Urban Basin Structure Imaging Based on Dense Arrays and Bayesian Array‐Based Coherent Receiver Functions

Urban basin investigation is crucial for seismic hazard assessment and mitigation. Recent advances in robust nodal-type sensors facilitate the deployment of large-N arrays urban areas high-resolution imaging. However, typically operate only one month due to instruments' battery life, hence, record a few teleseismic events. This limits number available events traditional receiver function (RF) analysis-the primary method used sediment-basement interface imaging passive source seismology. Insufficient stacking RFs from limited earthquakes could, however, introduce significant biases results. In this study, we present novel Bayesian array-based Coherent Receiver Function (CRF) that can leverage datasets short-term dense constrain geometry. We cast RF deconvolution as sparsity-promoted inverse problem, which at single-station involves constraints neighboring stations multiple solve problem using trans-dimensional Markov chain Monte Carlo algorithm find an ensemble solutions, provides quantitative way deciding features are well resolved warrant geological interpretation. An application northern Los Angeles demonstrates ability our produce reliable easy-to-interpret images. The use networks state-of-the-art CRF provide approach subsurface structure