Automatic Recognition of Faults in Mining Areas Based on Convolutional Neural Network

Guangui Zou, Hui Liu, Ke Ren, Bowen Deng and Jingwen Xue
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Guangui Zou: State Key Laboratory of Coal Resources and Safety Mining, China University of Mining and Technology, Beijing 100083, China
Hui Liu: College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China
Ke Ren: College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China
Bowen Deng: College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China
Jingwen Xue: College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China

Energies, 2022, vol. 15, issue 10, 1-18

Abstract: Tectonic interpretation is critical to a coal mine’s safe production, and fault interpretation is an essential component of seismic tectonic interpretation. With the increasing necessity for accuracy in fault interpretation in coal mines, it is increasingly challenging to achieve greater accuracy only through traditional fault interpretation. The convolutional neural network (CNN) is a machine learning method established in recent years and it has been widely applied in coal mine fault interpretation because of its powerful feature-learning and classification capabilities. To improve the accuracy and efficiency of fault interpretation in coal mines, an automatic seismic fault identification method based on the convolutional neural network has been developed. Taking a mining area in eastern Yunnan province as an example, the CNN model realized automatic identification of faults with eight seismic attributes as feature inputs, and the model-training parameters were optimized and compared. Ten faults in the area were selected to analyze the prediction effect, and a comparative experiment was done with model structure parameters and training sets. The experimental results indicate that the training parameters have a significant influence on the training time and testing accuracy of the model, while structural parameters and training sets affect the actual prediction effect of the model. By comparison, the fault results predicted by the convolutional neural network are in good agreement with the manual interpretation, and the accuracy of the model is more than 85%, which proves that this method has certain feasibility and provides a new way to shorten the fault interpretation period and improve the interpretation accuracy.

Keywords: convolutional neural network; fault interpretation; model parameters; training set (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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