A Deep Learning-Based Data-Driven Approach for Predicting Mining Water Inrush From Coal Seam Floor Using Microseismic Monitoring Data

Micro-seismic monitoring during mining operations generates spatiotemporal data that could indicate strata fractures and deformations leading to water inrush anomalies. However, current water inrush prediction methods face challenges from the data non-stationarity and multi-dimensionality, resulting in low prediction precision and effectiveness. This study proposes an innovative data-driven approach for predicting mining water inrush using field 3D micro-seismic monitoring data. The approach couples machine learning and deep learning models to analyze micro-seismic events, pre-processed using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and the Random Sample Consensus (RANSAC) algorithms for both data denoising and water inrush risk locating. Weighting periods are analyzed in periodic variations of event attributes using the fast Fourier transform (FFT), continuous wavelet transform (CWT), empirical mode decomposition (EMD), and seasonal and trend decomposition using Loess (STL) methods. Anomalies are detected using the long short-time memory (LSTM)+absolute error (AE), isolation forest (iForest) and LSTM+iForest models. The study is conducted using a micro-seismic dataset acquired during intermittent water inflow anomalies in the Xingdong coal mine in China. The approach accurately predicts a major water inrush incident hours prior to its occurrence merging detected anomalies with the obtained weighting periods, which are also used for model calibration. Future studies could focus on performance evaluation and calibration of the deep learning models using micro-seismic datasets from different mining operations, and expanding the approach's scope by incorporating other geophysical exploration technologies like the electrical methods to further study the presence and movement of water in mines for improving mining safety.