Experiments on image data augmentation techniques for geological rock type classification with convolutional neural networks  

作　　者：Afshin Tatar Manouchehr Haghighi Abbas Zeinijahromi 

机构地区：[1]School of Chemical Engineering,Discipline of Mining and Petroleum Engineering,The University of Adelaide,Adelaide,SA 5005,Australia

出　　处：《Journal of Rock Mechanics and Geotechnical Engineering》2025年第1期106-125,共20页岩石力学与岩土工程学报(英文)

摘　　要：The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.

关 键 词：Deep learning(DL) Image analysis Image data augmentation Convolutional neural networks(CNNs) Geological image analysis Rock classification Rock thin section(RTS)images 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程] TU45[建筑科学—岩土工程]