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Research on acoustic emission precursor characteristics of coal sample unloading failure based on discrete wavelet analysis. [PDF]

Sci Rep
Ma S, Zhou Y, Ma D.
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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., Ma C., Pazzi V., Li T., Casagli N.   +4 more
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A first arrival detection method for low SNR microseismic signal

Acta Geophysica, 2018
Most of the microseismic signals have low signal-to-noise ratio (SNR) due to the strong background noise, which makes it difficult to locate the first arrival time. Both accuracy and stability of conventional methods are poor in this situation. To overcome this problem, here we proposed a new method based on the adaptive Morlet wavelet and principal ...
Ruiqing Hu, Yanchun Wang
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Digitalization of Rock Fracture Signal Identification From Tunnel Microseismic Data

IEEE Geoscience and Remote Sensing Letters
Microseismic monitoring technology serves as an effective means of providing early warning signs of rockburst, a type of disaster that poses a serious threat to life safety in tunnels.
Yaxun Xiao, Jianing Guo, Shujie Chen, Liu Liu, Bingrui Chen   +4 more
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Research on microseismic signal identification through data fusion

Computers & Geosciences
Xingli Zhang, Zihan Zhang, Ruisheng Jia, Xinming Lu   +3 more
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Microseismic Signal Reconstruction From Strong Complex Noise Using Low-Rank Structure Extraction and Dual Convolutional Neural Networks

IEEE Transactions on Neural Networks and Learning Systems, 2023
Microseismic signal reconstruction from complex nonrandom noise is challenging, especially when the signal is disrupted or completely covered by strong field noise.
Chao Zhang, M. Baan
semanticscholar   +1 more source

Automatic microseismic signal classification for mining safety monitoring using the WaveNet classifier

Geophysical Prospecting, 2023
Microseismic monitoring is a promising method for the safety monitoring of underground mines. However, it is crucial to isolate microseismic signals related to the collapse of a mine from others for successful monitoring because the monitoring system ...
Woo-Young Choi, S. Pyun, D. Cheon
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A Survey of Machine Learning Applications in Microseismic Signal Recognition and Classification

International Conference on Software, Knowledge, Information Management and Applications, 2023
Effective microseismic event identification and classification form the bedrock of data analysis in microseismic monitoring systems, facilitating real-time source location, rockburst prediction, and mine safety.
Hongmei Shu, Ahmad Yahya Dawod, Lei Mu, Worawit Tepsan   +3 more
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Spectral Evaluation of Microseismic Signals

68th EAGE Conference and Exhibition incorporating SPE EUROPEC 2006, 2006
Spectral evaluation is a very important operation in the processing of microseismic signals. In this poster we consider the microseismic signals that can be recorded at a point from the Earth’s surface as a random process (signal) with many realizations and we present a methodology for the evaluation and statistical stabilization of its frequency ...
V. Bardan, D. Zugravescu, L. Asimopolos
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Microseismic signal of a spring gravimeter

Measurement Techniques, 2007
It is shown that a spring gravimeter responds to microseisms with a pseudo-gravitational signal. Mathematical description of this signal is provided.
V. P. Dedov, V. M. Dorokhin, A. I. Kalenitskii, B. P. Filimonov   +3 more
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