Lithology identification method of rock thin section images based on MobileViT

WANG Qiong; YANG Jie; HUO Fengcai; DONG Hongli; REN Weijian; YU Tao

doi:10.12097/gbc.2022.10.002

2024 Vol. 43, No. 6

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WANG Qiong, YANG Jie, HUO Fengcai, DONG Hongli, REN Weijian, YU Tao. 2024. Lithology identification method of rock thin section images based on MobileViT. Geological Bulletin of China, 43(6): 938-946. doi: 10.12097/gbc.2022.10.002

Citation:

WANG Qiong, YANG Jie, HUO Fengcai, DONG Hongli, REN Weijian, YU Tao. 2024. Lithology identification method of rock thin section images based on MobileViT. Geological Bulletin of China, 43(6): 938-946. doi: 10.12097/gbc.2022.10.002

Lithology identification method of rock thin section images based on MobileViT

1.
School of Electrical Information Engineering, Northeast Petroleum University, Daqing 163318, Heilongjiang, China
2.
Artificial Intelligence Energy Research Institute of NEPU, Daqing 163318, Heilongjiang, China
3.
Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Daqing 163318, Heilongjiang, China
4.
Sanya Offshore Oil & Gas Research Institue, Sanya 572000, Hainan, China
5.
Technology Research Institute of the Fourth Oil Production Plant of Daqing Oilfield Company Limited, Daqing 163511, Heilongjiang, China

More Information

Corresponding author: HUO Fengcai, huofc@126.com

Received Date: 05 October 2022

Revised Date: 08 December 2022

Publish Date: 15 June 2024

Abstract

Abstract

The rock thin−section images contain a large amount of geological feature information that cannot be observed with the naked eye. The lithology identification of rock thin−section images lays the foundation for subsequent oil exploration and production. Aiming at the problems of unbalanced lithology identification data set and many identification model parameters, an improved lightweight MobileViT model is proposed to model and analyze the rock slice images covering more than 90% of common lithology. First, to enable the model to better learn the unique features contained in each type of rock slice image, adding numbers of the dataset set is performed on the image. Secondly, use GELU to replace the ReLU6 of the MV2 module in MobileViT as the activation function of the module, which effectively solves the problem of neuron death and improves the convergence speed of the model. Finally, the training set and the test set are divided, the cosine annealing algorithm is used to automatically update the learning rate, and the transfer learning is used to speed up the training process, so as to realize the automatic identification of rock slice images. The experimental results show that the accuracy of the improved MobileViT for lithology identification is 82.8%, and the model parameters are only 7.66M, which has good robustness.
- rock thin section /
- MobileViT /
- lithology identification /
- cosine annealing /
- light weight

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References

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