An Automatic Recognition Method of Microseismic Signals Based on S Transformation and Improved Gaussian Mixture Model

Abstract

The microseismic signals in the coal minefield are very complex because of its special environment with a large number of blast vibration signals, and how to effectively identify the microseismic signals is still a big problem. S transform (ST) and Manifold Learning (ML) methods are introduced to extract the characteristics of the microseismic signals, and Gaussian Mixture Model based on the improved Bee Colony optimization algorithm (IBC-GMM) is established to identify the microseismic signals accurately. Firstly, the time-frequency characteristics of microseismic signals in coal mine are extracted by ST analysis. It is found that there are obvious time-frequency differences between rock-fracturing signals and blast vibration signals. Blast vibration signals have short duration, high frequency, and complex frequency spectrum, and their dominant frequencies are mainly over 100 Hz. However, rock-fracturing signals are relatively slow, with low frequency and stable spectrum change, and their dominant frequencies are generally below 100 Hz. Then, combining with the microseismic data of Xiashinjie coal mine in Tongchuan, China, the feature dimension reduction is carried out by Manifold Learning (ML) method, and the processed feature vectors are automatically recognized by IBC-GMM. Field test results show that the method summarizes the characteristics of the microseismic wave which are difficult to emerge as the learning vector, and the features reflect the key features of microseismic signals well. The identification accuracy is as high as 94%, and its recognition effect is superior to other recognition models (such as traditional Gaussian Mixture Model based on Expectation-Maximum (EM-GMM), Backpropagation (BP) neural network, Random Forests (RF), Bayes (Bayes) methods, and Logistic Regression (LR) method). Therefore, IBC-GMM could be used to mine engineering microseismic monitoring waveform recognition to provide the reference.

Applications of Microseismic Monitoring Technique in Coal Mines: A State-of-the-Art Review

“…The sensor layout is a key contributing factor influencing microseismic location accuracy. Longwall extraction is the most popular mining method in China, and microseis-mic sensors in coal mines are usually distributed in a plane around the longwall panels (Figure 3) [55]. The planar layout of the microseismic network may make it difficult for high-precision identification of the microseismic location.…”

Strong noise-tolerant deep learning network for automatic microseismic events classification

“…In recent years, researchers have gradually paid more attention to the use of machine learning to analyze massive amounts of seismic data Rouet-Leduc, Hulbert, Lubbers, Barros, Humphreys and Johnson (2017); Corbi, Sandri, Bedford, Funiciello, Brizzi, Rosenau and Lallemand (2019); Bergen, Johnson, Maarten and Beroza (2019); Mousavi and Beroza (2020). In addition, serval algorithms have been increasingly used to identify the microseismic events, such as extreme learning machines Zhang, Jiang, Li and Xu (2019), the Gaussian mixture model Wang, Tang, Ma, Wang and Li (2020), logistic regression Pu, Apel and Hall (2020), the random forest algorithm Provost, Hibert and Malet (2017), and neural network algorithms Xu, Zhang, Chen, Li and Liu (2021). Although these studies have laid the foundation for the development of microseismic data processing, there is still a large gap regarding the demand for efficient, accurate, and real-time identification of useful microseismic events for engineering applications.…”