Research on Predicting Mine Earthquakes Based on Deep Learning Time-Series Methods - Xiufeng Zhang; Wei Li; Yang Chen; Junpeng Zou; Hangrui Zhang; Hao Wang; Chaohong Shi; Shaopeng Yan; Quan Zhang (Shock and Vibration)
As the depth and intensity of coal mining in China continue to increase, the frequency and intensity of coal mine earthquakes are also rising exponentially. The occurrence of strong mine earthquakes may result in dynamic disasters, such as impact ground pressure, which pose a significant threat to the lives and properties of individuals residing in mining regions. To more accurately monitor and predict mine earthquakes and thereby reduce the potential risk they pose, this paper presents a study on the inversion and localization of seismic sources of mine earthquakes and a study on the prediction of mine earthquakes based on the deep learning method. The latter is set in the context of the Dongtan coal mine. The principal findings are as follows: The Informer time-series prediction model employs historical data on daily maximum energy mine earthquakes to predict the source location coordinates of possible future daily maximum energy mine earthquakes. The Informer time-series prediction model demonstrates superior performance in the task of mine earthquake prediction, outperforming the prediction of the location of the mine earthquakes’ source coordinates than the prediction of the energy magnitude of the mine earthquakes.