LGF SeismoLocator: A Deep Learning Model for Precision Microseismic Event Localization in Coal Mines.

In the context of heightened safety concerns and the intricate nature of geological structures in coal mining, accurately localizing microseismic events is a critical challenge. This paper introduces the LGF SeismoLocator, a novel deep learning model tailored to enhance the precision of seismic source detection within coal mines. By innovatively combining long short-term memory networks (LSTM), graph convolutional networks (GCN), and fully convolutional networks (FCN), and utilizing 3D Gaussian distributions as labels, this model demonstrates remarkable capabilities in processing complex seismic data. When tested with microseismic events from the Dongtan Coal Mine, the LGF SeismoLocator exhibited superior accuracy in event localization and computational efficiency. Its effectiveness was further validated through controlled blasting experiments. This study not only highlights the potential of deep learning to improve microseismic monitoring but also provides a practical solution for mitigating risks associated with rockbursts and other mining-related hazards. Highlights: Introduction of the LGF SeismoLocator, a novel deep learning model designed for microseismic event localization in coal mines with enhanced accuracy. Integration of LSTM, GCN, and FCN to enhance the accuracy and efficiency of microseismic localization, demonstrating superior performance in processing complex seismic data. Validation of the LGF SeismoLocator's effectiveness through testing against microseismic events from the Dongtan Coal Mine and controlled blasting experiments. [ABSTRACT FROM AUTHOR]