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781 results for “earthquakes”
Data from: Global induced stress field from large earthquakes since 1900 and chained earthquake occurrence
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Fault geometry and slip distribution of the 2013 Mw 7.7 Balochistan earthquake from non-linear and linear inversions of SAR and optical data
<p>This archive contains data related to the paper “Fault geometry and slip distribution of the 2013 Mw 7.7 Balochistan earthquake from non-linear and linear inversions of SAR and optical data” by Benjamin Lauer, Raphael Grandin and Yann Klinger.</p>
Relocated Earthquake Catalog for Java-Indonesia (April 2009 to November 2018)
<p>Relocated Earthquake Catalog for Java (April 2009 to November 2018). </p> <p>Please refer to:</p> <p>Widiyantoro, S., Gunawan, E., Muhari, A., Rawlinson, N., Mori, J., Hanifa, N.R., Susilo, S., Supendi, P., Shiddiqi, H.A., Nugraha, A. D., Putra, H. E. (2020). Seismic gaps south of Java, Indonesia: Implications for megathrust earthquakes and tsunamis, Scientific Reports, 10, 15274 (2020). https://doi.org/10.1038/s41598-020-72142-z</p>
Earthquake Data around the Palu-Koro fault
<p>The earthquake data was used to simulate the stress change around the Palu-Koro fault in my paper submitted to GRL.</p>
Synthetic earthquake catalogs generated for "Dual seismic migration velocities in seismic swarms"
<p>Synthetic earthquake catalog for 40 simulations presented in "Dual seismic migration velocities in seismic swarms" by P. Dublanchet and L. De Barros, submitted to Geophysical Research Letters. Each file corresponds to one simulation described in the original paper. The three columns of each file are:</p> <p>-time of earthquake (in s)</p> <p>-location of earthquake (in m)</p> <p>-size of earthquake (in m)</p>
Coincident locations of rupture nucleation during the 2019 Le Teil earthquake, France and maximum stress change from local cement quarrying
<p>Earthquakes occurrence is controlled by tectonic stress load. However, it is now proved that geoengineering operations can contribute enough stress change to cause an earthquake. Industrial operations can produce either increase in the stress load and/or degradation of strength on nearby active faults, rising the potential for failure, or cause break on inactive faults that would have not moved instead. The 2019, M<sub>W</sub>=4.9, Le Teil (France) earthquake occurred in a relatively low strain rate area. Its location very close to a cement quarry, where significant mass removal has been continuously carried out for almost two centuries, raises questions about the possible link between stress change associated with rock extraction and occurrence of earthquakes. Here we show that the anthropic activity could have induced or at least triggered the Le Teil earthquake. In this latter case its occurrence could have been hastened by more than 100,000 years. Our results suggest that further mass removal in the area might induce even stronger earthquakes, by activating deeper sectors of the same fault plane.</p>
Probing fault frictional properties during afterslip up- and downdip of the 2017 Mw 7.3 Sarpol-e Zahab earthquake with space geodesy
<p>This zip file contains measurements of postseismic surface deformation due to the 2017 Mw 7.3 Sarpol-e Zahab earthquake derived from Sentinel-1 SAR data over a time period of one year after the mainshock. The line-of-sight (LOS) displacements of each satellite track are provided in high-resolution NetCDF file format.</p> <p># data format: NetCDF grid file</p> <p>dlos_yyyymmdd.grd ---- cumulative LOS displacement since the first postseismic acquisition of each track (unit: meters); <br> dates.dat ----- dates of the SAR image acquisitions of each track (ASCII file)<br> look_e/n/u.grd --- East, North, Vertical (Up) components of the line-of-sight direction, so that<br> <br> dlos_displacement = dUe*look_e + dUn*look_n + dUup*look_u</p> <p>where dUe, dUn, and dUup represent the Eastward, Northward, and Upward surface motion.</p> <p>Contact: Kang Wang (kjellywang@gmail.com or kwang@seismo.berkeley.edu)</p> <p>References:</p> <p>Wang, K and R. Bürgmann (2020), Probing fault frictional properties during afterslip up- and downdip of the 2017 Mw 7.3 Sarpol-e Zahab earthquake with space geodesy, Journal of Geophysical Research: Solid Earth<br> </p>
