Surface Wave Dispersion Measurement with Polarization Analysis Using Multicomponent Seismic Noise Recorded by a 1-D Linear Array

Linear arrays are popularly used for passive surface wave imaging due to their high efficiency and convenience, especially in urban applications. The unknown characteristics such as azimuth of noise sources, however, make it challenging to extract accurate phase-velocity dispersion information by em...

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Veröffentlicht in:Surveys in geophysics 2023-12, Vol.44 (6), p.1863-1895
Hauptverfasser: Pang, Jingyin, Xia, Jianghai, Cheng, Feng, Zhou, Changjiang, Chen, Xinhua, Shen, Chao, Xing, Huaixue, Chang, Xiaojun
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Sprache:eng
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Zusammenfassung:Linear arrays are popularly used for passive surface wave imaging due to their high efficiency and convenience, especially in urban applications. The unknown characteristics such as azimuth of noise sources, however, make it challenging to extract accurate phase-velocity dispersion information by employing a 1-D linear array. To solve this problem, we proposed an alternative passive surface wave method to capture the dominant azimuth of noise sources and retrieve the phase-velocity dispersion curve by polarization analysis with multicomponent ambient noise records. We verified the proposed method using synthetic data sets under various source distributions. According to the calculated dominant azimuth, it is deduced that noise sources are mainly classified as either inline or offline distribution. For inline noise source distribution, we are able to directly obtain the unbiased phase-velocity measurements; for offline noise source distribution, we should correct the velocity overestimation due to azimuthal effects using the proposed method. Results from two field examples show that the distributions of noise sources are predominantly offline. We eliminated the velocity bias caused by offline source distribution and picked phase velocities following higher amplitude peaks along the trend. After the azimuthal correction, the picked phase-velocity dispersion curves in dispersion images generated from passive source data match well with those from active source data, demonstrating the practicability of the proposed technique.
ISSN:0169-3298
1573-0956
DOI:10.1007/s10712-023-09787-8