project/Seismic-monitoring-of-Hans-glacier-Svalbard-using-dedicated-local-network">https://www.researchgate.net/project/Seismic-monitoring-of-Hans-glacier-Svalbard-using-dedicated-local-network</a></p> <p> </p> <p>The data includes seismic records (3C) from the temporary seismic network. It is advised to take into the processing also the permanent station HSPB.<br> Data is packed as a zip archive. Its structure is SDS, compatible with ObsPy query system.<br> The structure includes HSPB but HSPB data is not there due to limited file space here (its publicly available eg in Orpheus).</p> <p> </p> <p>Other files are:<br> coordinates,<br> map<br> data availability chart<br> my presentation from ESC Malta with preliminary results<br> photos from field installation<br> data conditioning report</p> <p>Have fun.</p> <p>You can contact me via researchgate:</p> <p><a href="https://www.researchgate.net/profile/Wojciech_Gajek">https://www.researchgate.net/profile/Wojciech_Gajek</a></p>
Dataset of "Triggering and propagation of exogeneous sediment pulses in mountain channels: insights from flume experiments with seismic monitoring"
<p>Dataset of "Triggering and propagation of exogeneous sediment pulses in mountain channels: insights from flume experiments with seismic monitoring".</p>
Dataset associated with article: Self-sufficient seismic boxes for monitoring glacier seismology in Greenland
<p>Dataset associated with article: Self-sufficient seismic boxes for monitoring glacier3 seismology in Greenland</p> <p>Contains:<br> - Seismic data of both SG-boxes and regular geophones from Gornergletscher fieldtest, 2021( Seismic_Data_Gorner_Fieldtest.zip) <br> --> SG-box data naming: GO"station_number"SG <br> --> Geophone data naming: GO"station_number"GP<br> <br> - Weather data Gornergletscher fieldtest from Monte Rosa, Meteo Swiss Weather station ( Weather_data_Gorner_Fieldtest_2021.zip) <br> --> 1hr wind averages <br> --> 1hr temperature averages<br> <br> - MSR logger data from SG-boxes from Gornergletscher fieldtest, 2021. Every 5 min these log battery power, tilt (along three axes, temperature and humidity inside the box and light strength on two sides of the SG-box. ( MSR_logger_data_SGboxes_Gorner_Fieldtest.zip )<br> <br> - Seismic data of SG box (Sensor code BSM) next to weather station first acquisition Greenland 2021 ( Seismic_Data_SG_Box_first_acquisition_Greenland_2021.zip) <br> --> .pri0 is East component, .pri1 is North component, .pri2 is Vertical component.<br> <br> - Weather data from weather station next to SG-box (sensor code BSM) during first acquisition Greenland 2021 ( Weather_station_data_Greenland_2021.zip) <br> --> The weather station logs a value every two hours.</p> <p> </p>
Towards automated early detection of risks for a CO2 plume containment from permanent seismic monitoring data
<p>This storage contains the training data for neural networks proposed in a manuscript 'Towards automated early detection of risks for a CO2 plume containment from permanent seismic monitoring data'. The data consists of output from reservoir simulations of a small-scale CO2 injection at CO2CRC Otway Project Stage 2C (Victoria, Australia). The output is presented as a set of images, where each pixel in a portable network graphics is a plume thickness for a particular injection scenario at a particular day after the injection has commenced. The format is unsigned integer 16-bit. The data set contains images of two major types:</p> <p>1. REALISTIC: plumes are obtained from reservoir simulations in a complex geological model that was calibrated on an extensive set of geophysical surveys. File naming follows this convention 'plume_thick_real_scenario_%S_day_%N.png', where %S represents a string that encodes the injection scenario name and %N denotes day number after the injection started.</p> <p>2. VANILLA: plumes are obtained from reservoir simulations in a simple model of a reservoir that reflects only few typical features of the Otway injection interval. 'plume_thick_vanilla_scenario_%S_day_%N.png', where %S represents a string that encodes the injection scenario name and %N denotes day number after the injection started.</p>
Dataset for the seismically monitored experiments of free-fall granular masses