Data from: Characteristics of pneumonia deaths after earthquake and tsunami: an ecological study of 5.7 million subjects in 131 municipalities, Japan
Objective: On 11 March 2011, the Great East Japan Earthquake struck off Japan. Although some studies showed that the earthquake increased the risk of pneumonia death, no study reported whether and how much tsunami increased the risk. We examined the risk for pneumonia death after the earthquake/tsunami. Design: This is an ecological study. Setting: Data on population and pneumonia deaths obtained from the Vital Statistics 2010 and 2012, National Census 2010 and Basic Resident Register 2010 and 2012 in Japan. Participants: About 5.7 million subjects residing in Miyagi, Iwate and Fukushima Prefectures during 1 year after the disaster were targeted. All municipalities (n=131) were categorized into inland (n=93), that is, the earthquake-impacted area, and coastal types (n=38), that is, the earthquake- and tsunami-impacted area. Outcome measures: The number of pneumonia deaths per week was totaled from 12 March 2010 to 9 March 2012. The number of observed pneumonia deaths (O) and the sum of the sex- and age-classes in the observed population multiplied by the sex- and age-classes of expected pneumonia mortality (E) were calculated. Expected pneumonia mortality was the pneumonia mortality during the year before. Standardized mortality ratios (SMRs) were calculated for pneumonia deaths (O/E), adjusting for sex and age using the indirect method. SMRs were then calculated by coastal and inland municipalities. Results: Six thousand six hundred three subjects died of pneumonia during 1 year after the earthquake. SMRs significantly increased during the 1st to 12th week. In the 2nd week, SMRs in coastal and inland municipalities were 2.49 (95% CI 2.02 to 7.64) and 1.48 (95% CI 1.24 to 2.61), respectively. SMRs of coastal municipalities were higher than those of inland municipalities. Conclusions: Earthquake increased the risk of pneumonia death and tsunamis additionally increased the risk.
Data from: Evolution of stickleback in 50 years on earthquake-uplifted islands
How rapidly can animal populations in the wild evolve when faced with sudden environmental shifts? Uplift during the 1964 Great Alaska Earthquake abruptly created freshwater ponds on multiple islands in Prince William Sound and the Gulf of Alaska. In the short time since the earthquake, the phenotypes of resident freshwater threespine stickleback fish on at least three of these islands have changed dramatically from their oceanic ancestors. To test the hypothesis that these freshwater populations were derived from oceanic ancestors only 50 y ago, we generated over 130,000 single-nucleotide polymorphism genotypes from more than 1,000 individuals using restriction site-associated DNA sequencing (RAD-seq). Population genomic analyses of these data support the hypothesis of recent and repeated, independent colonization of freshwater habitats by oceanic ancestors. We find evidence of recurrent gene flow between oceanic and freshwater ecotypes where they co-occur. Our data implicate natural selection in phenotypic diversification and support the hypothesis that the metapopulation organization of this species helps maintain a large pool of genetic variation that can be redeployed rapidly when oceanic stickleback colonize freshwater environments. We find that the freshwater populations, despite population genetic analyses clearly supporting their young age, have diverged phenotypically from oceanic ancestors to nearly the same extent as populations that were likely founded thousands of years ago. Our results support the intriguing hypothesis that most stickleback evolution in fresh water occurs within the first few decades after invasion of a novel environment.
