Results 11 to 20 of about 1,776,592 (192)
.Design of acquisition system of multi-channel microseismic signal
Gong-kuang zidonghua, 2018 In view of problems of high cost and low universality existed in current acquisition systems of mine microseismic signal, an acquisition system of multi-channel microseismic signal was designed.
CAI Jianxian +4 more
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Machine Learning Based Identification of Microseismic Signals Using Characteristic Parameters [PDF]
Sensors, 2021 Microseismic monitoring system is one of the effective means to monitor ground stress in deep mines. The accuracy and speed of microseismic signal identification directly affect the stability analysis in rock engineering.
Kang Peng +3 more
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Mine microseismic signal denoising method and application based on Adaboost_LSTM prediction [PDF]
Meitan xuebaoMicroseismic early warning is of great significance for ensuring mine safety, where a good denoising and accurate P-wave arrival picking of a microseismic signal is fundamental to the reliability of microseismic monitoring. By observing a large amount of
Xueyi SHANG +4 more
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Sensors, 2023 We propose a method to enhance the accuracy of arrival time picking of noisy microseismic recordings. A series of intrinsic mode functions (IMFs) of the microseismic signal are initially decomposed by employing the ensemble empirical mode decomposition ...
Xiaohui Zhang +2 more
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Mine Microseismic Signal Denoising Based on a Deep Convolutional Autoencoder
Shock and Vibration, 2023 Mine microseismic signal denoising is a basic and crucial link in microseismic data processing, which influences the accuracy and reliability of the monitoring system, and is of great significance with regard to safety during mining. Therefore, this study introduces a deep learning method to improve the mapping function and sparsity of signals in the ...
Ting Hu +5 more
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Sensors, 2021 Due to the complexity of the various waveforms of microseismic data, there are high requirements on the automatic multi-classification of such data; an accurate classification is conducive for further signal processing and stability analysis of ...
Hang Zhang +5 more
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Deep Convolutional Neural Network for Microseismic Signal Detection and Classification
Pure and Applied Geophysics, 2020 Reliable automatic microseismic waveform detection with high efficiency, precision, and adaptability is the basis of stability analysis of the surrounding rock mass. In this paper, a convolutional neural network (CNN)-based microseismic detection network (CNN-MDN) model was established and well trained to a high degree of accuracy using a dataset with ...
Zhang H. +4 more
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A Nonparametric Method for Automatic Denoising of Microseismic Data
Shock and Vibration, 2018 Noise suppression or signal-to-noise ratio (SNR) enhancement is often desired for better processing results from a microseismic dataset. In this paper, we proposed a nonparametric automatic denoising algorithm for microseismic data.
Pingan Peng, Liguan Wang
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Applied SciencesMicroseismic monitoring is essential for the early warning of mine dynamic disasters; however, weak signal characteristics and strong environmental noise often lead to missed detections and false alarms.
Guangdong Song +5 more
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The Optimum Wavelet Base of Wavelet Analysis in Coal Rock Microseismic Signals
Advances in Mechanical Engineering, 2014 Coal rock rupture microseismic signal is characterized by time-varying, nonstationary, unpredictability, and transient property. Wavelet transform is an important method in microseismic signals processing. However, different wavelet bases yield different
Shoufeng Tang, Minming Tong, Xinmin He
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