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

Microseismic 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 nonstationarity and multidimensionality, resulting in low prediction precision and effectiveness. This study proposes an innovative data-driven approach for predicting mining water inrush using field 3-D microseismic monitoring data. The approach couples machine learning and deep learning models to analyze microseismic events, preprocessed 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 microseismic 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 the performance evaluation and calibration of the deep learning models using microseismic 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.