Rock lithology classification and parameter sensitivity analysis based on wavelet scattering transform and support vector machine

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成果类型：

期刊论文

作者：

Zhang, Sheng;Huang, Ning;Chen, Qianqian;Deng, Zongwei;Zhang, Liang

通讯作者：

Zhang, S

作者机构：

[Zhang, Sheng] Hunan City Univ, Sch Management, Yiyang 413000, Hunan, Peoples R China.

[Zhang, Liang; Huang, Ning; Zhang, Sheng; Deng, Zongwei] Hunan City Univ, Sch Civil Engn, Yiyang 413000, Hunan, Peoples R China.

[Zhang, Liang; Zhang, Sheng; Deng, Zongwei] Hunan City Univ, Higher Educ Inst Hunan Prov, Key Lab Green Bldg & Intelligent Construct, Yiyang 413000, Hunan, Peoples R China.

[Chen, Qianqian] Hunan Commun Polytech, Inst Civil Engn, Changsha 410000, Hunan, Peoples R China.

通讯机构：

[Zhang, S ] H

Hunan City Univ, Sch Management, Yiyang 413000, Hunan, Peoples R China.

Hunan City Univ, Sch Civil Engn, Yiyang 413000, Hunan, Peoples R China.

Hunan City Univ, Higher Educ Inst Hunan Prov, Key Lab Green Bldg & Intelligent Construct, Yiyang 413000, Hunan, Peoples R China.

语种：

英文

关键词：

rock classification;wavelet scattering transform;support vector machine;sensitivity analysis;deep learning

期刊：

Measurement Science And Technology

ISSN：

0957-0233

年：

2025

卷：

期：

页码：

046133

DOI：

10.1088/1361-6501/adc61d

基金类别：

National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809 [22A0562, 23C0664]; Scientific Research Project of Hunan Provincial Department of Education [202319]; Science and Technology Program of Hunan Provincial Department of Transportation [202211527017]; National Undergraduate Training Program for Innovation and Entrepreneurship of China [52308399]; National Natural Science Foundation of China

机构署名：

本校为第一且通讯机构

院系归属：

土木工程学院

摘要：

In geological exploration and tunnel/underground engineering, precise, rapid, and intelligent rock lithology identification is crucial. A wavelet scattering transform-support vector machine (WST-SVM) rock image classification method is proposed that combines WST with SVM to address the limitations of conventional convolutional neural networks reliant on annotated samples. The method extracts multi-scale features from rock images using WST and trains an SVM classifier, achieving superior performance in test accuracy, macro-average precision, recall, and F1-score on a dataset of six rock types. ...

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Rock lithology classification and parameter sensitivity analysis based on wavelet scattering transform and support vector machine

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