Distributed acoustic sensing monitoring of overburden fractures in coal mine goaf

As socioeconomic development advances, challenges associated with coal mining beneath structures such as buildings, water bodies, and railways have intensified markedly. It is increasingly imperative to extract underground minerals within acceptable boundaries while diligently monitoring the environmental impacts of such activities. Previous research showed that mining-induced subsidence was a primary contributor to environmental geological disasters in mining areas, particularly when the integrity of the overlying strata is breached. Therefore, it is crucial to develop methodologies for the early detection of surface subsidence, which requires in-depth research into monitoring the fracture signals from overburden rock. Existing methods, including acoustic emission and microseismic monitoring systems, face significant challenges in achieving widespread, comprehensive, and distributed monitoring. In response to these limitations, distributed acoustic sensing (DAS), a state-of-the-art optoelectronic sensing technique, has recently gained prominence and been extensively employed across geophysical exploration fields such as oil and gas exploration and seismic monitoring. We explore applying DAS technology to enhance the monitoring and identification of fracture signals in the overburden of mined-out areas, aiming to improve both safety and sustainability in mining operations.

This research selects a coal mine in Ningdong town, Lingwu city, Ningxia Hui Autonomous Region, China. DAS technology is used to continuously monitor the fracture signals of the overburden in underground voids. A fibre optic cable was installed at the bottom of a trench stretching parallel to the coal mining face with dimensions of approximately 1 km in length, 15 cm in width, and 30 cm in depth. Additionally, several triaxial node seismometers were deployed along the route for comparison validation. Given the low signal-to-noise ratio of DAS data, comparative experiments were conducted using five denoising techniques: high-pass filtering, empirical mode decomposition, Fourier transform, F-X deconvolution, and synchronous compression wavelet transform. The DAS signals were preprocessed through detrending, mean removal, and denoising, followed by time-frequency analysis to extract overburden fracture signals. The event signals collected by the DAS system which formed a dataset were converted into recurrence plots. This dataset was used to train an intelligent recognition model for overburden fracture signals based on the convolutional neural network VGG-16.

The results demonstrate that synchronous compression wavelet transform effectively eliminates noise from DAS data. The overburden fracture signals detected by DAS were consistent with those by seismometers. Recurrence plots of DAS-collected fracture signals differed from nonfracture signals, which can be distinguished by the trained VGG-16 model with an accuracy of 85%.

Monitoring overburden fractures via DAS technology is feasible. The proposed deep learning approach, based on recurrence plots and the VGG-16 convolutional neural network, can effectively recognize fracture signals. This research provides significant technical support for developing an intelligent early warning system for mining subsidence based on DAS.