Improving Indonesia's tsunami early warning: Part I: Developing synthetic tsunami scenarios and initial deployment

Abstract

Indonesia's Java subduction zone has triggered devastating tsunamis, emphasizing the need for a robust Tsunami Early Warning System, specifically for Southern Java, Bali, and Nusa Tenggara. With only six Ocean Bottom Pressure Gauges (OBPGs) currently monitoring tsunami propagation in the deep sea, optimized future sensor deployment is crucial. This paper, the first in a two-part series, proposes new observation networks to enhance tsunami early warning system. Our methodology involves developing synthetic stochastic-slip earthquake-induced tsunami simulations, delineating tsunami lead times, and applying empirical orthogonal functions (EOF) to determine spatial modal energy. We also assess the reliability of spacing and bathymetry for potential sensor locations. Our analysis reveals potential locations for additional OBPGs across the area. The proposed network consists of 42 additional sensors, demonstrating the potential for earlier warnings. These findings lay the groundwork for the second part of our series, where we will develop advanced forecasting models incorporating deep learning techniques based on the proposed location and further optimize sensor locations with the novel approach of hybrid optimizer and deep learning model. By establishing an improved observation network, this study contributes to more effective tsunami early warning systems in Indonesia, potentially mitigating the impact of future events on coastal communities.