Ambient Field Analysis Using Unsupervised Machine Learning and Blind Source Separation for Groundwater Monitoring in California

The ambient seismic field comprises waves generated by a wide range of tectonic, environmental, and anthropogenic processes, and they also encode information about the subsurface medium through which the waves propagate. Over the past two decades, ambient-field-based techniques have emerged as a powerful approach for monitoring temporal changes in the Earth’s subsurface properties. These methods exploit the statistical characteristics of continuous seismic records to detect subtle perturbations in the medium without relying on earthquake sources. 

Recently, Steinmann et al. (2022) proposed an alternative approach for monitoring the freezing of near-surface material. This approach is based on a statistical blind source separation and an unsupervised machine learning framework applied to continuous seismic data. We apply this method to groundwater monitoring in California using single-station seismic recording. The approach aims to disentangle overlapping seismic signatures from different sources and physical processes to isolate components related to hydrological variations. We will evaluate the performance and robustness of this method and discuss its potential for improved monitoring of groundwater-driven changes in subsurface seismic properties.