The mineral intelligence identification method based on deep learning algorithms

doi:10.13745/j.esf.sf.2020.5.45

Abstract

Abstract:

Mineral classification plays an important role in many research fields. Intelligent mineral identification based on deep learning brings a new development direction to these fields, it can effectively save labor costs as well as reducing classification errors. The purpose of this paper is to study an accurate, efficient and versatile intelligent mineral identification method by deep learning. We trained and tested this method on five kinds of minerals: quartz, hornblende, biotite, garnet and olivine. We used the convolution neural network, commonly applies to image analysis, to establish the model and designed the model structure based on residual network (ResNet). In order to support deep learning, we collected microscopic imaging data sets of five kinds of minerals independently, and used them to train, verify and test the model. Besides, we also expanded the data sets for training through reasonable data augmentation. In terms of structural design of the convolutional neural network, we selected ResNets-18 as the framework and finally trained a successful mineral identification model achieving 89% accuracy in the test.

Key words: deep learning, mineral classification, computer vision, convolutional neural network, residual neural network

CLC Number:

P57
TP391.41

GUO Yanjun, ZHOU Zhe, LIN Hexun, LIU Xiaohui, CHEN Danqiu, ZHU Jiaqi, WU Junqi. The mineral intelligence identification method based on deep learning algorithms[J]. Earth Science Frontiers, 2020, 27(5): 39-47.

Figures/Tables 14

Table 1 Network structure of ResNet-18[25]

层名	输出大小	参数
conv1	112×112	7×7, 64, stride 2
conv2_x	56×56	3×3 max pool, stride 2
conv2_x	56×56	$3 × 3 64 3 × 3 64$ ×2
conv3_x	28×28	$3 × 3 128 3 × 3 128$ ×2
conv4_x	14×14	$3 × 3 256 3 × 3 256$ ×2
conv5_x	7×7	$3 × 3 512 3 × 3 512$ ×2
	1×1	average pool, 1 000-d fc, softmax
FLOPs		1.8×10⁹

Table 1 Network structure of ResNet-18[25]

层名	输出大小	参数
conv1	112×112	7×7, 64, stride 2
conv2_x	56×56	3×3 max pool, stride 2
conv2_x	56×56	$3 × 3 64 3 × 3 64$ ×2
conv3_x	28×28	$3 × 3 128 3 × 3 128$ ×2
conv4_x	14×14	$3 × 3 256 3 × 3 256$ ×2
conv5_x	7×7	$3 × 3 512 3 × 3 512$ ×2
	1×1	average pool, 1 000-d fc, softmax
FLOPs		1.8×10⁹

Fig.1 A structure chart of ResNet-18

Fig.2 Basic unit of residual learning. Adapted from [25].

Fig.3 Data processing flow chart

Fig.4 Model training and testing flow chart

Fig.5 Model classification example

Fig.6 Sample collection example

Fig.7 Data argumentation based on image flipping

Fig.8 Data argumentation based on scale transformation

Fig.9 Loss changes during training

Fig.10 Change of accuracy on validation set

Table 2 Test set score

参数	矿物的识别情况
参数	角闪石	橄榄石	黑云母	石英	石榴石	总计
数量	20	20	20	20	20	100
正确数	20	15	16	18	20	89
准确率	100%	75%	80%	90%	100%	89%

Table 3 Confusing analysis of test results

实测矿物	预测概率
实测矿物	角闪石	橄榄石	黑云母	石英	石榴石
角闪石	1.00	0.00	0.00	0.00	0.00
橄榄石	0.00	0.75	0.00	0.15	0.10
黑云母	0.10	0.10	0.80	0.00	0.00
石英	0.00	0.10	0.00	0.90	0.00
石榴石	0.00	0.00	0.00	0.00	1.00

Fig.11 Presentation of erred cases

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