Xiaolei Duan

Lithology identification is a critical technology for geological resource exploration and engineering safety assessment. However, traditional methods suffer from insufficient feature representation and low classification accuracy due to challenges such as weathering, vegetation cover, and spectral overlap in complex sedimentary rock regions. This study proposes a hierarchical multi-feature random forest algorithm based on Feature-Preserved Compressive Sampling (FPCS). Using 3D laser point cloud data from the Manas River outcrop in the southern margin of the Junggar Basin as the test area, we integrate graph signal processing and multi-scale feature fusion to construct a high-precision lithology identification model. The FPCS method establishes a geologically adaptive graph model constrained by geodesic distance and gradient-sensitive weighting, employing a three-tier graph filter bank (low-pass, band-pass, and high-pass) to extract macroscopic morphology, interface gradients, and microscopic fracture features of rock layers. A dynamic gated fusion mechanism optimizes multi-level feature weights, significantly improving identification accuracy in lithological transition zones. Experimental results on five million test samples demonstrate an overall accuracy (OA) of 95.6% and a mean accuracy (mAcc) of 94.3%, representing improvements of 36.1% and 20.5%, respectively, over the PointNet model. These findings confirm the robust engineering applicability of the FPCS-based hierarchical multi-feature approach for point cloud lithology identification.

To address the challenges of boundary ambiguity and classification accuracy degradation in lithology identification of outcrop point clouds within complex geological settings, this study proposes a Stratigraphy-Guided Continuous Clustering (SGCC) method. Leveraging sedimentological principles of lateral continuity, a dynamic density-threshold hierarchical clustering algorithm is designed to optimize lithological unit boundaries through adjacency-based cluster merging criteria. A Patch-level Feature Aggregation Operator (PFAO) is introduced to construct a multimodal feature space by integrating geometric covariance matrices and spectral distribution entropy. A random forest classifier is then employed for lithology discrimination. Experimental validation on the Qingshuihe Formation outcrop dataset from the Junggar Basin, Xinjiang, demonstrates that the SGCC method achieves an overall accuracy (OA) of 94.64% and a mean intersection over union (MIOU) of 90.87%, outperforming traditional machine learning (SVM, XGBoost) and deep learning methods (PointNet) by 26.22%–68.36%. Notably, SGCC significantly enhances boundary recognition in sandstone-mudstone thin interbeds and conglomerate-sandstone transitional zones. Ablation experiments confirm the efficacy of stratigraphic constraints in suppressing noise and improving computational efficiency, reducing training memory by 83.3% and processing time by 85.7%. By deeply integrating geological principles with computational models, this method provides a high-precision and interpretable technical pathway for intelligent geological exploration.

Lithological identification of outcrops in complex geological settings plays a crucial role in hydrocarbon exploration and geological modeling. To address the limitations of traditional field surveys, such as low efficiency and high risk, we proposed an intelligent lithology recognition method, SG-RFGeo, for terrestrial laser scanning (TLS) outcrop point clouds, which integrates spectral and geometric features. The workflow involves several key steps. First, lithological recognition units are created through regular grid segmentation. From these units, spectral reflectance statistics (e.g., mean, standard deviation, kurtosis, and other related metrics), and geometric morphological features (e.g., surface variation rate, curvature, planarity, among others) are extracted. Next, a double-layer random forest model is employed for lithology identification. In the shallow layer, the Gini index is used to select relevant features for a coarse classification of vegetation, conglomerate, and mud–sandstone. The deep-layer module applies an optimized feature set to further classify thinly interbedded sandstone and mudstone. Geological prior knowledge, such as stratigraphic attitudes, is incorporated to spatially constrain and post-process the classification results, enhancing their geological plausibility. The method was tested on a TLS dataset from the Yueyawan outcrop of the Qingshuihe Formation, located on the southern margin of the Junggar Basin in China. Results demonstrate that the integration of spectral and geometric features significantly improves classification performance, with the Macro F1-score increasing from 0.65 (with single-feature input) to 0.82. Further, post-processing with stratigraphic constraints boosts the overall classification accuracy to 93%, outperforming SVM (59.2%), XGBoost (67.8%), and PointNet (75.3%). These findings demonstrate that integrating multi-source features and geological prior constraints effectively addresses the challenges of lithological identification in complex outcrops, providing a novel approach for high-precision geological modeling and exploration.