Results 1 to 10 of about 9,177 (222)

A dual branch model for predicting microseismic magnitude time series named DTFNet [PDF]

Scientific Reports
Microseismic monitoring is crucial in realizing intelligent early warning of coal mine rockbursts. Utilizing historical microseismic monitoring data to predict future microseismic events effectively enhances the accuracy of impact disaster prediction and
Hao Luo, Zhongyi Liu, Yishan Pan, Liang Wang, Chao Kong, Huan Zhang   +5 more
doaj   +2 more sources

Genetic Programming-Based Prediction Model for Microseismic Data

Geofluids, 2022
Microseismic monitoring is a rock breakdown monitoring technology, and it has become a major technical tool for underground disaster warning and prevention.
Man Wang, Hongwei Zhou, Dongming Zhang, Yingwei Wang, Weihang Du, Beichen Yu   +5 more
doaj   +2 more sources

Research on computational propagation and identification of mine microseismic signals based on deep learning. [PDF]

PLoS ONE
In the mining field, hydraulic fracturing of coal - seam boreholes generates a large number of weak microseismic signals. The accurate identification of these signals is crucial for subsequent positioning and inversion.
Dongmei Liu, Junsong Zhang, Bingrui Zhao, Linsheng Gao, Hao Zhou, Zhiheng Cheng, Liang Chen, Meichen Li   +7 more
doaj   +2 more sources

Multi-Classification of Complex Microseismic Waveforms Using Convolutional Neural Network: A Case Study in Tunnel Engineering

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, Jun Zeng, Chunchi Ma, Tianbin Li, Yelin Deng, Tao Song   +5 more
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Decision Tree Model for Rockburst Prediction Based on Microseismic Monitoring

Advances in Civil Engineering, 2021
Rockburst is an extremely complex dynamic instability phenomenon for rock underground excavation. It is difficult to predict and evaluate the rank level of rockburst in practice.
Hongbo Zhao, Bingrui Chen, Changxing Zhu
doaj   +1 more source

Time series prediction of microseismic energy level based on feature extraction of one-dimensional convolutional neural network

工程科学学报, 2021
With the gradual transition of coal mining to deep mining, the number and intensity of rock burst events in the deep mining process are gradually increasing.
Yan-yu PEI, Xiao-bin YANG, Jin-ping CHUAN, Xue-song WU, Hong-ming CHENG, Xiang-feng LÜ   +5 more
doaj   +1 more source

An Improved Microseismic Signal Denoising Method of Rock Failure for Deeply Buried Energy Exploration

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, Shun Ding, Jiaming Li, Chun Zhu, Leyu Cao   +4 more
doaj   +1 more source

Wavelet Transform-Based Fuzzy Clustering Microseismic First-Arrival Picking Method

IEEE Access, 2023
Microseismic arrival time picking serves as the foundation for microseismic source localization and holds significant importance in the field of microseismic monitoring.
Tingting Lin, Jingwang Cheng, Qiang Chen, Siyu Cui   +3 more
doaj   +1 more source

Recognition of Rock Micro-Fracture Signal Based on Deep Convolution Neural Network Inception Algorithm

IEEE Access, 2021
Rockburst is a common geological disaster in mines, tunnels, deep underground engineering, and during excavation, mining, and construction. Rockburst frequently occurs as the depth of burial increases, and its early warning technology is in urgent need ...
Guili Peng, Xianguo Tuo, Tong Shen, Jing Lu   +3 more
doaj   +1 more source

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, Wenjin Yan, Weihao Xu, Tianbin Li, Xuefeng Ran, Jiangjun Wan, Ke Tong, Yu Lin   +7 more
doaj   +1 more source