期刊
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
卷 126, 期 9, 页码 -出版社
AMER GEOPHYSICAL UNION
DOI: 10.1029/2021JB022279
关键词
Coherent Receiver Function; basin structure; large-N arrays; trans-dimensional inversion; array-processing
资金
- nodal Basin Amplification Seismic INvestigation (BASIN)
- National Science Foundation [1722879]
- National Natural Science Foundation of China [91958209, 41774058]
- Young Elite Scientists Sponsorship Program by CAST [2020QNRC001]
- BASIN project - U.S. Geological Survey awards [GS17AP00002, G19AP00015]
- Southern California Earthquake Center awards [18029, 19033]
- NSF [2105320, 2105358]
- Division Of Earth Sciences
- Directorate For Geosciences [1722879, 2105358, 2105320] Funding Source: National Science Foundation
Urban basin investigation is crucial for seismic hazard assessment and mitigation. A novel Bayesian array-based Coherent Receiver Function (CRF) method is introduced in this study to constrain basin geometry, demonstrating its ability in the northern Los Angeles basin. The use of dense seismic networks and state-of-the-art CRF method provide a robust approach for subsurface structure imaging.
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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