摘要:砂岩显微图像分类是地质学研究中一项基本工作，在油气储集层评估等方面有重要意义.在实现自动分类时，由于砂岩显微图像具有复杂多变的显微结构，人工定义特征对砂岩显微图像的表示能力有限.此外，由于样本采集和标注成本高昂，带标记的砂岩显微图像很少.提出一种面向小规模数据集的基于卷积神经网络的特征表示方法FeRNet，以便有效地捕获砂岩显微图像的语义信息，提高对砂岩显微图像的特征表示能力.FeRNet网络结构简单，可降低网络对带标记图像数据量的要求，防止参数过拟合.针对带标记砂岩显微图像数量不足的问题，提出了图像扩增预处理方法及基于卷积自编码网络的权重初始化策略，降低了因数据不足造成的过拟合风险.基于采自西藏地区的砂岩显微图像数据集设计并进行实验，实验结果表明，在带标记砂岩显微图像数据不足的情况下，图像扩增和卷积自编码网络可以有效地改善FeRNet网络的训练效果，通过FeRNet网络提取的特征对砂岩显微图像的表示能力优于人工定义特征.



Abstract:The classification of microscopic sandstone images is a basic work in geological research, and it has an important significance in the evaluation of oil and gas reservoirs. In the automatic classification of microscopic sandstone images, due to their complex and variable micro-structures, the hand-crafted features have limited abilities to represent them. In addition, since the collection and labeling of sandstone samples are costly, labeled microscopic sandstone images are usually few. In this study, a convolutional neural network based feature representation method for small-scale data sets, called FeRNet, is proposed to effectively capture the semantic information of microscopic sandstone images and enhance their feature representation. The FeRNet has a simple structure, which reduces the quantity requirements for labeled images, and prevents the overfitting. Aiming at the problem of insufficient labeled microscopic sandstone image, the image augmentation preprocessing and a CAE network-based weight initialization strategy are proposed, to reduce the risk of overfitting. Based on the microscopic sandstone images collected from Tibet, the experiments are designed and conducted. The results show that both image augmentation and CAE network can effectively improve the training of FeRNet network, when the labeled microscopic sandstone images are few; and the FeRNet features are more capable of the representations of microscopic sandstone images than the hand-crafted features.



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