Time Series Prediction of Microseismic Multi-parameter Related to Rockburst Based on Deep Learning

Zhang, Hang; Zeng, Jun; Ma, Jiaji; Fang, Yong; Ma, Chunchi; Yao, Zhigang; Chen, Ziquan

doi:10.1007/s00603-021-02614-9

Cited by 33 publications

(4 citation statements)

References 38 publications

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“…In it, each node sends its own location packet to neighboring nodes, including node ID, coordinate position, and jump number [22]. The unknown node will record the least number of hops received, while larger packets sent to the same beacon node will be ignored.…”

Section: G Description Of the Improved Location Algorithmmentioning

confidence: 99%

Research on Road Geological Hazard Risk Assessment Model Based on Computational Deep Learning Algorithm Integrated With GIS

Zhichao Xu

2024

jes

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This paper studies the geological disaster emergency command and management system based on geographic information system. This system is for the province, city, county land resources and other aspects of the comprehensive application, the realization of real-time information display, meteorological early warning, group measurement and mass prevention, monitoring and early warning, major disaster management, prevention plan, quick report processing, emergency investigation, emergency disposal, emergency consultation and other functions. Through the GSM network, the monitoring data is sent to the monitoring center through the mobile phone, and then the monitoring center processes, stores, displays and alarms the monitoring data. Finally, B/S structure is used to build a remote Web data service publishing platform, so that it can carry out real-time monitoring at any place, and improve the speed, efficiency and intuition of high-speed slope disaster. It is proved that the method has the advantages of stable operation, accurate data, fast transmission speed and high monitoring accuracy through the practical application of a highway project foundation pit slope monitoring.

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Section: G Description Of the Improved Location Algorithmmentioning

confidence: 99%

Research on Road Geological Hazard Risk Assessment Model Based on Computational Deep Learning Algorithm Integrated With GIS

Zhichao Xu

2024

jes

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“…It is observed that the most extensive research has been conducted in the area "tunnel disease identification and assessment". This primarily includes the identificatio of lining cracks and water leakage areas [17,18], the prediction of rockburst and collap risk [19,20], and the control of surrounding rock deformation [21], among other factor Feng et al [22] combined the mean impact value algorithm (MIV-A) with the improve firefly algorithm to optimize the probabilistic neural network (PNN), using cumulativ microseismic event numbers, microseismic energy, etc., as input parameters and the roc burst intensity level as the output parameter. They proposed a rockburst predictio method based on microseismic monitoring and an optimized probabilistic model, demon strating that the prediction rate of rockburst based on this method can reach 86.75%.…”

Section: Introductionmentioning

confidence: 99%

A Review of Deep Learning Applications in Tunneling and Underground Engineering in China

Su,

Hu,

Yang

et al. 2024

Applied Sciences

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With the advent of the era of big data and information technology, deep learning (DL) has become a hot trend in the research field of artificial intelligence (AI). The use of deep learning methods for parameter inversion, disease identification, detection, surrounding rock classification, disaster prediction, and other tunnel engineering problems has also become a new trend in recent years, both domestically and internationally. This paper briefly introduces the development process of deep learning. By reviewing a number of published papers on the application of deep learning in tunnel engineering over the past 20 years, this paper discusses the intelligent application of deep learning algorithms in tunnel engineering, including collapse risk assessment, water inrush prediction, crack identification, structural stability evaluation, and seepage erosion in mountain tunnels, urban subway tunnels, and subsea tunnels. Finally, it explores the future challenges and development prospects of deep learning in tunnel engineering.

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“…Hang Zhang and Jun Zeng et al used neural networks for multi-microseismic parameter time series prediction starting from the microseismic time series of rock burst. It provides a good idea for microseismic magnitude (energy) time series periodicity prediction 4 .…”

Section: Introductionmentioning

confidence: 99%

Prediction of microseismic events in rock burst mines based on MEA-BP neural network

Lan

Guo

Zhang

et al. 2023

Sci Rep

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Microseismic monitoring is an important tool for predicting and preventing rock burst incidents in mines, as it provides precursor information on rock burst. To improve the prediction accuracy of microseismic events in rock burst mines, the working face of the Hegang Junde coal mine is selected as the research object, and the research data will consist of the microseismic monitoring data from this working face over the past 4 years, adopts expert system and temporal energy data mining method to fuse and analyze the mine pressure manifestation regularity and microseismic data, and the "noise reduction" data model is established. By comparing the MEA-BP and traditional BP neural network models, the results of the study show that the prediction accuracy of the MEA-BP neural network model was higher than that of the BP neural network. The absolute and relative errors of the MEA-BP neural network were reduced by 247.24 J and 46.6%, respectively. Combined with the online monitoring data of the KJ550 rock burst, the MEA-BP neural network proved to be more effective in microseismic energy prediction and improved the accuracy of microseismic event prediction in rock burst mines.

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Time Series Prediction of Microseismic Multi-parameter Related to Rockburst Based on Deep Learning

Cited by 33 publications

References 38 publications

Research on Road Geological Hazard Risk Assessment Model Based on Computational Deep Learning Algorithm Integrated With GIS

Research on Road Geological Hazard Risk Assessment Model Based on Computational Deep Learning Algorithm Integrated With GIS

A Review of Deep Learning Applications in Tunneling and Underground Engineering in China

Prediction of microseismic events in rock burst mines based on MEA-BP neural network

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