Integrated Earthquake Catalog III: Gakkel Ridge, Knipovich Ridge and Svalbard Archipelago
<p>Integrated Earthquake Catalog III: Gakkel Ridge, Knipovich Ridge and Svalbard Archipelago</p>
PyCOMPSs Probabilistic Tsunami Forecast (PTF) - Kos-Bodrum 2017 earthquake and tsunami test-case results
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Digital Appendix: The Size Distributions of Faults and Earthquakes: Implications for Orogen-Internal Seismic Deformation [Dataset]
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Calibration of Nonlinear Rheology Parameters in the Salt Lake Basin from the March 18, M5.7 Magna, Utah, Earthquake Sequence Records
<p>Simulated time series and scripts to extract intensity measures described in the U.S. Geological Survey (USGS) technical report (under USGS award number G22AP00033).</p> <p>The simulation results are located "<a href="../api/records/10892430/draft/files/time-series-all.zip/content" target="_blank" rel="noopener noreferrer">time-series-all.zip</a>" with folder names defined as in the table-of-contents shown in the additional descriptions, all waveform data are in ASCII format, where the first column is time in seconds from the initial time of the event, the second column is velocity in m/s. The python scripts used to calculate intensity measurements based on the time series are located in "<a href="../api/records/10892430/draft/files/itensity-measures-calculation.zip/content" target="_blank" rel="noopener noreferrer">itensity-measures-calculation.zip</a>", with package requirements listed within each script.</p>
Challenges of Interpreting Geodetic Deformation in Low Strain Rate Regions: Insights from the 2021 Mw 7.4 Maduo Earthquake
<p>This repository contains the datasets from the supporting information for the manuscript:</p> <p>Rutland, C., Bie, L., Johnson, J.H., Ou, Q. & Mildon, Z.K. (2025). "Challenges of Interpreting Geodetic Deformation in Low Strain Rate Regions: Insights from the 2021 Mw 7.4 Maduo Earthquake."</p> <p>Data Set S1. V E derived from InSAR data 2015/05/28–2021/05/20.<br>Data Set S2. σV E derived from InSAR data 2015/05/28–2021/05/20.<br>Data Set S3. ε̇ shear derived from InSAR data 2015/05/28–2021/05/20.<br>Data Set S4. V E derived from InSAR data 2015/05/28–2017/05/17.<br>Data Set S5. σV E derived from InSAR data 2015/05/28–2017/05/17.<br>Data Set S6. ε̇ shear derived from InSAR data 2015/05/28–2017/05/17.<br>Data Set S7. V E derived from InSAR data 2016/05/10–2018/05/12.<br>Data Set S8. σV E derived from InSAR data 2016/05/10–2018/05/12.<br>Data Set S9. ε̇ shear derived from InSAR data 2016/05/10–2018/05/12.<br>Data Set S10. V E derived from InSAR data 2017/05/29–2019/05/19.<br>Data Set S11. σV E derived from InSAR data 2017/05/29–2019/05/19.<br>Data Set S12. ε̇ shear derived from InSAR data 2017/05/29–2019/05/19.<br>Data Set S13. V E derived from InSAR data 2018/05/24–2020/05/25.<br>Data Set S14. σV E derived from InSAR data 2018/05/24–2020/05/25.<br>Data Set S15. ε̇ shear derived from InSAR data 2018/05/24–2020/05/25.<br>Data Set S16. V E derived from InSAR data 2019/05/31–2021/05/20.<br>Data Set S17. σV E derived from InSAR data 2019/05/31–2021/05/20.<br>Data Set S18. ε̇ shear derived from InSAR data 2019/05/31–2021/05/20.<br>Data Set S19. V E derived from InSAR data 2015/05/28–2018/05/12.<br>Data Set S20. σV E derived from InSAR data 2015/05/28–2018/05/12.<br>Data Set S21. ε̇ shear derived from InSAR data 2015/05/28–2018/05/12.<br>Data Set S22. V E derived from InSAR data 2016/05/10–2019/05/19.<br>Data Set S23. σV E derived from InSAR data 2016/05/10–2019/05/19.<br>Data Set S24. ε̇ shear derived from InSAR data 2016/05/10–2019/05/19.<br>Data Set S25. V E derived from InSAR data 2017/05/29–2020/05/25.<br>5Data Set S26. σV E derived from InSAR data 2017/05/29–2020/05/25.<br>Data Set S27. ε̇ shear derived from InSAR data 2017/05/29–2020/05/25.<br>Data Set S28. V E derived from InSAR data 2018/05/24–2021/05/20.<br>Data Set S29. σV E derived from InSAR data 2018/05/24–2021/05/20.<br>Data Set S30. ε̇ shear derived from InSAR data 2018/05/24–2021/05/20.</p> <p> </p>
Nucleation process of laboratory earthquakes on a submeter fault under different normal stresses
