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

Abstract

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

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