Deep convolutional neural network target detection algorithm for coal mine fault detection

Zhang Chunxiang; Tang Yexiu; Zou Guangui; Zeng Yiwen; Fan Zhuo

doi:10.19606/j.cnki.jmst.2023.06.001

Volume 8 Issue 6

Dec. 2023

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Article Navigation > Journal of Mining Science and Technology > 2023 > 8(6): 733-743

Zhang Chunxiang, Tang Yexiu, Zou Guangui, Zeng Yiwen, Fan Zhuo. Deep convolutional neural network target detection algorithm for coal mine fault detection[J]. Journal of Mining Science and Technology, 2023, 8(6): 733-743. doi: 10.19606/j.cnki.jmst.2023.06.001

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Deep convolutional neural network target detection algorithm for coal mine fault detection

doi: 10.19606/j.cnki.jmst.2023.06.001

1.
College of Geosciences and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
2.
State Key Laboratory of Coal Resources and Safe Mining, Beijing 100083, China
3.
School of Mechanics and Civil Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China

Received Date: 2023-04-24
Rev Recd Date: 2023-07-02
Publish Date: 2023-12-31

Abstract

Abstract

Fault interpretation plays an important role in the field of coal mine safety. The development of neural network gives rise to many intelligent seismic data interpretation and processing schemes based on neural network algorithm. This study ①selected the Faster R-CNN target detection algorithm more suitable for fault recognition by comparing different deep convolutional neural network target detection algorithms. ②tested AlexNet, residual network ResNet50 and ResNet101 feature extraction networks through seismic forward modeling with various geological characteristics. It is found that ResNet101 feature extraction network has better performance in fault detection. ③constructed a fault detection model based on the preferred ResNet101 feature extraction network and Faster R-CNN target detection algorithm, and detected the actual seismic data. Results show that the object detection algorithm based on deep convolutional neural network shows satisfactory generalization ability in fault detection. It could improve the fault interpretation efficiency, and has potential in application.
- deep convolutional neural networks,
- fault detection,
- seismic orthorectification models,
- target detection

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References

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Deep convolutional neural network target detection algorithm for coal mine fault detection

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