Bedrock mapping based on terrain weighted directed graph convolutional network using stream sediment geochemical samplings

Abstract

To explore an efficient strategy for intelligent bedrock mapping that can be applied in the areas with coexisting Quaternary coverages and bedrock outcrops, a graph convolutional network (GCN) was implemented for bedrock classification using stream sediment geochemical samplings in the Chahanwusu River area, Qinghai Province, China. The sampling points were organized into a terrain weighted directed graph (TWDG) using Delaunay triangulation to capture the upstream-downstream relationships among the geochemical sampling points. The experimental results indicate that the semi-supervised GCN models, only using 20% of the labeled sampling points, achieved accuracies of 68.20% and 78.31% in ten-type and five-type bedrock discrimination, respectively. In conclusion, it is feasible to map the bedrock type through the concentrations of elements on the stream sediment geochemical sampling points. The proposed data-driven GCN bedrock classification method not only improves the efficiency of bedrock mapping but also may be applied in a large area.