<p>Datasets related to the paper "Experiments on Landquakes Generated by Free-fall Granular Masses: Implications for Rockfall Impacting Dynamics", submitted to <em>Earth and Space Science</em>.</p> <p>S1_images_the dynamic evolution of the free-fall granular masses tracked by a high-speed camera.</p> <p>S2_data_ data of vertical acceleration signals for all tests recorded by an accelerometer.</p> <p>S3_data_ data of the extracted seismic parameters including maximum seismic amplitude, mean frequency and radiated seismic energy for all tests.</p> <p>S4_data_ data supporting the relationships between intermediate functions associated with the maximum seismic amplitude, mean frequency and seismic energy and number of particles.</p> <p>S5_data_ data supporting the successive velocity profiles and acceleration profiles along the vertical direction of the granular mass.</p> <p>S6_data_ data supporting the relationships between the ratio of components in a granular mass contributing to the maximum seismic amplitude with the number of layers.</p> <p>S7_data_data supporting the relationships between intermediate coefficients associated with the maximum seismic amplitude, mean frequency and seismic energy and the number of layers of granular masses.</p> <p>S8_data_ data of spectrogram of condition D5 computed using Stockwell transform based on the acceleration signals recorded by an accelerometer.</p> <p>S9_data_ data supporting the evolutions of horizontal and vertical motion components of granular masses of tests C1-C5 over time.</p> <p>S10_data_ data supporting the relationships between characteristic frequency and the velocity vector of granular masses of series C, D, E and F.</p>
Data for Seismic Noise and Subsurface Velocity Characterization for a Unique Bedload Monitoring Observatory in a Dryland Ephemeral Channel
<p>Seismic dataset used in submitted manuscript "Seismic Noise and Subsurface Velocity Characterization for a Unique Bedload Monitoring Observatory in a Dryland Ephemeral Channel".</p>
Quantitatively Monitoring of Seasonal Frozen Ground Freeze-thaw Cycle Using Ambient Seismic Noise Data
<p>This is the electronic supplemental data for the publication entitled </p> <p>"<strong>Quantitatively Monitoring of Seasonal Frozen Ground Freeze-thaw Cycle Using Ambient Seismic Noise Data</strong>"</p> <p>submitted to <strong>Seismological Research Letters (SRL)</strong>. </p> <p>The names of the compressed files represent the experiment number and station number. For example, "1_2" indicates data collected from the second station during the first experiment. Each compressed file contains seismic raw data in the ".SAC" format. The filenames include the UTC end time of data collection. For instance, "453003616.00000001.2021.10.20.06.40.22.000.z.sac" indicates that data collection ended at 06:40:22 on October 20, 2021. Each complete .sac file contains 4 days of data with a sampling interval of 0.002 seconds.</p>
Monitoring water content variations from seismic noise in a controlled laboratory experiment: PART 2 [Dataset]
<p>This dataset has been obtained with an original laboratory experiment aimed at assessing the sensitivity of passive seismic interferometry imaging (PII) to controlled fluctuations in water content. Multiple controlled cycles of water imbibition and draining at the base of the sandbox produce significant variations in the seismic wavefield and especially in dominant surface waves. PART 2: seismic measurements from 1501 to 2700minutes</p>
Monitoring water content variations from seismic noise in a controlled laboratory experiment: PART 1 [Dataset]
<p>This dataset has been obtained with an original laboratory experiment aimed at assessing the sensitivity of passive seismic interferometry imaging (PII) to controlled fluctuations in water content. Multiple controlled cycles of water imbibition and draining at the base of the sandbox produce significant variations in the seismic wavefield and especially in dominant surface waves. PART 1: seismic measurements from 0001 to 1500minutes</p>
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>
Grond reports for the seismic moment tensor inversions done for "The January 2022 Hunga Volcano explosive eruption from the multi-technological perspective of CTBT monitoring"
<p>This are the Grond reports of the seismic moment tensor inversion done for the manuscript submitted to GJI titled:</p> <p>"The January 2022 Hunga Volcano explosive eruption from the multi-technological perspective of CTBT monitoring"</p> <p>You can view the summary figures of the inversions in the subfolders for each event manually if you wish.</p> <p>However to view the reports interactively you need to have the pyrocko and grond softwares installed. See here for installation instruction for pyrocko: https://pyrocko.org/ and here for grond https://pyrocko.org/grond/docs/current/</p> <p>After correct installation you can view the reports in any browser by executing the command "grond report --so" in the folder which contains the unpacked "report" folder.</p>
Using Seismic Noise Levels to Monitor Social Isolation: An Example from Rio de Janeiro, Brazil
<p>Paper</p> <p>Dias, F.L., M. Assumpção, P.S. Peixoto, M.B. Bianchi, B. Collaço, J. Calhau (2020), <strong>Using Seismic Noise Levels to Monitor Social Isolation: </strong><strong>An Example from Rio de Janeiro, Brazil. </strong><em>Geophysical Research Letters.</em></p> <p>1) Daily median particle velocity amplitudes for station ON.ON02. Each amplitude is the median of all values between 09 AM - 6 PM local time.</p> <p>median_VEL_4_14_Hz.csv = median daily velocity (m/s) in the 4-14Hz frequency band.</p> <p>median_VEL_8_14_Hz.csv = median daily velocity (m/s) in the 8-14Hz frequency band. Data used in Fig. 2.</p> <p>median_VEL_4_8_Hz.csv = median daily velocity (m/s) in the 4-8Hz frequency band. Data used in Fig. 4.</p> <p>The raw data from station ON.ON02 can be retrieved from the RSBR (Brazilian Seismic Network) database at www.rsbr.gov.br</p> <p>2) <em>In Loco</em> Isolation index for Rio de Janeiro city. Data provided by <em>In Loco</em> company on 2020 April 22.</p> <p>InLoco_IsolationIndex_RioDeJaneiroCity.txt = date and <em>Ik</em> index for Rio de Janeiro city.</p> <p>Data used in Figs. 2, 4 and 5.</p>
