Abstract:

The development of karst fissures is characterized by high dimensionality, nonlinearity, and spatial heterogeneity. Accurately characterizing the spatial distribution of fissures remains a challenging issue in the study of karst development patterns. Data-driven machine learning modeling methods can effectively capture the implicit nonlinear and discontinuous characteristics within fissure systems, thereby significantly improving the efficiency and accuracy of fissure identification and characterization. This study takes the Zhangfang area of Fangshan, Beijing, as its research subject. Based on detailed field-measured fissure data, it systematically integrates multi-source datasets including surface topographic information, regional tectonic background, stratigraphic lithology distribution, and groundwater levels. Using a machine learning framework, a comprehensive quantitative feature system was constructed, covering multiple dimensions such as spatial fault influence, stratigraphic lithological characteristics, variations in groundwater depth, and high-precision topographic derivatives (e.g., slope, curvature). The study focused on comparing three machine learning methods—Support Vector Regression, Extreme Gradient Boosting, and Random Forest-to predict the development and spatial distribution of karst fissures in the study area. The results indicate that the prediction model based on Random Forest performed the best. The simulation results for fissure density, joint orientation, and dip angle were most consistent with measured statistical data, demonstrating the most robust model performance, strong generalization capability, and high method applicability. It exhibited unique advantages in representing complex geological processes such as multi-phase fissure development. The findings of this study reveal that integrating data-driven models with in-depth geological mechanism analysis is an effective approach to overcoming the challenges in the quantitative characterization and prediction of complex karst systems.

Key words: karst fractures, machine learning, support vector regression, gradient boosting trees, random forest, Fangshan area in Beijing