PI | |
Co-PIs | ; ; ; |
Project Type | Field research |
Natural Hazard Type(s) | Earthquake |
Facilities | |
Event(s) | 2018 Palu-Donggala Earthquake | Palu, Central Sulawesi, Indonesia | 2018-09-28 ― 2018-09-28 | Lat -0.178 long 119.84 |
Related Work | |
Keywords | Earthquake reconnaissance, Flowslide, landslide, liquefaction, ground failure, digital surface model, unmanned aerial vehicle (UAV), remote sensing, geotechnical earthquake engineering, Palu, Sulawesi, Indonesia |
The Mw7.5 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), collapsed buildings, and generated tsunami waves that impacted the coast of Palu Bay. The earthquake claimed over 4,000 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, 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) [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 RAPID Facility, including mobile data collection software, digital imaging systems, high-resolution global position system (GPS) antennas, and unmanned aerial vehicles (UAVs, or "drones"). The resulting dataset includes over 2,000 geotagged photographs, UAV images, GPS 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 1,200 structures affected by the flowslides.