High-quality surface wave retrieval from vibrations generated by high-speed trains moving on viaducts
Short-time passive surface wave imaging of the shallow subsurface faces great challenges due to the lack of anthropogenic noise in areas far away from towns. Vibrations generated by high-speed trains (HST) are good complements because of its strong energy and broad frequency band. The processing sch...
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| Published in | Journal of applied geophysics Vol. 212; p. 105005 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier B.V
01.05.2023
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| Subjects | |
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| Abstract | Short-time passive surface wave imaging of the shallow subsurface faces great challenges due to the lack of anthropogenic noise in areas far away from towns. Vibrations generated by high-speed trains (HST) are good complements because of its strong energy and broad frequency band. The processing scheme of surface wave imaging from HST vibrations has not been systematically studied. We propose an optimized workflow of high-quality surface wave imaging from HST vibrations. An HST signal selection method is used to automatically select the data during the HST running through the stationary phase zone of the survey line. The characteristic of sharp equidistant spectral lines in the frequency spectrum of HST signals will distort the virtual shot gather and the frequency domain normalization operation can suppress the distortion. The phase weighted stacking and the multi-channel correlation weighted stacking are applied to improve the signal-to-noise ratio of the virtual shot gather. The HST signal processing scheme is applied to field data with four survey lines and surface wave signals are better retrieved than using the conventional ambient noise processing scheme. The results demonstrate the advantage of our proposed workflow in real-world applications, which provides a tool of surface wave imaging from HST vibrations.
•An optimized workflow of surface wave imaging from HST vibrations.•A data selection method for HST signals.•High-quality surface waves are retrieved from field data with HST vibrations. |
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| AbstractList | Short-time passive surface wave imaging of the shallow subsurface faces great challenges due to the lack of anthropogenic noise in areas far away from towns. Vibrations generated by high-speed trains (HST) are good complements because of its strong energy and broad frequency band. The processing scheme of surface wave imaging from HST vibrations has not been systematically studied. We propose an optimized workflow of high-quality surface wave imaging from HST vibrations. An HST signal selection method is used to automatically select the data during the HST running through the stationary phase zone of the survey line. The characteristic of sharp equidistant spectral lines in the frequency spectrum of HST signals will distort the virtual shot gather and the frequency domain normalization operation can suppress the distortion. The phase weighted stacking and the multi-channel correlation weighted stacking are applied to improve the signal-to-noise ratio of the virtual shot gather. The HST signal processing scheme is applied to field data with four survey lines and surface wave signals are better retrieved than using the conventional ambient noise processing scheme. The results demonstrate the advantage of our proposed workflow in real-world applications, which provides a tool of surface wave imaging from HST vibrations.
•An optimized workflow of surface wave imaging from HST vibrations.•A data selection method for HST signals.•High-quality surface waves are retrieved from field data with HST vibrations. |
| ArticleNumber | 105005 |
| Author | Guan, Bo Zhang, Hao You, Baiyang Mi, Binbin Liu, Ya |
| Author_xml | – sequence: 1 givenname: Baiyang surname: You fullname: You, Baiyang email: byyou@zju.edu.cn organization: Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University, Hangzhou 310027, China – sequence: 2 givenname: Binbin surname: Mi fullname: Mi, Binbin email: mibinbin@zju.edu.cn organization: Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University, Hangzhou 310027, China – sequence: 3 givenname: Bo surname: Guan fullname: Guan, Bo email: guanbo@zju.edu.cn organization: Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University, Hangzhou 310027, China – sequence: 4 givenname: Hao surname: Zhang fullname: Zhang, Hao email: zhanghao05@aircas.ac.cn organization: GBA Branch of Aerospace Information Research Institute, Chinese Academy of Sciences, Guangzhou 510700, China – sequence: 5 givenname: Ya surname: Liu fullname: Liu, Ya email: liuya@zju.edu.cn organization: Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University, Hangzhou 310027, China |
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| Keywords | Signal selection Surface wave Processing scheme High-speed train signals |
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