Abstract

Pixel-level identification of whole-rock mineral components in shale scanning electron microscopy (SEM) images is essential for characterizing shale-reservoir microstructures and quantifying mineral contents. Existing mineral identification algorithms generally cannot identify all whole-rock mineral components within a unified framework. Their overall accuracy is also limited by class imbalance, and fine-grained minerals and mineral boundaries remain difficult to segment in complex lithological backgrounds. To address these limitations, shale samples from the Lianggaoshan Formation in the Sichuan Basin were investigated, and a dynamic attention Transformer (DAM-Transformer) was developed for whole-rock mineral component identification in shale SEM images. The proposed method (1) integrates the matrix and associated minerals into a unified segmentation framework; (2) employs a hybrid loss function tailored to the feature distribution of shale SEM images to mitigate class imbalance and improve training stability and model generalizability; and (3) introduces a dynamic attention mechanism that adaptively optimizes window attention weights, focuses on mineral target regions, enhances boundary detail features, and suppresses background noise. The DAM-Transformer achieved a pixel-level mean accuracy (mAcc) of 78.12% across ten mineral classes, outperforming Mask2Former, FCN, UPerNet, DeepLabV3+, and other benchmark methods by 1.51–8.92%. Visual comparisons further demonstrated that the proposed method preserves the continuity of major mineral regions and substantially improves the identification of fine-grained minerals and complex mineral boundaries. In addition, application analysis of shale plug samples showed that the mineral contents identified by the DAM-Transformer exhibited clear response relationships with saturation–centrifugation NMR parameters, providing quantitative support for interpreting shale pore structure, fluid occurrence, and reservoir properties.

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