Inversion Analysis of Stress Fields Based on the LSTM–Attention Neural Network

Conventional geostress methods of measurement cannot reveal an accurate geostress field distribution in an engineering area, limited by both cost and prevailing geological conditions. This study introduces an improved LSTM–Attention neural network for in situ stress field inversion. By integrating long short-term memory (LSTM) networks—which capture temporal dependencies in sequential data with attention mechanisms that emphasize critical features, the proposed method addresses inherent non-linearity and discontinuity challenges in deep subsurface stress field inversion. The integrated LSTM and multi-head attention architecture extracts temporal features and weights critical information within ground stress field data. Through iterative refinement via optimizers and loss functions, this framework successfully inverts stress boundary conditions while mitigating overfitting risks. The inversion of the stress field around a hydropower station indicates that the proposed method allows accurate inversion of distribution of the geostress field; the inversion values of the maximum principal stress, intermediate principal stress, and minimum principal stress conform to those measured. This study provides a new method for accurately and reliably inverting the stress field for deep engineering geological surveys and rock mass engineering design, which has significant scientific value and engineering application prospects. The rockburst risk of chambers is evaluated according to the stress field, which shows that locations with a burial depth of 274.3 m are at moderate to weak risk of rockburst.