[S-SS07] Environmental Seismology: from deep earth to surface process

[SSS07-P16] Research on the Improved Microtremor Method Based on Distributed Acoustic Sensing Data

*XI CHAOQIANG¹, Huanqin Yun¹, Shiang Xu², Xiongwu Hu¹, Shenglin Li², Binyang Sun¹ (1.School of Earth and Environment, Anhui University of Science and Technology, 2.State Key Laboratory of Digital and Intelligent Technology for Unmanned Coal Mining, Anhui University of Science and Technology)

Keywords:Distributed Acoustic Sensing, DAS, Microtremor , Ambient noise

In recent years, Distributed Acoustic Sensing (DAS) technology has made significant strides in subsurface exploration. By configuring optical fibers into a dense array of seismic sensors, DAS enables low-cost, high-resolution detection of shallow subsurface spaces, providing a new technological tool for urban underground space development and environmental protection. In urban areas, ambient noise, primarily generated by human activities, is especially pronounced, with surface wave components often dominating. As an effective seismic analysis technique, microtremor methods have been widely applied in engineering and environmental fields to extract surface wave signals from background noise collected by traditional seismic equipment. However, conventional microtremor methods typically rely on Bessel functions for dispersion curve fitting, which may introduce "crossed" artifacts and interfere with the extraction of dispersion curves in the high-frequency range, thereby affecting result accuracy.

To address this issue, we propose an improved microtremor method that uses Hankel functions instead of Bessel functions for cross-correlation spectrum fitting. Unlike Bessel functions, which describe bidirectional wave propagation, Hankel functions are better suited for unidirectional wave propagation, effectively suppressing cross-frequency artifacts and significantly enhancing dispersion image precision. This improved method enables more accurate extraction of high-quality surface wave dispersion curves from DAS ambient noise data, providing reliable support for precise subsurface media detection.

Furthermore, by applying multi-mode dispersion curve inversion techniques, we successfully obtained the shear wave velocity structure of urban subsurfaces. The proposed method not only enhances the accuracy of subsurface media detection but also provides robust technical support and theoretical foundations for the scientific planning of urban underground spaces and environmental protection.