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SUMMARY Receiver functions (RFs) estimated on dense arrays have been widely used for the study of Earth structures across multiple scales. However, due to the ill-posedness of deconvolution, RF estimation faces challenges such as non-uniqueness and data overfitting. In this paper, we present an array-based RF deconvolution method in the context of emerging dense arrays. We propose to exploit the wavefield coherency along a dense array by joint inversions of waveforms from multiple events and stations for RFs with a minimum number of phases required by data. The new method can effectively reduce the instability of deconvolution and help retrieve RFs with higher fidelity. We test the algorithm on synthetic waveforms and show that it produces RFs with higher interpretability than those by the conventional RF estimation practice. Then we apply the method to real data from the 2016 Incorporated Research Institutions for Seismology (IRIS) community wavefield experiment in Oklahoma and are able to generate high-resolution RF profiles with only three teleseismic earthquakes recorded by the temporary deployment. This new method should help enhance RF images derived from short-term high-density seismic profiles. 

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.