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.

For many inverse problems, the data on which the solution is based is acquired sequentially. We present an approach to the solution of such inverse problems where a sensor can be directed (or otherwise reconfigured on the fly) to acquire a particular measurement. An example problem is magnetic resonance image reconstruction. We use an estimate of mutual information derived from an empirical conditional distribution provided by a generative model to guide our measurement acquisition given measurements acquired so far. The conditionally generated data is a set of samples which are representative of the plausible solutions that satisfy the acquired measurements. We present experiments on toy and real world data sets. We focus on image data but we demonstrate that the method is applicable to a broader class of problems. We also show how a learned model such as a deep neural network can be leveraged to allow generalisation to unseen data. Our informed adaptive sensing method outperforms random sampling, variance based sampling, sparsity based methods, and compressed sensing.