Abstract
The feature extraction of high-precision microseismic signals is an important prerequisite for multicategory recognition of microseismic signals, and it is also an important basis for intelligent sensing modules in smart mines. Aiming at the problem of unobvious feature extraction of multiclass mine microseismic signals, this paper is based on the unsupervised learning method in the deep learning method, combined with wavelet packet energy ratio and empirical modulus singular value decomposition, and proposes a method based on wavelet packet energy and empirical modulus singular value decomposition and proposes a method (M-W&E) based on wavelet packet energy and empirical modulus singular value decomposition. This method firstly performs empirical modulus singular value decomposition and wavelet packet energy ratio on the microseismic signal to construct the basic feature vector and then uses the unsupervised learning algorithm to perform the unsupervised learning method feature fusion of the basic feature vector to construct the fused feature vector. After visualization by t-SNE, various distinctions in the fusion feature vector are more obvious. After testing the fusion feature classification using SVM, it is found that the recognition rate of the new feature after feature fusion is better than that of a single wavelet packet empirical energy component and singular value of empirical modulus, which basically meets the engineering needs and is a mine microseism. The signal extraction and feature enhancement fusion of multiclass samples provide a new idea.
Highlights
With the substantial development of the mathematics industry and the computer industry, there has been some progress in processing time series data [1, 2]
Shang et al [10] studied the construction of IMF components after EMD decomposition of the microseismic signal, matrix, and singular value decomposition and used the decomposition class with SVM to realize the distinction between microseismic signal and blasting signal
Feature fusion is a valuable technique for improving the apparentness of features. e purpose of this paper is to analyze the feature fusion processing of the microseismic signal using the singular value of wavelet packet capacity energy ratio and the component singular value of EMD energy using the unsupervised learning algorithm of deep learning
Summary
With the substantial development of the mathematics industry and the computer industry, there has been some progress in processing time series data [1, 2]. With the development and generalized application of the big data industry, the microseismic system is no longer just the processing of microseismic signals is required, and it is required to be used as a sensing device in mine production. Deep learning methods for fusing time series data and signals have gained widespread attention [15, 16], mainly due to the rapid development of the big data industry. E purpose of this paper is to analyze the feature fusion processing of the microseismic signal using the singular value of wavelet packet capacity energy ratio and the component singular value of EMD energy using the unsupervised learning algorithm of deep learning Feature fusion is a valuable technique for improving the apparentness of features. e purpose of this paper is to analyze the feature fusion processing of the microseismic signal using the singular value of wavelet packet capacity energy ratio and the component singular value of EMD energy using the unsupervised learning algorithm of deep learning
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