A transformer-based model for grid-agnostic full-field reconstruction of tsunami waves from sparse observations.

The Senseiver (Santos et al., 2023) is a recently developed deep learning architecture based on the Perceiver-IO that facilitates sparse sensing and reconstruction of large, highly complex fields residing on arbitrary grids. This talk will discuss a recent implementation tailored to reconstruct tsunami waves from sparse measurements. We demonstrate the first successful ML-based full-field reconstructions of tsunami waves from sparse data based on physically realistic simulations with accurate bathymetry of the Pacific Ocean. We will provide an overview of the model architecture and our adjustments to facilitate its use for dynamics governed by shallow water equations. Our results consist of high-resolution, full-field tsunami wave reconstructions given incredibly sparse measurements corresponding to locations of ocean buoys currently deployed in the Pacific. Since our approach is domain agnostic, we aim to frame the method and results within the larger context of optimal sensor placement and data assimilation problems in fluid dynamics. We demonstrate significant advantages of the Senseiver, such as the ability to efficiently handle large domain sizes without the quadratic scaling complexity that impedes the practical use of transformers in earth sciences.