基于岩样细观图像深度学习的岩性自动分类方法

doi:10.13278/j.cnki.jjuese.20200291

吉林大学学报(地球科学版) ›› 2021, Vol. 51 ›› Issue (5): 1597-1604.doi: 10.13278/j.cnki.jjuese.20200291

基于岩样细观图像深度学习的岩性自动分类方法

熊越晗¹, 刘东燕¹, 刘东升², 王艳磊², 唐小山³

1. 重庆大学土木工程学院, 重庆 400045;
2. 重庆市地质矿产勘查开发局, 重庆 401121;
3. 重庆科技学院建筑工程学院, 重庆 401331

收稿日期:2020-12-03 出版日期:2021-09-26 发布日期:2021-09-29
通讯作者: 刘东燕(1959-),男,教授,博士生导师,主要从事岩土工程、防灾减灾工程方面的研究,E-mail:liudy@cqu.edu.cn E-mail:liudy@cqu.edu.cn
作者简介:熊越晗(1995-),男,硕士研究生,主要从事岩土材料智能识别方面的研究,E-mail:xyh@cqu.edu.cn
基金资助:
重庆市自然科学基金项目（博士后基金）（cstc2020jcyj-bsh0137）；重庆市地质矿产勘查开发局科研项目（DKJ-2020DZJ-A-015）

Automatic Lithology Classification Method Based on Deep Learning of Rock Sample Meso-Image

Xiong Yuehan¹, Liu Dongyan¹, Liu Dongsheng², Wang Yanlei², Tang Xiaoshan³

1. School of Civil Engineering, Chongqing University, Chongqing 400045, China;
2. Chongqing Bureau of Geology and Minerals Exploration, Chongqing 401121, China;
3. School of Civil Engineering and Architecture, Chongqing University of Science and Technology, Chongqing 401331, China

Received:2020-12-03 Online:2021-09-26 Published:2021-09-29
Supported by:
Supported by the Natural Science Foundation of Chongqing (Postdoctoral Fund) (cstc2020jcyj-bsh0137) and the Scientific Research Project of Chongqing Bureau of Geology and Minerals Exploration (DKJ-2020DZJ-A-015)

摘要/Abstract

摘要： 在现阶段的岩土工程中，通常采用人工识别的方法来判别岩样种类，不仅耗时长、专业性强，还易受主观因素影响，准确率不理想。随着计算机技术的发展，机器学习逐渐被应用于岩性的自动识别，开启了岩样分类的新路径。本文以重庆市主城区4种典型岩样（泥岩、砂质泥岩、泥质砂岩和砂岩）的细观图像为研究对象，基于Inception V3卷积网络模型和迁移学习算法，建立了岩样细观图像深度学习模型，并完成了训练学习。结果显示：模型在训练1 000次后，训练集中的分类准确率达到92.77%，验证集中的分类准确率为76.31%。其中，验证集中的砂岩识别准确率为97.28%，泥岩识别准确率为81.85%，泥质砂岩识别准确率为72.59%，砂质泥岩识别准确率为72.35%。与现有的机器学习方法相比，本识别模型不仅可以自动识别岩性极为相近的岩样，而且具有较好的识别准确率、鲁棒性和泛化能力。

关键词: 岩样细观图像, 深度学习, 岩性识别, 卷积神经网络, 自动分类

Abstract: In the current geotechnical engineering, manual identification methods are usually used to distinguish the types of rock samples, which are highly specialized, time-consuming,and susceptible to subjective factors, resulting in low accuracy. With the development of computer technology, machine learning is gradually applied to the automatic lithology identification, which opens up a new path for rock sample classification. Based on this, the authors took the meso-images of the four typical rock samples (mudstone, sandy mudstone, argillaceous sandstone, and sandstone) in the main urban area of Chongqing as the research object. Based on the Inception V3 convolutional network model and migration learning algorithm, a deep learning model of rock sample mesoscopic images was eatablished, and training and learning were completed. The results show that after 1 000 times training, the classification accuracy rate in the training set reaches 92.77%, and the classification accuracy rate in the verification set is 76.31%. Among them, the sandstone recognition accuracy rate is 97.28%, the mudstone recognition accuracy rate in the verification set is 81.85%, the argillaceous sandstone recognition accuracy rate is 72.59%, and the sandy mudstone recognition accuracy rate is 72.35%. Compared with the existing machine learning methods, this recognition model can automatically recognize the rock samples with very similar lithology, and has higher recognition accuracy, robustness, and generalization ability.

Key words: rock sample meso-image, deep learning, lithology identification, convolutional neural network, automatic classification

中图分类号:

P588

熊越晗, 刘东燕, 刘东升, 王艳磊, 唐小山. 基于岩样细观图像深度学习的岩性自动分类方法[J]. 吉林大学学报(地球科学版), 2021, 51(5): 1597-1604.

Xiong Yuehan, Liu Dongyan, Liu Dongsheng, Wang Yanlei, Tang Xiaoshan. Automatic Lithology Classification Method Based on Deep Learning of Rock Sample Meso-Image[J]. Journal of Jilin University(Earth Science Edition), 2021, 51(5): 1597-1604.

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基于岩样细观图像深度学习的岩性自动分类方法

Automatic Lithology Classification Method Based on Deep Learning of Rock Sample Meso-Image

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