Geological fault identification via multi-knowledge causally embedding graph representation applied in underground gas storage

Geological fault identification is critical for gas storage safety, yet remains reliant on manual interpretation with inherent inefficiencies. While Artificial intelligent (AI) methods offer alternatives, their neglect of causal structures particularly physical mechanisms and geological confounders and severely limits sensitivity to subtle seismic signals. To overcome this, a multi-knowledge causally embedded graph representation approach for fault identification (FI) in seismic images is proposed. Utilizing geological prior knowledge, the seismic volume is first discretized into node sets through pixel-to-node transformation. To explicitly capture causal structures, a multi-relational feature computation method integrates: (a) spatial continuity constraints of faults as physical causality priors, and (b) seismic signal characteristics. Guided by these causal priors, Causally chain-graphs (CCGs) are constructed through knowledge-driven edge pruning and reinforcement, with connections constrained by pixel spatial receptive fields to reflect geological causality. These causally structured graphs subsequently undergo feature aggregation via graph convolutions, modeling non-Euclidean relationships between nodes. The pipeline evolves from low-level graphs to high-order fault representations, enabling end-to-end pixel-level identification. A gas storage plot in central China was used to verify the effectiveness of the proposed method, and the graph representation learning framework demonstrates excellent performance in pixel-level fault identification within complex geological structures. It efficiently detects the NE-SW trending fault system, including regional, block-bounding, and minor intra-block faults, achieving a mean accuracy of 93.11% and an average F1-score of 0.758. The framework significantly enhances the detection capability and noise immunity for subtle deep-seated faults (with amplitude variations <5%). It provides intelligent early-warning analysis for critical issues in gas storage operations, such as late-stage tectonic reactivation, compromised caprock integrity due to gently dipping boundary faults, and reservoir heterogeneity exacerbated by fault interactions.