A Microseismic Energy Field Distribution Prediction Method Guided by Geological Structure Priors

Existing microseismic prediction methods commonly incorporate domain prior knowledge as ordinary input features, which makes it difficult to fully exploit its structural constraints and physical implications. To address this limitation, this study proposes a geological-structure-prior-guided method (SPG) for predicting the distribution of microseismic energy fields. In the study, a dataset was constructed from 11,081 original microseismic events, yielding 476 day-indexed microseismic energy maps. In SPG, historical microseismic energy maps are used to characterize the recent dynamic evolution of the energy field, whereas geological spatial fields, including coal-seam depth, coal-seam thickness, fault distance, fold distance, and goaf distance, are used to represent static structural priors. A decoder-side prior modulation mechanism is designed to introduce structural constraints into the reconstruction of the target-day energy field. The experimental results show that SPG achieves the best overall continuous-field prediction performance compared with a baseline U-Net and representative spatiotemporal prediction models, including PredRNN-V2, SwinLSTM, and Earthformer. In particular, SPG reduces the mean squared error (MSE) by 11.77% and improves the structural similarity index measure (SSIM) by 2.20% relative to the best-performing comparison model, while also decreasing the average structural gap by 21.29% compared with the baseline U-Net. Furthermore, supplementary high-energy-mask evaluation indicates that SPG maintains competitive capability in identifying high-energy regions. These results indicate that SPG can more effectively reconstruct continuous energy distributions, thereby providing a feasible methodological reference for spatially resolved rockburst early warning and domain-knowledge-informed predictive modeling.