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Gold Science and Technology ›› 2023, Vol. 31 ›› Issue (5): 794-802.doi: 10.11872/j.issn.1005-2518.2023.05.046

• Mining Technology and Mine Management • Previous Articles     Next Articles

Automatic Recognition Model of Microseismic Signal Based on Improved DCNN Method and Its Application

Yinan YANG1(),Jianhua HU2(),Tan ZHOU1,Fengwen ZHAO1,Mufan WANG1   

  1. 1.School of Resources and Safety Engineering, Central South University, Changsha 410083, Hunan, China
    2.Zijin School of Geology and Mining, Fuzhou University, Fuzhou 350108, Fujian, China
  • Received:2023-03-21 Revised:2023-09-11 Online:2023-10-31 Published:2023-11-21
  • Contact: Jianhua HU E-mail:857674908@qq.com;hujh21@126.com

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Abstract:

In order to accurately identify mine microseismic signals，an improved deep convolutional neural network （DCNN） method was adopted.The spatial domain image of the original image superimposed by the spectrum map obtained by Fourier transform was used as the identification object of microseismic signals.An automatic identification and classification model of microseismic signals based on an improved DCNN method was proposed. The training set，verification set and test set of the IMS microseismic monitoring signal of a lead-zinc mine were established，and the reliability of the method was verified by actual engineering data. The results show that the automatic recognition model of microseismic signals with higher precision and better generalization ability is constructed by using the eigenvalues stacked on the BGR channel of the spectrum map and the spatial domain image as the input of DCNN，which can extract the features efficiently. By evaluating the F1 value，ROC curve and AUC value，the feasibility，effectiveness，and reliability of the improved method is verified.

Key words: DCNN method, microseismic monitoring, signal identification, Fourier transform, automatic recognition model, deep mining

CLC Number: 

  • TD326

Table 1

Implementation of four models on TensorFlow platform"

网络模型数据库标准识别率/%
Le Net-5Mnist99.1
Alex Net（top-5）Image Net201280.2
Google Net（top-5）Image Net201288.9
ResNet152（top-5）Image Net201296.4

Fig.1

Treatment process of Fourier transform"

Fig.2

Sample graph of microseismic events"

Fig.3

Pretreatment process of microseismic events samples"

Fig.4

Image comparison before and after Fourier transform"

Fig.5

ResNet18 network structure model"

Fig.6

Accuracy and loss curves of training set and verification set"

Table 2

Model prediction results and real results"

测试集类型真实情况/个预测结果/个

准确率

/%

微震事件非微震事件微震事件非微震事件
正例50004574391.4
反例05004845290.4

Fig.7

ROC curve and AUC value of the model"

Fig.8

Sample graphs of microseismic signals and blasting signals"

Table 3

Microseismic signal recognition results"

信号编号输入类别输出类别识别结果
微震信号1微震信号微震信号正确
微震信号2微震信号微震信号正确
微震信号3微震信号微震信号正确
爆破信号1爆破信号非微震信号正确
爆破信号2爆破信号非微震信号正确
爆破信号3爆破信号非微震信号正确
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