SARC-GNN
Published: 8 July 2026| Version 1 | DOI: 10.17632/tb2ttvdk4g.1
Contributor:
husheng zhangDescription
This code package contains the core Python workflow for the Cu geochemical interpolation method described in the associated manuscript. The workflow combines a graph neural network trend model with source-attention residual interpolation to produce an anomaly-preserving Cu prediction surface. The package includes the main modeling script and environment configuration files.
Files
Institutions
- Zhejiang UniversityZhejiang, Hangzhou
Categories
Machine Learning, Mineral Exploration