Applying cellular automata to dynamic simulation of a tsunamigenic submarine landslide in the South China Sea

Document Type

Article

Source of Publication

Marine and Petroleum Geology

Publication Date

4-1-2024

Abstract

This work presents the first known application of a new approach to tsunami modelling, by linking a discrete cellular automata (CA) dynamic model of a submarine landslide to an off-the-shelf tsunami modelling package. This has the advantage of simulating the temporal evolution of the submarine landslide rather than as a single rigid sliding mass. We first tested that the coupled model was able to satisfactorily reproduce observed tsunami wave heights resulting from the flank collapse of the Anak Krakatau volcano in the Sunda Strait (Indonesia) in December 2018, before applying it to a speculative area of interest on the North Borneo (Continental) Shelf (NBS). The NBS has one of the thickest near-shore clastic sediment wedges known globally (12 km thickness) and a very long pre-history of submarine landsliding. We modelled a small slide (5 km3 volume) because this is far more likely to occur than a recurrence of the gigantic Brunei slide (1200 km3 volume). Results indicate that a 5 km3 submarine landslide generates multiple tsunami waves, the largest of which reached 8 m at the coast of nearby Balabac Island (The Philippines). Small tsunami waves also arrived on the central Vietnam coast, more than 900 km distant. A larger potential failure on the NBS therefore appears capable of causing a tsunami with greater runup heights, which could pose a risk to much of central and southern Vietnam, northern Borneo (Sabah, Brunei) and Palawan. Finally, we note that the South China Sea is fringed by a necklace of other thick offshore clastic sediment accumulations, most notably on the southern China continental margin, offshore southern Taiwan, and offshore central Vietnam. Given the proof-of-concept of our coupled CA–tsunami modelling approach described here, we recommend this also be applied in these areas to assess the risk posed by a wide range of submarine landslides to coastal populations and infrastructural assets (including nuclear power plants).

ISSN

0264-8172

Publisher

Elsevier BV

Volume

162

Disciplines

Earth Sciences

Keywords

Tsunami modelling, Submarine landslide, Cellular automata, South China Sea, Coastal populations

Scopus ID

85185166830

Indexed in Scopus

yes

Open Access

no

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