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8 results for “Damaging earthquake”
A Composite Catalog of Damaging Earthquakes for Mainland China
<p>The Mainland China Composite Damaging Earthquake Catalog (MCCDE-CAT) was developed by Li et al. (2021). It contains three databases: Earthquake damage database, Intensity map database, Population exposure database, which for 493 damaging earthquakes that occurred in Mainland China during 1950-2019. Citation: "Y. Li, Z. Zhang, D. Xin, A Composite Catalog of Damaging Earthquakes for Mainland China, Seismol. Res. Lett. 92(6) (2021) 3767-3777. https://doi.org/10.1785/0220210090"</p> <p> </p>
Building damage assessment of Adassil, Morocco after 2023 Marrakesh–Safi earthquake
<p>The dataset contains shapefile of damaged and non damaged buildings of Adassil town, Chichaoua Province of the Marrakesh-Safi region of Morocco. The dataset has been created using openly available google buildings dataset, humanitarian open street data, MAXAR satellite data and segment-geospatial package. </p>
Data for: Historical earthquake scenarios for the middle strand of the North Anatolian Fault deduced from archeo-damage inventory and building deformation modeling
<p>This dataset is associated to the article "Historical earthquake scenarios for the middle strand of the North Anatolian Fault deduced from archeo-damage inventory and building deformation modeling " published in Seismological Research Letters (<a href="https://pubs.geoscienceworld.org/ssa/srl/article-abstract/doi/10.1785/0220200278/592607/Historical-Earthquake-Scenarios-for-the-Middle?">link</a>).</p> <p>It includes the following:</p> <ul> <li>The annotated photographs of the EAE (Earthquake Archeological Effects) inventoried in Iznik ("EAE_xxx.pdf").</li> <li>The 3D displacement signals used as input for obelisk modeling ("Displacement_xxx").</li> <li>The output obelisk displacement curves and final block shift values relative to base ("Obelisk_block_motion.pdf").</li> </ul>
Damage to RC Structures and Intensity Measures from the 2023 Türkiye Earthquake
<p>This database is associated with the following paper:</p> <p><strong>Hariri-Ardebili, M.A., and Sattar, S. (2024).</strong> <em>Data-Driven Insights into Post-Earthquake Reconnaissance Findings: 2023 Türkiye Earthquake Sequence</em>, Earthquake Spectra.</p> <p>The database contains information on over 240 reinforced concrete structures surveyed after the 2023 Türkiye earthquake sequence. The data is divided into two main categories: (1) building-related information and observed damage, and (2) various ground motion intensity measures from different shocks and aftershocks, calculated using either the Kriging method or the USGS ShakeMap.</p> <p>Part (1) of the database was primarily developed as part of the ACI Committee 133 initiative and was also presented in the following paper. However, several missing data points were supplemented by the developers of this new database based on further investigation of the individual buildings and engineering judgment:</p> <p><strong>Pujol et al. (2024).</strong> <em>Quantitative Evaluation of the Damage to RC Buildings Caused by the 2023 Southeast Turkey Earthquake Sequence</em>, Earthquake Spectra.</p>
M180 Data for "Laboratory Earthquakes Simulations – Typical Events, Fault Damage, and Gouge Production"
<p>M180 Data for "Laboratory Earthquakes Simulations – Typical Events, Fault Damage, and Gouge Production"</p>
7.8 Earthquake Damage Mapping in Turkey and Syria
<p>In the early hours of February 6th, 2023, a devastating earthquake of magnitude 7.8 in Gaziantep and magnitude 7.5 in Ekinözü struck Turkey and Syria, causing widespread loss of life and property. In the aftermath of the quake, more than 36,000 fatalities and numerous injuries were reported in one week. The unprecedented magnitude of the earthquake highlights the delicate nature of human life, yet it also underscores the resilience of the human spirit.</p> <p>To assist in the aftermath of this disaster, the present study aims to assess the damage caused by the earthquake through the analysis of Sentinel-1 satellite data. We adopt a combination of coherence and displacement parameters to create a damage map of the affected areas. First, we calculate the difference in coherence between pre- and post-earthquake images and retain only pixels with a coherence value higher than 0.25 and a negative change in coherence. The resulting change in coherence is expressed as a percentage (diff_coherence_percent) after converting it to a percentage by multiplying it by 100.</p> <p>Next, we enhance positive displacement values and take the absolute value of all displacement values, expressing the magnitude of displacement as a percentage (disp_percent) by dividing it by a threshold of 0.9 m and multiplying it by 100. Finally, we compute the damage percentage as the weighted average of both “diff_coherence_percent” and “disp_percent”, with equal weighting.</p> <p>The results of our study can provide valuable insights into the impact of the earthquake and help guide recovery efforts by providing a map of the damage caused by the quake. Advanced radar technology and satellite imagery highlight the importance of utilizing cutting-edge tools in studying natural disasters and their impacts on communities.</p>
Dataset to support "Explore the potential of using social media crowdsourcing for earthquake damage assessment"
<p>This dataset is used to support findings in the paper "<strong>Explore the potential of using social media crowdsourcing for earthquake damage assessment.</strong>"</p> <p>In this paper, we investigated six earthquake events to explore the potential of using social media crowdsourcing for earthquake damage assessment. These cases include the 2014 Iquique earthquake, the 2015 Napa earthquake, the 2015 Nepal earthquake, the 2019 Ridgecrest earthquake, the 2021 Fukushima earthquake, and the 2021 Haiti earthquake. For the first three cases, tweet ID information was collected from the publicly available website CrisisNLP (https://crisisnlp.qcri.org/). For the last three cases, we collected “near-real-time” tweets with the key search term “earthquake.” For the 2019 Ridgecrest earthquake, the search date range was from July 4 to 10, 2019. For the 2021 Fukushima earthquake, the search date range was February 13 to 17, 2021. For the 2021 Haiti earthquake, the search date range was August 14 to 19, 2021. It should be noted that restrictions apply to the availability of tweet data due to the policy from <em>Twitter</em> Inc. Tweet data showing user profiles or text content is not allowed to be shared publicly, but tweet IDs are available to download from this dataset. </p> <p>In addition, we attached our processed results to this dataset, including,</p> <ol> <li>Damage estimation. The damage estimation for each event is based on the average estimated level of all damage-related tweets identified by text classification models, as documented in our paper.</li> <li>Temporal dynamics of damage estimation. We conducted the temporal analysis to explore the convergence of damage estimates over time, in which data were first binned based on the number of damage-related tweets in each 2-hour interval.</li> </ol> <p>If you need the training and testing datasets or have any further questions regarding the dataset or paper, please do not hesitate to reach us. </p>
Complexities of Extremely Shallow Damaging Earthquakes as Evidence for Seismogenic Intersecting Faults in Low Seismicity Regions Unloaded by Erosion
<p>The Seismic data used in this paper for inversion Centroid Moment Tensor and First Motion Mechanism</p>
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