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Research on lithology identification based on multi-sensor hybrid domain information fusion and support vector machine

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Abstract

To quickly and accurately identify lithology, this paper proposes a lithology identification method based on the combination of three-dimensional vibration signal hybrid domain feature and support vector machine (SVM). First, the original signal is divided into several segments by the window function, and the multi-domain features of each segment are extracted respectively to construct a hybrid domain vector; then, the hybrid domain vector is dimensionally reduced by the kernel principal component analysis method, and the principal components with a cumulative contribution rate greater than 90% are selected as the feature parameters, which are input into the SVM model optimized by the gray wolf algorithm for lithology identification. Experiments show that the lithology identification results of the proposed method are consistent with the test rock types, which verifies the feasibility of the proposed method. Compared with other methods, the advantages of the proposed method in classification accuracy and time complexity are verified again.

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Acknowledgments

The authors would like to thank the chang’an University Ph.D. Candidates’ Innovative Ability Cultivation Funding Project (No. 300203211252).

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Authors and Affiliations

  1. National Engineering Laboratory for Highway Maintenance Equipment, Chang’an University, Xi’an, 710064, China

    Kangping Gao & Shengjie Jiao

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  1. Kangping Gao
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  2. Shengjie Jiao
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Correspondence to Kangping Gao.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Communicated by: H. Babaie

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Gao, K., Jiao, S. Research on lithology identification based on multi-sensor hybrid domain information fusion and support vector machine. Earth Sci Inform 15, 1101–1113 (2022). https://doi.org/10.1007/s12145-022-00795-7

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