Analytic and finite-difference modeling of DAS fiber data from moment tensor sources
Matthew Eaid, Kristopher A. Innanen
Distributed acoustic sensing has become a prevalent technology for reservoir monitoring, and has potential for applications in earthquake seismology. In preparation for advanced applications of these datasets such as imaging and inversion, we develop two methods for forward modeling DAS datasets generated by moment tensor type sources. The first is an efficient analytic modeling algorithm well-suited to modeling large datasets of DAS-microseismic direct arrivals. Another paper in this issue uses this algorithm for generation of a datasets used as input for a machine learning study for source mechanism estimation. The second method we develop is a full 3D finite-difference method based on the velocity-stress method.