Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 5
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.
https://doi.org/10.5194/gmd-19-2219-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 5
Development and technical paper
 | 
17 Mar 2026
Development and technical paper |  | 17 Mar 2026

Recognizing spatial geochemical anomaly patterns using deformable convolutional networks guided with geological knowledge

Xinyu Zhang, Yihui Xiong, and Zhiyi Chen

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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.
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