Signal Super Prediction and Rock Burst Precursor Recognition Framework Based on Guided Diffusion Model with Transformer
Source abstract
Abstract
Implementing precise and advanced early warning systems for rock bursts is a crucial approach to maintaining safety during coal mining operations. At present, FEMR data play a key role in monitoring and providing early warnings for rock bursts. Nevertheless, conventional early warning systems are associated with certain limitations, such as a short early warning time and low accuracy of early warning. To enhance the timeliness of early warnings and bolster the safety of coal mines, a novel early warning model has been developed. In this paper, we present a framework for predicting the FEMR signal in deep future and recognizing the rock burst precursor. The framework involves two models, a guided diffusion model with a transformer for FEMR signal super prediction and an auxiliary model for recognizing the rock burst precursor. The framework was applied to the Buertai database, which was recognized as having a rock burst risk. The results demonstrate that the framework can predict 360 h (15 days) of FEMR signal using only 12 h of known signal. If the duration of known data is compressed by adjusting the CWT window length, it becomes possible to predict data over longer future time spans. Additionally, it achieved a maximum recognition accuracy of 98.07%, which realizes the super prediction of rock burst disaster. These characteristics make our framework an attractive approach for rock burst predicting and early warning.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Seismic Imaging and Inversion Techniques · Hydrocarbon exploration and reservoir analysis · Hydraulic Fracturing and Reservoir Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Shanghai Datun Energy Resources Co., Ltd., Shanghai 200120, China
School of Safety Engineering, China University of Mining and Technology, Xuzhou 221116, China
Kongzhuang Coal Mine, Shanghai Datun Energy Resources Co., Ltd., Xuzhou 221600, China
Key Laboratory of Gas and Fire Control for Coal Mines, China University of Mining and Technology, Xuzhou 221116, China
Information Institution of Ministry of Emergency Management, Beijing 100029, China
Ministry of Science, Technology and Environmental Protection, Shanghai Datun Energy Resources Co., Ltd., Shanghai 200120, China
Funding
National Natural Science Foundation of China · National Key Research and Development Program of China
References (71)
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