Detection of near-surface cavities using the 2D multi-parameter full-waveform inversion of Rayleigh waves

LI Yu; WANG Jingqi; GUAN Jianbo; YAN Yingwei

doi:10.12363/issn.1001-1986.23.02.0064

Volume 51 Issue 7

Jul. 2023

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COAL GEOLOGY & EXPLORATION > 2023 > 51(7): 162-173. > DOI: 10.12363/issn.1001-1986.23.02.0064

LI Yu，WANG Jingqi，GUAN Jianbo，et al. Detection of near-surface cavities using the 2D multi-parameter full-waveform inversion of Rayleigh waves[J]. Coal Geology & Exploration，2023，51(7)：162−173. DOI: 10.12363/issn.1001-1986.23.02.0064

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Detection of near-surface cavities using the 2D multi-parameter full-waveform inversion of Rayleigh waves

1.
School of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China
2.
Key Laboratory of Western China’s Mineral Resources and Geological Engineering, Ministry of Education, Chang’an University, Xi’an 710054, China
3.
Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, China

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Received Date: February 02, 2023
Revised Date: May 31, 2023
Available Online: July 02, 2023

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Abstract

Abstract

The accurate detection of near-surface low-velocity anomalies (such as cavities), which has always been a valuable and challenging research topic in the field of geophysics, holds great significance for the fine-scale near-surface modeling in urban disaster assessment and the seismic exploration of coalfields under complex conditions. The full-waveform inversion (FWI) of surface waves is suitable for high-precision near-surface modeling. However, there still exist many urgent problems with the modeling in practical applications. To solve the key issues of the FWI of surface waves, such as multi-parameter crosstalk, actual data preprocessing, and source wavelet estimation, this study developed a complete method of multi-parameter FWI of Rayleigh waves to achieve the accurate detection of near-surface cavities. In this method, (1) the S- and P-wave velocities and density of the models were updated synchronously in the process of inversion, thus reducing the negative effects of the deviations of the P-wave velocity and density from their actual values on the accuracy of inverted S-wave velocities; (2) the quasi-Hessian operator constructed using the adjoint state method was employed to conduct gradient preprocessing in order to suppress surface artifacts, enhance wavefield illumination, and improve the characterization ability for small-scale anomalies; (3) to transform the 3D wave field into a 2D wave field, the convolutional factor was used to eliminate the dimension difference between the wavefield forward modeling and the actual data acquisition; (4) to reduce the influence of specific inaccurate parameter models, the corrected filtering method was used to dynamically estimate the source wavelets during the iterative process; (5) to improve the stability of the inversion, a multi-scale inversion strategy was adopted to alleviate the non-convexity of the objective function caused by low-velocity anomalies. The synthetic data and the test results of actual cases show that the models of the S- and P-wave velocities and the density developed using the multi-parameter FWI method of Rayleigh waves were roughly consistent, with the S-wave velocity model being the most accurate. The S-wave velocity model obtained through the inversion of measured data revealed a 4 m × 3 m artificial cavity, whose location and size are consistent with the actual situation. This result demonstrates the method proposed in this study features feasibility and effectiveness in the detection of near-surface cavities.
- Rayleigh wave,
- full-waveform inversion,
- cavity detection,
- low-velocity anomaly,
- shear-wave velocity

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Detection of near-surface cavities using the 2D multi-parameter full-waveform inversion of Rayleigh waves

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