Mineral component identification and intelligent interpretation: Information sharing and transfer learning across different lithologies

doi:10.13745/j.esf.sf.2024.5.8

Abstract

Abstract:

In earth sciences the rock microscopic data collection process is both labor-intensive and inefficient, which have a negative impact on research cost/reliability and open data sharing. Additionally, rock heterogeneity and variation in data collection methods typically result in small-scale datasets—this poses a significant challenge to deep learning frameworks that rely on large-scale datasets for training. To address this issue, we investigate how transfer learning can facilitate information sharing across different rock types and enhance model performance in tasks such as mineral identification and intelligent interpretation. By compiling thin section image datasets, taking in diverse rock sampling regions, rock types, mineral compositions, viewed under varying viewing modes, we delve into the mechanisms of transfer learning across different observational targets and methods, focusing on the deep representation of geological information. Our findings not only highlight the pivotal role of transfer learning in promoting information sharing and improving model performance within the field of geosciences, but also lay a foundation for the automatic and intelligent integration of geological insights. According to experimental results, transfer learning led to significant accuracy improvement, from 53.3% to 98.73%, in intelligent interpretation task, and a nearly 10% improvement in mineral identification task. These results convincingly showcase the great potential of transfer learning in addressing practical problems in geology as well as enhancing model generalization, model performance, and model stability.

Key words: transfer learning, thin section mineral composition identification, thin section image intelligent interpretation, geological understanding integration

CLC Number:

LIU Ye, HAN Yubo, ZHU Wenrui. Mineral component identification and intelligent interpretation: Information sharing and transfer learning across different lithologies[J]. Earth Science Frontiers, 2024, 31(4): 95-111.

Figures/Tables 30

Fig.1 Sample of thin section images in datasets A (a) and B (b, c)

Fig.2 Research methodology flowchart

Fig.3 Dataset preprocessing scheme

Fig.4 Preprocessing workflow for monolith rock dataset

Fig.5 Sample of thin section images in dataset A-a

Fig.6 Sample of thin section images in dataset B-a

Table 1 Description of component recognition datasets A and B

数据集	数据集A(图像)						数据集B(图像)
(预处理)组分分类	A-a						B-a
类别	石英	高岭石	岩屑	杂基	孔隙	其他	石英	斜长石	钾长石
数量	1 200	1 200	1 200	1 200	1 200	1 200	50	50	50

Fig.7 Sample of thin section images in dataset C

Fig.8 Sample of thin section images in dataset D

Fig.9 Flowchart of transfer learning method

Fig.10 Pre-trained component recognition model

Fig.11 Transfer learning model based on pre-trained model

Fig.12 Convolutional block attention module

Fig.13 Semantic model used in intelligent interpretation

Fig.14 Semantic model encoder

Fig.15 Semantic model decoder

Fig.16 GRU training process

Table 2 Hyperparameters in component recognition model

模型	预训练模型						组分识别迁移模型
超参数	学习率	批大小	迭代次数	优化器	损失函数	学习率衰减	学习率	批大小	迭代次数	优化器	损失函数	学习率衰减
	0.003	16	80	Adam	交叉熵损失	每30次迭代将学习率乘以0.5	0.000 3	8	40	Adam	交叉熵损失	每20次迭代将学习率乘以0.5

Table 3 Hyperparamerters in semantic model

模型	智能解释语义预训练模型
超参数	字典长度	最大语句长度	嵌入层尺寸	GRU隐藏层尺寸	学习率	批大小	迭代次数	优化器	损失函数	学习率衰减
	30	32	16	512	0.000 2	64	150	Adam	交叉熵损失	每5次迭代将学习率乘以0.1
模型	智能解释语义迁移模型
超参数	字典长度	最大语句长度	嵌入层尺寸	GRU隐藏层尺寸	学习率	批大小	迭代次数	优化器	损失函数	学习率衰减
	16	18	16	512	0.0002	16	50	Adam	交叉熵损失	每5次迭代将学习率乘以0.1

Fig.17 Evolution of the predictive accuracy of pre-trained component recognition model during training (blue) and testing (green)

Fig.18 Confusion matrices for pre-trained component recognition model on dataset A-a

Fig.19 Comparisons of component recognition model performances between transfer learning model (blue) and the original model (orange) during training (a) and testing (b)

Table 4 Comparison of the predictive accuracy of semantic model with (blue) and without (orange) transfer learning

模型	类别	准确率/%
原始模型	训练集	86.6
原始模型	测试集	87.5
迁移模型	训练集	92.5
迁移模型	测试集	96.8

Fig.20 Confusion matrices for component recognition model on dataset B-a with (a) and without (b) transfer learning

Fig.21 Component recognition results on dataset A

Fig.22 Component recognition results on dataset B

Fig.23 Semantic interpretation results using pre-trained model

Fig.24 Comparisons of semantic model performances between transfer learning model (blue) and the original model (orange) during training (a) and testing (b)

Table 5 Comparison of the descriptive accuracy of semantic model with and without transfer learning

模型	类别	准确率/%
原始模型	训练集	57.6
原始模型	测试集	53.3
迁移模型	训练集	100
迁移模型	测试集	98.3

Fig.25 Semantic interpretation results using transfer learning model

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