Being able to image the ruptures of moderate earthquakes would significantly increase our observations towards comprehending earthquake source physics, fault properties and seismic hazards.

However, resolving their rupture characteristics remains challenging for conventional seismic networks due to limited station density. The emergence of Distributed Acoustic Sensing (DAS) offers a potential solution by providing dense and continuous measurements. In this study, we systematically evaluate the resolution capabilities of multi-fibre DAS networks for back-projection (BP) and demonstrate the feasibility of using DAS networks through both synthetic tests and analysis of a Mw 4.9 event in the Eastern California Shear Zone.

Furthermore, we propose a two-step inversion procedure that strategically integrates DAS with the conventional network. Our results suggest that strategically deployed multi-fibre DAS network can serve as the next generation of earthquake observation system and significantly enhance our understanding of earthquake rupture physics, as well as seismic risk preparedness.

Numerical simulations of seismic wave propagation are crucial for investigating velocity structures and improving seismic hazard assessment. However, standard methods such as finite difference or finite element are computationally expensive. Recent studies have shown that a new class of machine learning models, called neural operators, can solve the elastodynamic wave equation orders of magnitude faster than conventional methods. Full waveform inversion is a prime beneficiary of the accelerated simulations. Neural operators, as end‐to‐end differentiable operators, combined with automatic differentiation, provide an alternative approach to the adjoint‐state method. State‐of‐the‐art optimization techniques built into PyTorch provide neural operators with greater flexibility to improve the optimization dynamics of full waveform inversion, thereby mitigating cycle‐skipping problems. We demonstrate the application of neural operators for full waveform inversion on real seismic data, using nodal transects collected across the San Gabriel, Chino, and San Bernardino basins in the Los Angeles metropolitan area. 

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