ERT and Interpretable Machine Learning Integrated Prediction of Karst Soil and Epikarst Thickness in a Peak Cluster-Valley Catchment, Southwest China

The soil weathering rate of karst carbonate rocks is very low, resulting in scarce and thin soil layers. Under the subtropical monsoon climate, a well-developed epikarst zone is formed in the upper part of the vadose zone, with abundant fractures and pores filled with soil. The karst hillslopes surface bedrock is exposed, forming a mosaic soil landscape with soil.However, traditional profile surveys make it difficult to quantitatively determine the spatial distribution of soil and the epikarst zone with high precision; additionally, complex lithological conditions and strong spatial heterogeneity of carbonate rocks further limit the accurate quantification of soil thickness (ST) and epikarst thickness (EkT).

Therefore, this study investigated the soil-epikarst structures and their spatial distribution at different topographic locations (including different hillslope positions, ridges, saddles, and valleys) using Electrical Resistivity Tomography (ERT, 5268 sampling points) in a peak cluster-valley catchment in Southwest China. Furthermore, an interpretation method was established, where the application of revised inflexion points in 1D resistivity vertical profiles for improving ST and EkT characterization accuracy was assessed, with interpretations validated against borehole data.

Results showed that between hillslopes, the average ST ranges from 0.42 to 0.52 m, and the average EkT ranges from 3.38 to 4.58 m. The average ST in the valley (3.23 m) is significantly greater than that on hillslopes (0.49 m). Although there are some scattered, fragmented areas with EkT exceeding 20 m in the valley, both the average and median EkT in the valley (3.77 m and 2.98 m) are slightly smaller than those on hillslopes (3.93 m and 3.63 m).

This study integrated high-density ERT observations with a 1-m UAV LiDAR DEM and interpretable machine learning to predict karst soil and epikarst thickness. Important topographic controlling factors were screened out by machine learning, including those affecting ST (i.e., slope position (SP), relative elevation (RElev), slope gradient (S), slope roughness (SR), hillslope shape (HS), slope aspect (SOS), profile curvature (PrC)) and those affecting EkT (i.e., plan curvature (PLC), profile curvature (PrC), flow length (FLU), flow direction (FLD), aspect (A), relative elevation (RELE)).

Moreover, machine learning has made it possible to predict the spatial distribution of soil and the epikarst zone in the catchment with high precision, thereby providing structural information for studies such as soil erosion investigation, hydrological models, and material transport in porous media.

Keywords

Karst; Epikarst zone; Soil thickness (ST); Epikarst thickness (EkT); Electrical Resistivity Tomography (ERT); Machine learning; Southwest China