中國有色金屬學(xué)報(英文版)
Transactions of Nonferrous Metals Society of China
| Vol. 33 No. 9 September 2023 |
（1. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Ministry of Education), School of Geosciences & Info-Physics, Central South University, Changsha 410083, China;
2. PowerChina Zhongnan Engineering Corporation Limited, Changsha 410014, China;
3. Department of Surveying and Mapping Geography, Hunan Vocational College of Engineering, Changsha 410151, China）
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.
Key words: graph convolutional network; deep learning; stream sediment geochemical samplings; bedrock mapping; quaternary coverage
