ArticleScientific reports2023
Prediction of microseismic events in rock burst mines based on MEA-BP neural network.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Optimization Research on Sensor Network Layout for Microseismic Monitoring Based on Location Error Analysis.Sensors (Basel, Switzerland) · 2026Article
- Quadratic decomposition based TCN-Transformer for time series prediction of micro-seismic signals in coal mines.Scientific reports · 2025Article
- Research on blasting technology for thick and hard roof fracturing in isolated island coal mine working face.Scientific reports · 2025Article
- A dual branch model for predicting microseismic magnitude time series named DTFNet.Scientific reports · 2025Article
- Research on prediction of high energy microseismic events in rock burst mines based on BP neural network.Scientific reports · 2024Article
- Article
- Financial big data management and intelligence based on computer intelligent algorithm.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.