GM-Geo: Graph-Enhanced Selective State Space Learning for Irregular Borehole Sequences

Lithology identification is essential for formation evaluation and reservoir characterization, serving as a crucial basis for assessing the presence of mineral deposits underground. However, due to the lateral spatial correlations and ordered vertical dependencies of subsurface lithology across boreholes, high-precision lithology prediction from irregular borehole observation data is extremely challenging. Existing methods typically focus on sequence modeling or graph-based neighborhood learning, but rarely integrate both into a unified framework. To address this issue, we propose GM-Geo, a graph-enhanced selective state space framework for lithology prediction from irregular borehole sequences. Specifically, the proposed method first transforms heterogeneous interval-based borehole records into standardized depth-wise samples through geology-aware resampling. It then constructs a sample-level spatial neighborhood graph to capture local geological relationships across boreholes. Finally, graph-aggregated spatial features are fused into a selective state space model to jointly encode lateral spatial continuity and vertical lithological evolution. This paper evaluates the framework in 218 boreholes in the Tangwuli fluorite mining area and compares it with baseline methods such as RF, XGBoost, Transformer, GNN, and ET4DD. GM-Geo achieved best precision, recall, macro F1 score, and micro F1 score of 0.845, 0.798, 0.812, and 0.821, respectively. Ablation experiments further demonstrated that both the graph module and the state-space module contributed to the final performance.