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

Wang, Xin; Zhan, Zhongwen; Zhong, Minyan; Persaud, Patricia; Clayton, Robert W.

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. Plain Language Summary: Basin imaging is very important in urban areas and megacities (e.g., Los Angeles, San Francisco, Seattle, Tokyo) that are densely populated and prone to earthquakes, as basins tend to trap seismic energy, and increase the amplitude and duration of strong ground shaking. Recent advances in nodal‐type sensors facilitate the deployment of a larger number of seismometers (dense arrays) in the urban environment areas for rapid basin surveying. In this study, we developed a novel method that can leverage the dense arrays for urban sub‐surface structure imaging. The imaged sub‐surface structures are presented in probabilistic form, allowing objective assessment of feature identification and geological interpretation. We applied our method to two linear nodal arrays deployed in the greater Los Angeles region to demonstrate the ability of our method to produce reliable and easy‐to‐interpret sediment‐basement imaging. Our method can effectively unleash the power of dense array data for subsurface structure imaging. Key Points: We developed a novel Bayesian array‐based receiver function method that can leverage dense arrays for urban basin imagingProbabilistic representation of the receiver functions helps objective assessment of feature identification and geological interpretationOur method produces reliable and coherent basin images that can improve our understanding of subsurface structures

EARTHQUAKE hazard analysis; BAYESIAN analysis; UNDERGROUND areas; STATISTICAL decision making; GEOPHYSICS

Journal of Geophysical Research. Solid Earth, 2021, Vol 126, Issue 9, p1

2169-9313

Academic Journal

10.1029/2021JB022279