Rock thin section image recognition and classification based on VGG model
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摘要:
岩石薄片图像的复杂性和多解性，导致岩石薄片分类难度较大。尝试将深度学习方法应用于岩石薄片图像分类。实验选取了安山岩、白云岩、花岗岩等6种常见岩石种类的薄片图像，每类1000张图像作为实验数据，建立了岩石薄片分类的VGG模型，经过9万次训练后，测试集识别准确率达到了82%。对实验结果进行了分析，发现相似组成成分的岩石图像容易混淆，如白云岩与鲕粒灰岩均属于碳酸盐岩，容易相互误判。在安山岩特征图中提取出了斜长石斑晶和微晶及隐晶质或玻璃质基质，在鲕粒灰岩特征图中提取了鲕粒及填隙物中的亮晶方解石，也验证了方法的可靠性。
Abstract:The complexity and multiple solutions of rock thin section images lead to the difficulty in classification of rock thin sections. This paper attempts to apply the deep learning method to the classification of rock thin images. Thin section images of 6 common rock types, such as andesite, dolomite and granite, were selected in the experiment, and 1000 images of each type were used as experimental data. The VGG model was established, and the identification accuracy of the verification set reached 82% after 90, 000 iterations. Based on the analysis of the experimental data, the authors found that the rock images with similar compositions are easy to be confused; for example, dolomite and oolitic limestone are both carbonate rocks and it is easy to misjudge each other. Plagioclase porphyry, microcrystalline and cryptocrystalline or vitreous matrix were extracted from the andesite characteristic diagram, and oolitic and interstitial materials were extracted from the oolitic limestone characteristic diagram. The result obtained by the authors proves that the VGG model is effective in the classification of rock thin section.
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Key words:
- rock thin section images /
- deep learning /
- VGG /
- feature extraction
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表 2 薄片图像分类混淆矩阵
Table 2. Confusion matrix of rock thin section images classification
准确率 安山岩 白云岩 鲕粒灰岩 花岗岩 岩屑砂岩 石英砂岩 安山岩 0.76 0.07 0.04 0.07 0.04 0.03 白云岩 0.12 0.74 0.10 0.00 0.01 0.00 鲕粒灰岩 0.05 0.11 0.80 0.03 0.01 0.01 花岗岩 0.05 0.01 0.01 0.79 0.05 0.09 岩屑砂岩 0.00 0.01 0.00 0.00 0.98 0.01 石英砂岩 0.03 0.03 0.00 0.05 0.00 0.89 
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