论文标题
基于串联卷积神经网络的岩石学薄部分图像中的岩石分类图像
Rock Classification in Petrographic Thin Section Images Based on Concatenated Convolutional Neural Networks
论文作者
论文摘要
岩石分类在岩石力学，岩石学，采矿工程，岩浆过程以及与地球科学有关的许多其他领域中起着重要作用。这项研究提出了一种串联的卷积神经网络（CON-CNN）方法，用于基于岩石学薄部分对地质岩石类型进行分类。在此，使用平面偏振光（PPL）和交叉偏振光（XPL）获取薄截面图像作为基本数据。在进行必要的预处理分析之后，将PPL和XPL图像及其综合图像（CI）纳入了三个卷积神经网络（CNN）中，包括相同的结构，以实现初步分类；这些图像是通过采用融合主成分分析（PCA）来开发的。随后，通过使用最大似然检测来获得全面的分类结果，通过使用最大似然检测来连接CNN的结果。最后，由于外观相似的矿物的比例差异，应用了统计修订以解决错误分类。在这项研究中，制造了13种92种岩石样品，196个岩石学薄部分，588张图像和63504张图像贴片，用于训练和验证CON-CNN。五倍的交叉验证表明，提出的方法提供了89.97％的总体精度，这有助于岩石薄层中岩石分类的自动化。
Rock classification plays an important role in rock mechanics, petrology, mining engineering, magmatic processes, and numerous other fields pertaining to geosciences. This study proposes a concatenated convolutional neural network (Con-CNN) method for classifying the geologic rock type based on petrographic thin sections. Herein, plane polarized light (PPL) and crossed polarized light (XPL) were used to acquire thin section images as the fundamental data. After conducting the necessary pre-processing analyses, the PPL and XPL images as well as their comprehensive image (CI) were incorporated in three convolutional neural networks (CNNs) comprising the same structure for achieving a preliminary classification; these images were developed by employing the fused principal component analysis (PCA). Subsequently, the results of the CNNs were concatenated by using the maximum likelihood detection to obtain a comprehensive classification result. Finally, a statistical revision was applied to fix the misclassification due to the proportion difference of minerals that were similar in appearance. In this study, 13 types of 92 rock samples, 196 petrographic thin sections, 588 images, and 63504 image patches were fabricated for the training and validation of the Con-CNN. The five-folds cross validation shows that the method proposed provides an overall accuracy of 89.97%, which facilitates the automation of rock classification in petrographic thin sections.
