Direct current resistivity method for the advance prediction of water Hazards in coal mines based on an artificial neural network

被引：0

作者：

Li Y. ^{[1
,2
]}

Cheng J. ^{[2
]}

Lu J. ^{[2
]}

Dai F. ^{[2
]}

Wu Z. ^{[2
]}

Fang Z. ^{[2
]}

Zhao J. ^{[2
]}

机构：

[1] China Coal Research Institute, Beijing

[2] CCTEG Xi’an Research Institute (Group) Co., Ltd., Xi’an

来源：

Meitiandizhi Yu Kantan/Coal Geology and Exploration | 2023年 / 51卷 / 06期

关键词：

advance detection; artificial neural network; DC resistivity method; response error matching; unstructured mesh generation;

D O I：

10.12363/issn.1001-1986.22.07.0545

中图分类号：

学科分类号：

摘要：

The mine Direct Current (DC) resistivity method has the characteristics of high construction efficiency and low cost, which is widely used in the advanced detection of water hazards in coal mine heading face.With the continuous deepening of applications, higher requirements have been put forward for the advanced detection signal processing method of mine DC resistivity method. Based on the response information database obtained by finite element method and data reconstruction, the Levenberg-Marquardt (L-M) artificial neural network method is used to realize the advanced prediction of mine DC resistivity method. First, this study established an advanced prediction model of the DC resistivity method using unstructured mesh and iterative finite element method BiCGSTAB; matched the numerically simulated responses with the measured responses using the empirical mode decomposition (EMD) method; and proposed the prediction mechanism based on the advance detection distance and anomaly rate. As a result, 3 000 sets of 23-dimensional reconstructed responses were obtained. Subsequently, the artificial neural network model is constructed by L-M algorithm. Finally, using the trained artificial neural network, this study predicted the water hazards at the heading face based on the measured data and numerically simulated responses. The results of this study are as follows: (1) For the numerically simulated responses, the mine DC resistivity method based on the L-M artificial neural network can effectively detect the water hazard anomalies within the advance detection distance of 100 m. The mean square error in the detection was 0.002 47, and test samples exhibited that the maximum error of the advance detection distance was less than 0.6 m; (2) As shown by the measured data, the accuracy rate of the detection was 67% in the whole study and was higher than 85% for water hazard anomalies at the advance detection distance of 15-80 m. Therefore, the L-M artificial neural network can be applied to the advance prediction of water disasters based on the mine DC resistivity method. The research results are of great significance for the improvement and wide applications of the mine DC resistivity method-based advance detection of water hazards at the heading face of coal mines. © The Author(s) 2023.

引用

页码：185 / 193

页数：8

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