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Active faults and bedrock detection with super-high-density electrical resistivity imaging

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Abstract

Electrical resistivity imaging can reveal the spatial information of subsurface geological structures, especially active faults and bedrock. In this study, we introduced an improved super-high-density resistivity method, based on network parallel technique with an exploration mode similar to seismic method. The proposed technique simplifies the conventional electrical arrays, e.g., Dipole-Dipole and Wenner-Schlumberger arrays into AM and ABM arrays and enhances the data acquisition efficiency. A comparison between the two arrays and conventional ones revealed that AM and ABM array data can be transformed into all kinds of existing electrical arrays. We performed six 2D electrical resistivity surveys on a construction site at Yantai (Shandong Province, Eastern China) with AM and ABM array of 5-m electrode spacing to produce a ~ 2.5-m spatial resolution. For the inversion routine, the least squares method was used to obtain the subsurface electrical structures for objective interpretation. Consequently, a set of electrical anomalies associated with the active faults and bedrock interface were identified with four resistivity tomographic profiles. In these resistivity profiles, the electrical anomalies show good correlations with the drilling results. Therefore, the super-high-density electrical resistivity method is useful for quick evaluation of subsurface geological structures in an urbanized area and provides more precise and faster data acquisition than conventional techniques.

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Acknowledgments

We thank two anonymous reviewers and the editors for their insightful comments and suggestions.

Funding

This study is benefited from the help of Prof Shengdong Liu (CUMT, China). This research was supported by the National Natural Science Foundation of China (41807296 and 41802006), and the Natural Science Fund for Universities of Anhui Province (KJ2017A036).

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Authors and Affiliations

  1. School of Resources and Environmental Engineering, Anhui University, Hefei, 230601, China

    Qi Feng Yin, Peng Fei Tao & Yi Min Xia

  2. Anhui Province Engineering Laboratory for Mine Ecological Remediation, Hefei, 230601, China

    Qi Feng Yin, Peng Fei Tao & Yi Min Xia

  3. Anhui University, Hefei, Anhui Province, China

    Qi Feng Yin

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  1. Qi Feng Yin
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  2. Peng Fei Tao
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Correspondence to Qi Feng Yin.

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Yin, Q.F., Tao, P.F. & Xia, Y.M. Active faults and bedrock detection with super-high-density electrical resistivity imaging. Bull Eng Geol Environ 79, 5049–5060 (2020). https://doi.org/10.1007/s10064-020-01867-y

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  • DOI: https://doi.org/10.1007/s10064-020-01867-y

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