Intelligent Lithology Identification Method Based on Geological Priors and Mechanical Mechanisms Within a Hierarchical Framework

Lithology identification in complex heterogeneous formations faces the challenges of overlapping well-log responses and long-tailed data distributions, causing traditional flat models to misclassify fine-grained lithologies. To address this, we propose a hierarchical routing method integrating geological priors and rock mechanics. A joint resampling strategy combining stratified undersampling and SMOTE first rectifies data bias. Next, conventional well logs are inverted into mechanical parameters, such as uniaxial compressive strength and abrasiveness, constructing a high-dimensional physical discriminative space. Finally, a two-stage network is deployed: an XGBoost gating model performs macroscopic rock classification to block cross-facies noise, followed by a dynamic routing mechanism distributing samples to deep neural network (DNN) sub-experts for fine-grained decoupling. Tested on actual multi-block logging data, the XGBoost-DNN architecture achieved a global accuracy of 0.85 and improved the macro F1-score from 0.55 under the baseline model to 0.84. Integrating physical mechanisms with a hierarchical topology effectively overcomes bottlenecks in fine-grained lithology identification, offering robust geological interpretability and engineering value.