Automatic Recognition Model of Microseismic Signal Based on Improved DCNN Method and Its Application
Received date: 2023-03-21
Revised date: 2023-09-11
Online published: 2023-11-21
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.
Yinan YANG , Jianhua HU , Tan ZHOU , Fengwen ZHAO , Mufan WANG . Automatic Recognition Model of Microseismic Signal Based on Improved DCNN Method and Its Application[J]. Gold Science and Technology, 2023 , 31(5) : 794 -802 . DOI: 10.11872/j.issn.1005-2518.2023.05.046
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