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.
Longjun Dong +3 more
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.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.
MIAO Jie +4 more
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Reliable Denoising Strategy to Enhance the Accuracy of Arrival Time Picking of Noisy Microseismic Recordings. [PDF]
Sensors (Basel), 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 ...
Zhang X, Li H, Rong W.
europepmc +3 more sources
IEEE AccessDuring the downhole microseismic monitoring for hydraulic fracturing, microseismic signals are constantly vulnerable to interference from different kinds of noise.
Wenxuan Ge, Qinghui Mao, Zhixian Gui
exaly +4 more sources
IEEE Access, 2018 Microseismic monitoring is widely applied in dams, mines, and various fields of underground engineering. The number of sensors in microseismic monitoring systems is usually very large, which will result in a huge amount of data being produced if the Nyquist sampling theorem is used to acquire microseismic signals.
Ran Zhang, Qingsong Hu, Bin Ye
exaly +4 more sources
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.
europepmc +2 more sources
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.
europepmc +2 more sources
Enhancing Microseismic Signal Classification in Metal Mines Using Transformer-Based Deep Learning
Sustainability, 2023 As microseismic monitoring technology gains widespread application in mine risk pre-warning, the demand for automatic data processing has become increasingly evident. One crucial requirement that has emerged is the automatic classification of signals. To
Jinmiao Wang, Pingan Peng, Ru Lei
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Identification of Microseismic Signals Based on Multiscale Singular Spectrum Entropy
Shock and Vibration, 2020 The accurate identification of effective microseismic events has great significance in the monitoring, early warning, and forecasting of rockburst hazards.
Xingli Zhang +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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