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Field reconnaissance data from GEER investigation of the 2018 M(W) 7.5 Palu-Donggala earthquake

The M(w)7.5 Palu-Donggala earthquake occurred on 28 September 2018 and caused significant damage in Palu City and the surrounding Central Sulawesi region of Indonesia. The earthquake initiated a series of catastrophic landslides (classified as flowslides) [1,2], collapsed buildings, and generated ts...

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Autores principales: Montgomery, Jack, Wartman, Joesph, Reed, A. Nicole, Gallant, Aaron P., Hutabarat, Daniel, Mason, H. Benjamin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7829142/
https://www.ncbi.nlm.nih.gov/pubmed/33532524
http://dx.doi.org/10.1016/j.dib.2021.106742
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author Montgomery, Jack
Wartman, Joesph
Reed, A. Nicole
Gallant, Aaron P.
Hutabarat, Daniel
Mason, H. Benjamin
author_facet Montgomery, Jack
Wartman, Joesph
Reed, A. Nicole
Gallant, Aaron P.
Hutabarat, Daniel
Mason, H. Benjamin
author_sort Montgomery, Jack
collection PubMed
description The M(w)7.5 Palu-Donggala earthquake occurred on 28 September 2018 and caused significant damage in Palu City and the surrounding Central Sulawesi region of Indonesia. The earthquake initiated a series of catastrophic landslides (classified as flowslides) [1,2], collapsed buildings, and generated tsunami waves that impacted Palu Bay's coast. The earthquake claimed over 4000 lives, making it the deadliest natural disaster of 2018. We performed a post-earthquake field reconnaissance and collected perishable data at the sites of five significant flowslides (named for the communities where they occurred: Balaroa, Petobo, Lolu Village, Jono Oge, and Sibalaya), as well as at other damage locations in the mesoseismal region. Our field team consisted of five U.S.-based members, who were sponsored by the U.S. National Science Foundation-supported Geotechnical Extreme Events Reconnaissance (GEER) organization [3], in collaboration with geologists, geotechnical engineers, and other researchers from Indonesia's Center for Earthquake Studies (PusGen) and the Indonesian Society of Geotechnical Engineers (HATTI) [this international team is collectively referred to as the Palu Earthquake “;GEER” team]. The GEER team arrived at Palu City on 13 November 2018 and conducted five days of extensive fieldwork using instrumentation from the Natural Hazards Reconnaissance Facility (known as the “RAPID”) [4,5], including mobile data collection software, digital imaging systems, high-resolution Global Navigation Satellite System (GNSS) antennas, and unmanned aerial vehicles (UAVs, or “;drones”). The resulting dataset includes over 2000 geotagged photographs, UAV images, ground coordinates, and other field measurements and observations, as well as associated post-processed geospatial data products (point clouds, digital surface models, orthomosaic images). Additionally, we used remote sensing data (i.e., pre- and post-event satellite imagery) to generate displacement vectors for over 1200 structures affected by the flowslides. The complete reconnaissance dataset is openly available on DesignSafe [6]. The data collected by the field team and subsequent mapping efforts, which document the morphology and patterns of movements of the flowslides, may be used by researchers studying liquefaction-induced flowslides. In addition, the displacement mapping provides a unique dataset for researchers who are calibrating and verifying simulation models of landslide displacements, or who are seeking a validation dataset for image correlation analysis (including machine learning routines). This dataset is associated with original research presented in “;East Palu Valley Flowslides Induced by the 2018 M(W) 7.5 Palu-Donggala Earthquake” [1] and also is the basis of research presented by Gallant et al. [2].
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spelling pubmed-78291422021-02-01 Field reconnaissance data from GEER investigation of the 2018 M(W) 7.5 Palu-Donggala earthquake Montgomery, Jack Wartman, Joesph Reed, A. Nicole Gallant, Aaron P. Hutabarat, Daniel Mason, H. Benjamin Data Brief Data Article The M(w)7.5 Palu-Donggala earthquake occurred on 28 September 2018 and caused significant damage in Palu City and the surrounding Central Sulawesi region of Indonesia. The earthquake initiated a series of catastrophic landslides (classified as flowslides) [1,2], collapsed buildings, and generated tsunami waves that impacted Palu Bay's coast. The earthquake claimed over 4000 lives, making it the deadliest natural disaster of 2018. We performed a post-earthquake field reconnaissance and collected perishable data at the sites of five significant flowslides (named for the communities where they occurred: Balaroa, Petobo, Lolu Village, Jono Oge, and Sibalaya), as well as at other damage locations in the mesoseismal region. Our field team consisted of five U.S.-based members, who were sponsored by the U.S. National Science Foundation-supported Geotechnical Extreme Events Reconnaissance (GEER) organization [3], in collaboration with geologists, geotechnical engineers, and other researchers from Indonesia's Center for Earthquake Studies (PusGen) and the Indonesian Society of Geotechnical Engineers (HATTI) [this international team is collectively referred to as the Palu Earthquake “;GEER” team]. The GEER team arrived at Palu City on 13 November 2018 and conducted five days of extensive fieldwork using instrumentation from the Natural Hazards Reconnaissance Facility (known as the “RAPID”) [4,5], including mobile data collection software, digital imaging systems, high-resolution Global Navigation Satellite System (GNSS) antennas, and unmanned aerial vehicles (UAVs, or “;drones”). The resulting dataset includes over 2000 geotagged photographs, UAV images, ground coordinates, and other field measurements and observations, as well as associated post-processed geospatial data products (point clouds, digital surface models, orthomosaic images). Additionally, we used remote sensing data (i.e., pre- and post-event satellite imagery) to generate displacement vectors for over 1200 structures affected by the flowslides. The complete reconnaissance dataset is openly available on DesignSafe [6]. The data collected by the field team and subsequent mapping efforts, which document the morphology and patterns of movements of the flowslides, may be used by researchers studying liquefaction-induced flowslides. In addition, the displacement mapping provides a unique dataset for researchers who are calibrating and verifying simulation models of landslide displacements, or who are seeking a validation dataset for image correlation analysis (including machine learning routines). This dataset is associated with original research presented in “;East Palu Valley Flowslides Induced by the 2018 M(W) 7.5 Palu-Donggala Earthquake” [1] and also is the basis of research presented by Gallant et al. [2]. Elsevier 2021-01-15 /pmc/articles/PMC7829142/ /pubmed/33532524 http://dx.doi.org/10.1016/j.dib.2021.106742 Text en © 2021 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Montgomery, Jack
Wartman, Joesph
Reed, A. Nicole
Gallant, Aaron P.
Hutabarat, Daniel
Mason, H. Benjamin
Field reconnaissance data from GEER investigation of the 2018 M(W) 7.5 Palu-Donggala earthquake
title Field reconnaissance data from GEER investigation of the 2018 M(W) 7.5 Palu-Donggala earthquake
title_full Field reconnaissance data from GEER investigation of the 2018 M(W) 7.5 Palu-Donggala earthquake
title_fullStr Field reconnaissance data from GEER investigation of the 2018 M(W) 7.5 Palu-Donggala earthquake
title_full_unstemmed Field reconnaissance data from GEER investigation of the 2018 M(W) 7.5 Palu-Donggala earthquake
title_short Field reconnaissance data from GEER investigation of the 2018 M(W) 7.5 Palu-Donggala earthquake
title_sort field reconnaissance data from geer investigation of the 2018 m(w) 7.5 palu-donggala earthquake
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7829142/
https://www.ncbi.nlm.nih.gov/pubmed/33532524
http://dx.doi.org/10.1016/j.dib.2021.106742
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