To estimate tsunami hazards, it is first necessary to have reliable data relating to the rupture characteristics, such as epicenter, fault geometry, uplift speed, and duration. We made use of a mathematical model that combines analytical and machine learning technique capable of retrieving rupture characteristics from acoustic data. The model was applied with short computational times to data recorded by the comprehensive nuclear-test-Ban Treaty organization hydrophones during four tectonic events that were reported to trigger tsunami waves. The presented inverse problem model for acoustic waves with adequate tsunami propagation tools can be used as a complementary technique alongside tsunami warning systems due to the high propagating speeds of the sound in the ocean. In this paper, the validity of the solutions provided by the inverse problem model is tested by using the calculated earthquake parameters as input to the Cornell multi-grid coupled tsunami numerical model, which, in turn, output surface wave elevations (tsunami) to be compared against deep-ocean assessment and reporting of tsunamis buoy data.
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