Seismic Source Characterization with Distributed Acoustic Sensing

Abstract

ABSTRACT: The emerging distributed acoustic sensing (DAS) technology can convert a pre-existing telecommunication fiber cable of tens of kilometers in length into a dense seismic array of thousands of recording channels. By measuring the laser phase change of the Rayleigh backscattering from intrinsic impurities in the fiber, a DAS system can infer the longitudinal strain or strain rate every few meters along the fiber at a frequency of hundreds of Hertz. DAS unprecedented temporal and spatial resolution makes it a promising technology for monitoring and characterizing earthquake source properties. Yet, challenges remain in leveraging this new technology due to large data volumes and the inadequacy of conventional algorithms applied to dense single-component DAS recordings. Here, we will showcase a series of research progresses in leveraging DAS for analyzing high-resolution earthquake source properties, including seismic phase picking, earthquake relocation, focal mechanism inversion, and high-resolution rupture imaging. We first introduce PhaseNet-DAS, a convolutional neural network model, for automated earthquake detection and seismic phase identification from DAS recordings. Our scalable cross-correlation framework, called CCTorch, then robustly measures differential phase traveltimes and relative polarities between picked seismic events. Furthermore, we have devised a matrix-free adjoint solver that can perform double-difference relocation using thousands of DAS channels. We have also developed a data-driven method that can reliably invert absolute first-arrival polarities on DAS, which can tightly constrain the nodal plane orientations and are critical for inverting high-resolution focal mechanisms. We have successfully applied all of these methods to several DAS arrays in California. Finally, we will showcase the back-projection rupture imaging results for the Mw6 crustal earthquake that occurred in Antelope Valley, CA in 2021. We will show that the high-resolution rupture imaging enabled by the dense DAS array can reveal the detailed underlying rupture processes and physical mechanisms at a much lower cost compared to the conventional seismic network. These successful research progresses underscore the potential of DAS as the next-generation seismic monitoring tool, that can significantly enhance and complement existing seismic networks. With the extensive existing and proposed network of onshore/offshore telecommunication fiber cables, DAS would provide critical datasets for systematically investigating the detailed seismic source properties.