Rock and mineral thin section identification based on deep learning

doi:10.13745/j.esf.sf.2023.6.7

Abstract

Abstract:

Rock-mineral microscopic image identification is one of the basic means of rock and mineral identification, which is of great significance to the exploration of geological resources. Thin-section microscopic images are generally carried out in the laboratory. This work is tedious and time-consuming, requires a lot of human resources, and the accuracy is limited by the experience of the expert. Deep learning intelligent image recognition algorithm can extract the deep features of microscopic images by convolutional neural network, to achieve the purpose of fast and accurate classification and recognition of microscopic images. In this study, the PyCharm platform is used as the deep learning framework, and the data set that can be applied to the classification and recognition of rock-mineral microscopic images is made based on six data sets such as the teaching rock slice dataset of Nanjing University and the Carboniferous limestone microscopic image dataset of South North China on the China Science Data Network. We design a VGG convolutional neural network model. The model can analyze the feature information in the deep layer of the whole rock slice image and the single mineral image respectively, to achieve the purpose of identifying rock slices. The test results show that with the increase of model training times, the loss function between the predicted value and the real value is decreasing, and the recognition accuracy is increasing. After 50 and 30 cycles of training, the loss function and recognition accuracy of the model have been basically convergent. The recognition success rate of the model for the microscopic image test set is higher than 90%, indicating that the model has a good feature extraction effect for the image and can complete the task of rock-mineral microscopic image recognition. Through the research of this paper, it can be realized that deep learning has high efficiency and accuracy for dealing with such tasks as rock and mineral identification. Developing relevant models and applying them to front-end software can speed up the speed of mineral resources exploration and has important application significance for production practice.

Key words: rock identification, deep learning, convolutional neural network, machine learning, image recognition

CLC Number:

TP753
P585

ZHANG Lijun, LU Wenhao, ZHANG Jiandong, PENG Guangxiong, BU Jiancai, TANG Kai, XIE Jiancheng, XU Zhibin, YANG Haiyan. Rock and mineral thin section identification based on deep learning[J]. Earth Science Frontiers, 2024, 31(3): 498-510.

Figures/Tables 19

Fig.1 Schematic diagram of research technical route

Fig.2 Dataset of rock thin section microphotography

Fig.3 Dataset of mineral thin section microphotography

Table 1 Summary of rock thin section microphotography

类型	数量	单位
白云岩	155	张
灰岩	151	张
砂岩	332	张
总体	638	张

Table 2 Summary of mineral thin section microphotography

类型	数量	单位
橄榄石	174	张
普通辉石	184	张
角闪石	106	张
黑云母	244	张
斜长石	112	张
红柱石	93	张
十字石	122	张
石榴子石	95	张
阳起石	34	张
鲕粒	103	张
总体	1 267	张

Fig.4 Flip original microphotography (a), vertical flip microphotography (b), and horizontal flip microphotography (c)

Fig.5 Clip original microphotography (a), and random clip flip microphotography (b)

Fig.6 Rotating original microphotography (a), and rotating microphotography (b)

Fig.7 VGG model structure diagram

Fig.8 Basic training flow chart of VGG convolution neural network for rock

Fig.9 Basic training flow chart of VGG convolution neural network for mineral

Fig.10 Loss function diagram for rock microphotography classification

Table 3 Recognition probability of microphotography classification in rock training set

训练集图像	分类识别概率/%
训练集图像	灰岩	砂岩	白云岩
灰岩	97.642	0.562	2.457
砂岩	1.125	98.214	0.661
白云岩	1.233	1.224	97.543

Fig.11 Accuracy diagram for rock microphotography classification

Fig.12 Comparison diagram of accuracy in VGG11, VGG16, and VGG19 verification sets

Fig.13 Loss function diagram of mineral microphotography classification

Table 4 Recognition probability of microphotography classification in mineral training set

训练集图像	分类识别概率/%
训练集图像	橄榄石	辉石	角闪石	黑云母	斜长石	红柱石	十字石	石榴子石	阳起石	鲕粒
橄榄石	98.863	0.31	0.128	0.357	0.275	0	0.382	0.532	0.457	0.028
辉石	0.321	98.61	0.41	0.146	0.246	0.532	0.253	0.248	0.254	0.148
角闪石	0.691	0.105	97.932	0.213	0.183	0.425	0.537	0.396	0.098	0.258
黑云母	0.241	0.261	0.025	98.254	0.261	0.216	0.429	0	0.248	0.427
斜长石	0.032	0.327	0.251	0.023	98.124	0.527	0.053	0.268	0.014	0.658
红柱石	0.124	0.141	0.521	0.245	0	96.534	0.293	0.421	0.054	0.320
十字石	0.231	0	0.326	0.612	0.342	0.623	97.632	0.134	0.147	0.247
石榴子石	0.052	0.056	0.153	0	0.279	0.495	0.327	97.894	0.014	0.089
阳起石	0.04	0.047	0.059	0.125	0.124	0.325	0.058	0.051	98.673	0.013
鲕石	0	0.143	0.195	0.025	0.166	0.323	0.036	0.056	0.041	93.812

Fig.14 Accuracy diagram of mineral microphotography classification

Fig.15 Comparison diagram of accuracy in VGG11, VGG16, and VGG19 verification sets

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