SY011-0017
Modeling the sources of the 2018 Palu Tsunami, Indonesia, with the help of social media videos.

Tuesday, 8 December 2020
Poster
Ignacio Sepulveda, University of California San Diego, Scripps Institution of Oceanography, La Jolla, CA, United States, Matias Carvajal, University of Concepcion, Concepcion, Chile, Jennifer S Haase, UCSD, La Jolla, CA, United States, Philip L-F. Liu, National University of Singapore, Department of Civil and Environmental Engineering, Singapore, Singapore, Xiaohua Xu, Scripps Institution of Oceanography, Institute of Geophysics and Planetary Physics, La Jolla, CA, United States, Cristian Araya-Cornejo, Pontificia Universidad Católica de Chile, Doctorado en Geografía, Instituto de Geografía, Santiago, Chile and Daniel Melnick, Universidad Austral, Instituto de Ciencias de la Tierra, Capital Federal, Argentina
Abstract:
The 2018 Palu tsunami contributed significantly to the devastation caused by the associated Mw 7.5 earthquake. The tsunami event led to a debate about how the moderate size earthquake triggered such a large tsunami within Palu Bay, with runups of more than 10 m. The possibility of a large component of vertical coseismic deformation and submarine landslides have been considered as potential explanations. However, scarce instrumental data have made it difficult to resolve the potential contributions from either type of source.

We analyze an extraordinary collection of 38 social media videos and closed-circuit television videos to show that the Palu tsunami waves devastated widely separated coastal areas around Palu Bay within a few minutes after the mainshock and with wave periods of 100 s and shorter. We exploit tsunami waveforms derived from the collected videos to model the possible sources of the tsunami. We invert InSAR data with different fault geometries and use the resulting seafloor displacements to simulate tsunamis. The coseismic sources alone cannot match both the video‐derived time histories and surveyed runups. Then we conduct a tsunami source inversion using the video‐derived time histories and a tide gauge record as inputs. We specify hypothetical landslide locations and solve for initial tsunami elevation. Our results, validated with surveyed runups, show that a limited number of landslides in southern Palu Bay are sufficient to explain the tsunami data. The Palu tsunami highlights the difficulty in accurately capturing the amplitude and timing of short period tsunami waves with traditional tide gauges that can have large impacts at the coast. The proximity of landslides to locations of high fault slip also suggests that tsunami hazard assessment in strike‐slip environments should include triggered landslides, especially for locations where the coastline morphology is strongly linked to fault geometry. These findings are only possible with the analysis of the collected videos and demonstrate the great potential of social media contributing to geosciences.

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