Applying a Deep Learning Algorithm to Tsunami Inundation Database of Megathrust Earthquakes

Abstract

AbstractWe explore recent developments in computer science on deep learning to estimate high‐resolution tsunami inundation from a quick low‐resolution computation result. Deep network architecture is capable of storing large information acquired via a training/learning process by pairing low‐ and high‐resolution deterministic simulation results from precalculated hypothetical scenarios. In a real case, with a real‐time source estimate and linear simulation computed on a relatively low‐grid resolution, optimized network parameters can be used to rapidly and accurately transform low‐resolution simulation outputs to higher‐resolution grids. We generate 532 source scenarios of interplate earthquakes (Mw 8–9) along the Japan Trench subduction zone to simulate tsunamis and utilize a precalculated library for deep learning. To test the proposed method, we consider a realistic source model inferred from static ground displacements and offshore tsunami data presumably available in real‐time and compare forecasted inundation heights against observations in Rikuzentakata and Otsuchi cities associated with the 2011 Tohoku‐oki tsunami. Our results show that the proposed method exhibits comparable accuracy to the conventional physics‐based simulation but achieves approximately 90% reduction of real‐time computational efforts. Thus, it has good potential as a future tsunami forecasting algorithm.