Articles | Volume 19, issue 5
https://doi.org/10.5194/gmd-19-2219-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-2219-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
17 Mar 2026
Development and technical paper |
| 17 Mar 2026
| 17 Mar 2026
Recognizing spatial geochemical anomaly patterns using deformable convolutional networks guided with geological knowledge
Xinyu Zhang
State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China
Yihui Xiong
CORRESPONDING AUTHOR
State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China
Zhiyi Chen
State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China
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Short summary
Geochemical anomalies associated with mineralization represent one of the most significant types of geo-anomalies for mineral exploration.This study develops a AI method that combines geological knowledge with a flexible deep learning model. It helps identify geochemical anomaly patterns more accurately and reliably by focusing on key features like ore-controlling faults. The model's decisions are easier to understand through visual explanations, increasing transparency and trust in the results.
Geochemical anomalies associated with mineralization represent one of the most significant types...
