On September 28, 2018, a Mw 7.5 earthquake triggered near Central Sulawesi generated a highly destructive tsunami within Paul Bay (Indonesia). Field surveys and various studies conducted after the event showed that, as a result of the earthquake, several large submarine landslides were triggered along the shores of the bay, that significantly contributed to tsunami generation. The estimated geometry and other parameters for these slides, however, were affected by a large uncertainty. Here, we present a probabilistic tsunami hazard analysis of this event, based on Monte Carlo simulations using a linear Mild Slope Equation (MSE) model combined with a Green's function approach, that allow efficiently simulating a large number of stochastic landslide tsunami generation and propagation scenarios within Palu Bay, for each of the identified landslides. In the MSE model, a space and time-dependent source term is used to represent the seafloor motion associated with each landslide scenario. In the Green's function approach, a large database of elementary solutions and their tsunami elevation at a large number of coastal save points is pre-computed for tsunamis generated by a unit seafloor acceleration specified over a small area. Then, given an actual submarine landslide scenario, with a specific acceleration function, the tsunami elevation at the save points is simply and efficiently computed as a weighed linear superposition of the elementary solutions. Tsunami runup is finally obtained using a semi-empirical method, based on results computed at save points for each landslide scenario. The model is applied to the 2018 Palu Bay tsunami event, allowing to investigate how the uncertainty in landslide parameters affects tsunami hazard. A comparison with observed runups is made, which shows that these fall within the range of uncertainty of the simulated probabilistic runups.
Cecioni, C., Iorio, V., Bellotti, G., Grilli, S.T. (2023). Probabilistic landslide tsunami modeling of the 2018 Palu Bay event. COASTAL ENGINEERING, 183, 104332 [10.1016/j.coastaleng.2023.104332].