Deep Green’s Function Tsunami Inversion

An important source of uncertainty in tsunami forecasting arises from uncertainty in the event’s initial conditions. In this work, we propose a dictionary-based inversion method that uses off-shore sensor data to recover the initial ocean condition, allowing inversion of any tsunami event, and leading to reduced uncertainty in forecasts. We show that deep learning models can be used to address the computational requirements that arise from dictionary-based inversion. We validate our method using simulations of historic events.