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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. 

Abstract Cost-effective strategies for enhancing seismic velocity models are an active research topic. The recently developed hybridization technique shows promise in improving models used for deterministic earthquake hazard evaluation. We augment the results of Ajala and Persaud (2021) by exploring other hybrid models generated using 13 sets of embedding parameters—taper widths and subvolumes—and summarize their effect on waveform predictions up to a minimum period of 2 s. Our results introduce the notion of compatibility as a consideration by showing that the same basin models embedded into two different regional models can produce notably different outcomes. In contrast to most of our hybrid Harvard models that produce better matching ground motions, only one of the hybrid models generated using the Southern California Earthquake Center model as a regional model gives a closer match to the waveforms. Similar results are obtained at higher frequencies; however, improvements due to hybridization are reduced. A potential explanation for these results may be the limited high spatial frequencies in the travel time tomography basin models and the >5–6 s wavefield-dominated adjoint regional models. Although the strongly tapered compatible hybrid models tend to produce better results, we find instances of improvements even with merging artifacts.