Predicting microseismic, acoustic emission and electromagnetic radiation data using neural networks

Microseism, acoustic emission and electromagnetic radiation (M -A -E) data are usually used for predicting rockburst hazards. However, it is a great challenge to realize the prediction of M -A -E data. In this study, with the aid of a deep learning algorithm, a new method for the prediction of M -A -E data is proposed. In this method, an M -A -E data prediction model is built based on a variety of neural networks after analyzing numerous M -A -E data, and then the M -A -E data can be predicted. The predicted results are highly correlated with the real data collected in the field. Through field verification, the deep learning-based prediction method of M -A -E data provides quantitative prediction data for rockburst monitoring. (c) 2024 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).