A Novel Approach to Tsunami Prediction Using Ambient Noise-Derived Green's Functions

Abstract

Conventional tsunami simulations rely on accurate bathymetric data, posing challenges in regions lacking such information. We introduce a novel approach using ambient noise interferometry to derive empirical Green's functions of infragravity waves from noise correlation functions (NCFs) extracted from a 10-year Deep-ocean Assessment and Reporting of Tsunamis data set in the Pacific Ocean. Our analysis reveals pronounced propagating behavior in NCFs, indicative of wave dispersion relationships. Long-period NCFs align with shallow-water wave dynamics, making them suitable for tsunami simulations. By eliminating the need for precise bathymetry, our method offers a practical solution for data-sparse regions. A case study of an Alaska tsunami demonstrates our NCFs effectively fit observed pressure data, outperforming conventional Cornell Multi-Grid Coupled Tsunami Model simulations. The fidelity of our results underscores the potential of ambient noise interferometry-derived NCFs to enhance tsunami predictions, even in complex environments. Our findings advance tsunami research and have significant implications for disaster preparedness and mitigation.