Daily cross-correlation functions for "Time-lapse monitoring of seismic velocity associated with 2011 Shinmoe-dake eruption using seismic interferometry: an extended Kalman filter approach"
<p>The daily cross-correlation functions used in Nishida et al. 2020. We used three-component seismograms recorded at eight stations (six broadband sensors and two short-period sensors with a natural frequency of 1 Hz) from May 1st, 2010 to April 30th, 2018. Five stations were deployed by the Earthquake Research Institute, the University of Tokyo, and the other three were deployed by the National Research Institute for Earth Science and Disaster Prevention (NIED). The data can be found in the HDF5 file. You can also find a python code of an implementation of an extended Kalman filter/smoother for time-lapse monitoring of seismic velocity at GitHub (https://github.com/qnishida/eKlfS). The code estimates the temporal change in seismic velocities using this data set. </p>
Data series of seismic events for the article "Seismic monitoring using the telecom fiber network"
<p>The file "catalog.h5" contains the catalog of seismic events analyzed in the paper "Seismic monitoring using the telecom fiber network" by S. Donadello et al., Commun Earth Environ 5, 178 (2024) <a href="https://doi.org/10.1038/s43247-024-01338-2">https://doi.org/10.1038/s43247-024-01338-2</a> (formerly "Earthquake observatory with coherent laser interferometry on the telecom fiber network" on arXiv preprint).</p> <p>The reported data correspond to raw recordings, acquired by coherent interferometry techniques on a telecommunication fiber (see also <a href="doi.org/10.1109/TIM.2023.3288255">https://doi.org/10.1109/TIM.2023.3288255</a>), and decimated to a lower sampling rate.</p> <p>The catalog is organized as about 900 seismic events in the period between June 19th, 2021 and Sept. 26th, 2022, between Feb. 6th and March 23th 2023, and between Nov. 9th, 2022 and Nov. 18th, 2022, according to the criteria described in the paper.</p> <p>A detailed description of the ".h5" file format is provided in "h5_file_description.txt".</p> <p>A Python3 script "h5_cat_parser.py" for data interpretation in terms of standard python structures is provided.</p>
Monitoring of Seismic Geodynamics of the Earth's Crust of Central Armenia_DATASET
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A physical model for mean river discharge calculation: from riverside seismic monitoring experiments in a low-flow river, China
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Monitoring Spatiotemporal Seismic Velocity Changes Using Seismic Interferometry and Distributed Acoustic Sensing in Mexico City
<h2>Cross-Correlation Functions (CCFs) Data Files</h2> <p>The dataset consists of four zipped files:</p> <ul> <li> <p><strong>cc_25hz_das1_Freq_0.40-1.20hz.zip</strong></p> <ul> <li> <p>Contains CCFs using DAS fiber-1, sampled at 25 Hz, for the frequency range of 0.40–1.20 Hz.</p> </li> </ul> </li> <li> <p><strong>cc_25hz_das1_Freq_1.20-3.60hz.zip</strong></p> <ul> <li>Contains CCFs using DAS fiber-1, sampled at 25 Hz, for the frequency range of 1.20–3.60 Hz.</li> </ul> </li> <li> <p><strong>cc_25hz_das2_Freq_0.40-1.20hz.zip</strong></p> <ul> <li> <p>Contains CCFs using DAS fiber-2, sampled at 25 Hz, for the frequency range of 0.40–1.20 Hz.</p> </li> </ul> </li> <li> <p><strong>cc_25hz_das2_Freq_1.20-3.60hz.zip</strong></p> <ul> <li>Contains CCFs using DAS fiber-2, sampled at 25 Hz, for the frequency range of 1.20–3.60 Hz.</li> </ul> </li> </ul> <h4> </h4>
Earthquake catalog in QuakeML format from: "Local earthquake monitoring with a low-cost seismic network: a case study in Nepal"
<p>Earthquake catalog of the microseismicity in central Nepal recorded by a Low cost seismic network(Raspberry Shake) in 2021 in QuakeML format. The information included for each event contains location, phase pick, local magnitude information. </p>
Supporting Data for "Seismic Monitoring of Rockfalls Using Fiber-Optic Distributed Acoustic Sensing"
<p>This file contains data and MATLAB scripts to generate the figures presented in the manuscript "Seismic Monitoring of Rockfalls Using Fiber-Optic Distributed Acoustic Sensing".</p>
Waveform data for 'Seismic Footprints Monitoring and Trajectory Tracking of Moving Aircrafts'
<p>Data of waveform and flights for 'Seismic Footprints Monitoring and Trajectory Tracking of Moving Aircrafts'</p>
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