Path Signatures and Graph Neural Networks for Slow Earthquake Analysis: Better Together?
This work addresses earthquake prediction and sensor network analysis, but it appears incremental as it combines existing methods (path signatures and GNNs) for a specific application.
The paper tackles the problem of analyzing slow earthquake sequences by integrating path signatures for feature extraction with graph neural networks for spatial interactions, using GPS timeseries data from a sensor network in New Zealand, and shows promise for earthquake prediction and sensor network analysis.
The path signature, having enjoyed recent success in the machine learning community, is a theoretically-driven method for engineering features from irregular paths. On the other hand, graph neural networks (GNN), neural architectures for processing data on graphs, excel on tasks with irregular domains, such as sensor networks. In this paper, we introduce a novel approach, Path Signature Graph Convolutional Neural Networks (PS-GCNN), integrating path signatures into graph convolutional neural networks (GCNN), and leveraging the strengths of both path signatures, for feature extraction, and GCNNs, for handling spatial interactions. We apply our method to analyze slow earthquake sequences, also called slow slip events (SSE), utilizing data from GPS timeseries, with a case study on a GPS sensor network on the east coast of New Zealand's north island. We also establish benchmarks for our method on simulated stochastic differential equations, which model similar reaction-diffusion phenomenon. Our methodology shows promise for future advancement in earthquake prediction and sensor network analysis.