Joint source-structure full waveform inversion using distributed acoustic sensing

SUMMARY Seismological inversion traditionally targets either source parameters, such as location and moment tensor, or structural parameters, such as velocity and anisotropy. However, the natural formulation of full-waveform inversion, often used for high-resolution structural model estimation, is to jointly invert for source and structural parameters. The common practice of holding source parameters, after initial estimation, fixed throughout the inversion inherently leads to biased solutions of the structural model and vice versa. Whereas a joint inversion suffers from severe non-uniqueness, we demonstrate that leveraging the large amounts of data available from Distributed Acoustic Sensing (DAS) can yield robust and unbiased estimations of source and structural parameters, provided an appropriate misfit function and optimization scheme are used. We show how the size of the data space and eventual convergence can be improved by supplementing the phase misfit objective function with amplitude information. To this end, we formulate a new misfit function, the normalized envelope. To support native DAS data implementations, we calculate the adjoint sources for the new misfit function when defined directly on strain or strain-rate data. We also show how a new approach to pre-conditioning as part of the L-BFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) optimization scheme allows for effective updates of all parameters in the same iteration, despite enormous differences in their relative importance. We test our approach in a challenging synthetic noisy 2-D scenario, showing a considerable reduction in source parameter errors and an improved S-wave velocity model. We also show a 3-D synthetic case with an idealized DAS recording array, demonstrating a significant reduction of source parameter errors using realistic initial estimates and structural model errors. We argue that the proposed methodology can be used to improve the quality of earthquake catalogues and high-resolution structural models in seismically active regions, especially at the local-to-regional scale. Nonetheless, computational cost remains a major challenge of the method.