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Quantitative modeling of rock electrical resistivity under uniaxial loading and unloading

Qu Chuanqi, Yiguo Xue, Su Maoxin, Qiu Daohong, Ma Xiaoyin, Liu Qiushi, Li Guangkun

Acta Geophysica

2024

Vol. 72, no. 1

195--212

In geotechnical engineering practice, studying the characteristics of rock electrical resistivity under different physical and mechanical environments is important for improving the accuracy of unfavorable geological condition detection. In this study, Dali marble and granite were subjected to cyclic loading and unloading at different peak pressures, and the electrical resistivity of the core samples was continuously monitored. The aim was to derive the electrical resistivity variation patterns of two rocks under cyclic loading and unloading. Then, the theoretical models of electrical resistivity for the two rocks were established through theoretical derivation, and the model equations were consistent with the experimental findings. Finally, a MATLAB program was used for nonlinear fitting of the unloading phase electrical resistivity variation patterns of the rocks to reveal the quantified pressure-electrical resistivity patterns of the two rocks with different lithologies. Based on this, well-performing rock pressure-electrical resistivity relationship models were developed with three parameters: pressure, water content and electrical resistivity. The experimental results can significantly improve the accuracy of stratigraphic state inversion and unfavorable body boundary identification by providing realistic electrical resistivity constraints for electrical resistivity tomography.

Waveform and resistivity data fusion imaging method based on the refection coefficient

Su Maoxin, Han Min, Xue Yiguo, Zhao Ying, Wang Peng, Li Guangkun

Acta Geophysica

2023

Vol. 71, no. 1

175--192

To achieve comprehensive analyses, the presentation of comprehensive geophysical results usually involves the use of separate imaging and the combination of various results. At present, few studies have considered the correlation degree and unified imaging of different types of geophysical data. We establish a set of data fusion imaging methods for multiple geophysical data based on their refection coefficients. As geophysical exploration results are primarily provided through waveform and resistivity sections, waveform and resistivity data were selected for fusion and were converted into refection coefficients, and ground-penetrating radar (GPR) and surface electrical resistivity tomography (ERT) were taken as examples. Re-sampling and feature reconstruction were performed to unify the data in space and resolution. Finally, principal component analysis was used to calculate the correlation of the reconstructed refection coefficient and to perform data fusion; this led to unified imaging based on the refection coefficient of the considered geophysical data. Numerical simulation analyses and field experiments proved the efficacy of this method for producing unified imaging of multiple geophysical data. In summary, we provide a novel method for the unified interpretation of multiple geophysical data and enhance the identification ability of geological interfaces and anomaly distribution.