Subsurface imaging based on polarization analysis of microtremor Rayleigh waves

Abstract

The precise identification of adverse geological bodies, such as goafs, karst cavities, and faults, is crucial for engineering safety and economic viability. However, conventional drilling methods suffer from high costs, limited coverage, and the risk of overlooking anomalies. While existing geophysical techniques can address drilling’s limitations, they are often constrained by non-uniqueness and insufficient resolution. To address these challenges, this paper proposes a novel technique for stratigraphic evaluation based on the polarization analysis of microtremor surface waves, aiming to improve the accuracy and efficiency of identifying dynamic site parameters in complex geological settings. We systematically validated the feasibility of using the elliptical polarization ratio of Rayleigh waves for subsurface characterization through theoretical analysis and field data. Numerical simulations confirmed that the method can clearly identify localized geological anomalies under noise-free conditions. For field data processing, we developed a workflow to extract Rayleigh waves with a high signal-to-noise ratio from microtremors. This workflow isolates valid wave components using principal frequency filtering and directional discrimination, while mitigating noise impact on dispersion estimation. The final polarization ratio profile strongly correlated with magnetotelluric resistivity data, successfully imaging goafs, fractured zones, and bedrock interfaces. Stacking codirectional wave events further enhanced deep structural resolution and resulted in consistency. In conclusion, this study demonstrates that the polarization-based method is a feasible, flexible, and promising tool for engineering applications, offering a reliable foundation for fine-scale site characterization.

Keywords

Microtremor Rayleigh wave / Elliptical polarization ratio / Site characterization / Numerical simulations

Acknowledgments

None.

Funding

This research was financially supported by the Henan Province Science and Technology Research Project (Grant No. 252102320002 and 242102231058), the National Natural Science Foundation of China (Grant No. 42462028), Postgraduate Education Reform and Quality Improvement Project of Henan Province (YJS2023JD52), the Science and Technology Program of Gansu Province (Grant No. 24CXGE011), and the Science and Technology Program Funding of Tianshui Qinzhou District (Grant No. 2024-SHFZG-9168).

Conflict of interest

The authors declare they have no competing interests.

Author contributions

Conceptualization: Qingling Du, Qian Xu

Formal analysis: Qingling Du, Shijie Liu

Investigation: Shijie Liu, Qingling Du, Nan Guo

Methodology: Qingling Du, Shuai Liu

Writing-original draft: Qingling Du

Writing-review & editing: Qian Xu, Nan Guo, Denghui Gao

Availability of data

Data are available from the corresponding author upon reasonable request.

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