Application of Scientific Machine Learning (SciML) in Earthquake Research

Abstract

Scientific machine learning (SciML) is a research field aimed at solving various scientific problems through the mutual complementation and synergy of data science and physical laws/mathematical models. In this talk, I introduce examples of the application of SciML in earthquake studies. Among the representative methods of SciML are Physics-Informed Neural Networks (PINNs) and operator learning. PINN is a method that has attracted attention due to its widespread use across many engineering fields. For instance, in seismic travel time calculations, the introduction of PINNs has enabled simultaneous solutions and rapid inference for various source conditions without labeled data. It also allows solving inverse problems such as travel time tomography using methods different from conventional approaches. On the other hand, operator learning includes two types: DeepONet and Fourier Neural Operator (FNO). These learn mappings from functions to functions. With the capability of operator learning, it becomes possible to instantly predict seismic motions for various velocity structure. Although these architectures are still somewhat complex and incomplete, remarkable future developments are anticipated.