Mine-Microseismic-Signal Recognition Based on LMD–PNN Method
Applied Sciences, 2022 The effective recognition of microseismic signal is related to the accuracy of mine-dynamic-disaster precursor-information processing, which is a difficult method of microseismic-data processing.
Qiang Li, Yingchun Li, Qingyuan He
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Automatic Identification System for Rock Microseismic Signals Based on Signal Eigenvalues
Applied Sciences, 2023 The microseismic signals of rock fractures indicate that the rock mass in a particular area is changing slowly, and the microseismic signals of rock blasting indicate that the rock mass in a particular area is changing violently.
Junzhi Chen +3 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.
Ting Hu +5 more
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Investigation of microseismic signal denoising using an improved wavelet adaptive thresholding method. [PDF]
Sci Rep, 2022 There are high- and low-frequency noise signals in a microseismic signal that can lead to the distortion and submersion of an effective waveform. At present, effectively removing high- and low-frequency noise without losing the effective signal of local ...
Zhang Z, Ye Y, Luo B, Chen G, Wu M.
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CNN-Transformer for Microseismic Signal Classification
Electronics, 2023 The microseismic signals of coal and rock fractures collected by underground sensors contain masses of blasting vibration signals generated by coal mine blasting, and the waveforms of the two signals are highly similar. In order to identify the true microseismic signals with a microseismic monitoring system quickly and accurately, this paper proposes a
Xingli Zhang +3 more
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Energies, 2023 Microseismic monitoring has become a well-known technique for predicting the mechanisms of rock failure in deeply buried energy exploration, in which noise has a great influence on microseismic monitoring results.
Shibin Tang +4 more
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Parallel Processing Method for Microseismic Signal Based on Deep Neural Network
Remote Sensing, 2023 The microseismic signals released by rock mass fracture can be captured via microseismic monitoring to evaluate the development of geological disasters.
Chunchi Ma +7 more
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Ensemble Learning Improves the Efficiency of Microseismic Signal Classification in Landslide Seismic Monitoring. [PDF]
Sensors (Basel)A deep-seated landslide could release numerous microseismic signals from creep-slip movement, which includes a rock-soil slip from the slope surface and a rock-soil shear rupture in the subsurface.
Xin B, Huang Z, Huang S, Feng L.
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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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Recognition of Microseismic and Blasting Signals in Mines Based on Convolutional Neural Network and Stockwell Transform [PDF]
IEEE Access, 2020 The microseismic monitoring signals which need to be determined in mines include those caused by both rock bursts and by blasting. The blasting signals must be separated from the microseismic signals in order to extract the information needed for the ...
Guangdong Song +2 more
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