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Mine Microseismic Time Series Data Integrated Classification Based on Improved Wavelet Decomposition and ELM

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Abstract

Coal mine accidents induced by large energy microseisms are frequent and common. Classification of mine microseismic events is an important part of accident treatment and post-disaster recovery production. With the wide application of microseismic monitoring systems, they always generate a large number of microseismic monitoring time series data. Because of microseismic time series, data usually contain a large amount of random environmental noises, and different types of microseismic events have greatly different influences on mining work. So that, to effectively classify microseismic time series data is a key and difficult problem. Aiming at these characters of microseismic data, existing classification methods still have some problems, such as low noise reduction efficiency, low classification accuracy, and poor stability. In terms of these questions, this paper proposes an integrated classification model wavelet dynamic particle swarm optimization random technology extreme learning machine, named WA-DPSO-RTELM. In view of wavelet threshold functions defect with discontinuity and error, firstly, this paper proposes an improved wavelet threshold denoising method and realizes the effective denoising of data and proposes a PSO algorithm with dynamic adjustment factor to realize adaptive denoising. Secondly, this paper proposes a weighted integrate classification method to classify data. In terms of randomness of ELM parameters and uncertainly of the number of ELM hidden nodes leading to the poor classification performance, this paper proposes an ELM’s weight construction method and uses improved ELM-based classifiers to make up for the differences between classifiers and makes the classification results more stable. Finally, in terms of experimental results, the effectiveness of denoising method and integrated classification method is verified by experimental tests. First, in terms of denoising, the proposed method is compared with EMD, Kalman filtering, and DF-CNN methods, and the signal-to-noise ratio (SNR) and mean square deviation (MSE) are improved by about 1.04 and 0.16 on average. Second, it is compared with other advanced methods in classification, and the accuracy and recall are improved by about 1.36 and 1.15 on average. Effective classification of microseismic time series data is becoming more and more important in people’s daily life. This paper combines the advantages of wavelet denoising method and improves threshold function to realize adaptive wavelet coefficients to effectively remove the noise and uses the weighted integrated classification method of microseismic time series data based on ELM to realize the effective classification of time series data. Experimental results show that the proposed WA-DPSO-RTELM model has better classification performance for microseismic time series data set and UCR time series data set compared with state-of-the-art methods. In the future, we will combine the distributed processing framework to process microseismic data and carry out experimental verification in the distributed environment and continue to explore more types of microseismic events that will become a research trend.

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Funding

This study was funded by the National Natural Science Foundation of China (No. 62072220, 61502215). China Postdoctoral Science Foundation Funded Project (No. 2020M672134). Central Government Guides Local Science and Technology Development Foundation Project of Liaoning Province (No.2022JH6/100100032).

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Authors and Affiliations

  1. School of Information, Liaoning University, Shenyang, 110036, Liaoning, China

    Linlin Ding, Ze Chen & Baoyan Song

  2. School of Environment, Liaoning University, Shenyang, 110036, Liaoning, China

    Yishan Pan

Authors
  1. Linlin Ding
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  2. Ze Chen
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  4. Baoyan Song
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Correspondence to Baoyan Song.

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Ding, L., Chen, Z., Pan, Y. et al. Mine Microseismic Time Series Data Integrated Classification Based on Improved Wavelet Decomposition and ELM. Cogn Comput 14, 1526–1546 (2022). https://doi.org/10.1007/s12559-022-09997-z

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