One-dimensional layered isotropic velocities are typically used to locate microseismic events in conventional microseismic data processing. To account for anisotropy caused by the presence of a set of aligned vertical fractures, an inversion procedure is presented to simultaneously estimate microseismic event locations and the velocity model for horizontal transverse isotropic (HTI) media. The procedure employs Bayesian inference via Markov-chain Monte Carlo (McMC) sampling with parallel tempering and diminishing adaptation to ensure efficient sampling of the parameter space. This algorithm is exemplified with an application to a physical modeling data set, in which a phenolic CE material is used to simulate the HTI medium. In contrast to deterministic inversion algorithms, this approach provides a natural nonlinear uncertainty quantification by approximating the posterior probability density with an ensemble of model-parameter sets for both HTI velocity parameters and event locations.

Presentation Date: Wednesday, October 14, 2020

Session Start Time: 9:20 AM

Presentation Time: 9:20 AM

Location: Poster Station 11

Presentation Type: Poster

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