Intelligent identification of lithology and adverse geology: A state-of-the-art review

Abstract

The accurate and timely identification of lithology and adverse geology is crucial for the safe and efficient construction of tunnels. However, traditional methods for lithology and adverse geology identification rely excessively on the experience and accumulated knowledge of geologists, making them highly subjective and prone to misjudgement and omission. This study aims to introduce the latest advancements in lithology and adverse geology identification. First, we present an innovative high-precision method for the intelligent identification of lithology based on “pure image,” “infrared spectral,” and “image and spectral fusion” analyses. Second, we propose methods of adverse geology identification, including “element and mineral anomaly analysis,” “geological and geophysical joint inversion,” and “multi-source data fusion of borehole information,” which realize comprehensive identification of the location, shape, scale, property, and type of adverse geology ahead of a tunnel working face. Finally, we present new theories and methods for the quantitative testing and inversion of elements and minerals, multi-source data fusion for intelligent lithology identification, and adverse geology identification dual-driven by knowledge and data. Integrating and analyzing multi-source data on geology, geophysical prospecting, and advanced drilling is conducive to overcoming the limitations of single-source data and is the future development direction of accurate and intelligent lithology and adverse geology identification.

Keywords

Lithology identification / Image and spectral fusion / Adverse geology identification / Multi-source data fusion / Advanced drilling

CRediT authorship contribution statement

Zhenhao Xu: Writing -review & editing, Writing -original draft, Supervision, Methodology, Funding acquisition, Data curation, Conceptualization. Tengfei Yu: Writing -review & editing, Writing -original draft, Methodology, Formal analysis, Data curation. Shucai Li: Supervision, Methodology, Conceptualization. Peng Lin: Writing -review & editing, Visualization, Methodology. Wen Ma: Writing -original draft, Methodology, Data curation. Tao Han: Writing -original draft, Methodology, Data curation. Shan Li: Writing -original draft, Methodology, Data curation.

Declaration of competing interest

Shucai Li is the Editor-in-Chief for this journal, and was not involved in the editorial review or the decision to publish this article. All authors declare that there are no competing interests.

Acknowledgements

We would like to acknowledge the support from the National Natural Science Foundation of China (Grants Nos 52021005, 52279103 and 52379103).

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