<p>We conducted friction experiments on a 669 mm long granodiorite simulated fault to study the detailed spatiotemporal evolutions of nucleation zones (characterized by the strain-releasing zones) of stick-slip events under four different normal stresses via a dense array of strain rosettes (with spacing of 21 mm between the adjacent rosettes and offset distance of 8 mm from the fault). Cumulative shear strain relative to the strain at 1 s prior to the stick-slip instability is calculated for each strain rosette. By doing this, the strain-releasing zone can be detected in the spatiotemporal evolution of the cumulative shear strain. We present a typical pattern of the nucleation process under each normal stress. Under the normal stresses of 7.5 and 10 MPa, a quasi-static expanding nucleation zone initiates at <em>x </em>~ -80 mm and expands slowly and then shrinks to a critical length (referred to as the contraction nucleation length, <em>L<sub>s</sub></em>) before it merges into a subsequently accelerated expanding nucleation zone. The accelerated expanding nucleation zone initiates in the middle of the fault (<em>x </em>~ 0) near the outer edge of the quasi-static expanding nucleation zone, which first expands bilaterally at a speed of the order of m/s and then gradually accelerates towards the fault ends. When the length of the accelerated nucleation zone expands to another critical length (referred to as the ultimate nucleation length, <em>L<sub>c</sub></em>), the dynamic rupture initiates (<em>t</em> = 0). Under normal stresses of 2.5 and 5.0 MPa, the similar accelerated expanding nucleation zone is also detected but the quasi-static expanding nucleation zone is unobservable. Moreover, <em>L<sub>s</sub></em> increases but <em>L<sub>c</sub></em> decreases with increasing normal stress.</p>
A new Insight into the Sources of the 1733DC-M7.8 Earthquake on the Xiaojiang Fault zone, southeastern Tibet
<p>The observed near-fault GPS data of northern Xiaojiang fault.</p>
2005-2019 Campi Flegrei earthquake locations in GIS format
<p>2005-2019 Campi Flegrei earthquake location dataset in GIS format.</p> <p>This is the database used for the paper "GIS applications in volcano monitoring: the study of seismic swarms at the Campi Flegrei volcanic complex, Italy" by Bellucci Sessa, et al. 2021 (https://doi.org/10.5194/adgeo-52-131-2021)</p> <p> </p>
Dynamic rupture simulation of the 1833 Songming, Yunnan, China, M 8.0 earthquake: Effects from stepover location and overlap distance
<p>These datasets accompany the western Xiaojiang fault surface coordinates that were used for construction of the fault geometry model, the extrapolated velocity model from Shen et al. (2015), and the observational fault surface slip of the 1833 Songming M 8.0 earthquake in Dynamic rupture simulation of the 1833 Songming, Yunnan, China, M 8.0 earthquake: Effects from stepover location and overlap distance submitted to Earth and Space Science by Yu et al. (2021). The data is structured as follows:</p> <p> </p> <p>Once unzipped the data within the archive are three folders structured as follows: subfolder "extrapolated velocity model" is for the extrapolated velocity model, "fault_coordinates" is for the western Xiaojiang fault surface coordinates and "fault_slip" is for the observational fault surface slip. Inside the first folder, there is one NetCDF file, volume_media.nc. The variable x, y, depth starts from 0 km, 0 km, and 4 km, and ends at 200.70 km, 333.96 km, and -40.52 km; x = 0, y = 0 corresponding to 102°, 24°; x = 200.70, y = 333.96 corresponding to 104°, 26.5°. Inside the second folder, there are three txt files, "wxj_conti.txt" is for the continuous western Xiaojiang fault model, "wxj_qshso.txt" is for the western Xiaojiang fault model with Qingshuihai stepover structure, and "wxj_yzhso.tx" is for the western Xiaojiang fault model with Qingshuihai and Yangzonghai stepover structures. Inside the third folder, there is one txt file, 'fault_surface_slip.txt' is for the observational fault surface slip of the 1833 Songming earthquake with the coordinates.</p>
Dataset for "Investigating multiple observations around a seismic gap during the Lushan earthquake in Sichuan, China"
<p>"MsCatalog.csv" includes the catalog used in the paper.</p> <p>"gnss2013" includes the daily horizontal surface displacement (E and N components) in the South China Block frame in 2013.</p> <p>“2013.tar.gz” includes the Empirical Green's functions (EGF) of all 208 station pairs in 2013, Sichuan, which were derived from the continuous seismic waveforms (ambient noise). </p>
The down-sampled displacements and source models of the 2021 Mw7.0 Maduo earthquake
<p>1)The down-sampled LOS displacements from Sentinel-1 and ALOS-2 satellite; 2) the distributed slip source models of the 2021 Mw7.0 Maduo earthquake.</p>
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