基于空谱-空间关联双分支的地球化学找矿异常检测模型

向中林; 王路阔; 郑贺; 张博; 刘海睿

doi:10.3799/dqkx.2026.061

Volume 51 Issue 3

Mar. 2026

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Article Navigation > Earth Science > 2026 > 51(3): 1078-1092

Xiang Zhonglin, Wang Lukuo, Zheng He, Zhang Bo, Liu Hairui, 2026. A Dual-Branch Geochemical Prospecting Anomaly Detection Model with Spectral-Spatial and Spatial Correlation Fusion. Earth Science, 51(3): 1078-1092. doi: 10.3799/dqkx.2026.061

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Xiang Zhonglin, Wang Lukuo, Zheng He, Zhang Bo, Liu Hairui, 2026. A Dual-Branch Geochemical Prospecting Anomaly Detection Model with Spectral-Spatial and Spatial Correlation Fusion. Earth Science, 51(3): 1078-1092. doi: 10.3799/dqkx.2026.061

Citation:

Xiang Zhonglin, Wang Lukuo, Zheng He, Zhang Bo, Liu Hairui, 2026. A Dual-Branch Geochemical Prospecting Anomaly Detection Model with Spectral-Spatial and Spatial Correlation Fusion. Earth Science, 51(3): 1078-1092. doi: 10.3799/dqkx.2026.061

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A Dual-Branch Geochemical Prospecting Anomaly Detection Model with Spectral-Spatial and Spatial Correlation Fusion

doi: 10.3799/dqkx.2026.061

1.
School of Resources and Environment, Henan Polytechnic University, Jiaozuo 454000, China
2.
Altay Geological Team, Geological Bureau of Xinjiang Uygur Autonomous Region, Altay 836500, China

Received Date: 2025-12-25
Publish Date: 2026-03-25

Abstract

Abstract

Establishing a detection model that can take into account the multi-element geochemical spatial-spectral characteristics and effectively fit the complex distribution of data is the key to identification of abnormal areas. In response to the challenge of extracting geochemical prospecting anomalies in the high-altitude, deep-cutting, and shallow-coverage areas of the Eastern Kunlun Mountains in Xinjiang, this study proposes a Spatial-Spectral Feature and Global Spatial Correlation Network (SSGSNet). Based on ResNet residual blocks, the spatial-spectral feature branch is integrates a dual-attention module to extract local spatial-spectral features, with the spatial correlation branch using patch embedding and self-attention mechanisms to mine global spatial correlation features. Incorporating tectonic data improves the accuracy of geochemical prospecting, and SHAP values explain the critical role of faults within the model. Experimental results show that the AUC value of the SSGSNet model reaches 0.945 3, significantly outperforming the ResNet and ViT single models as well as the conventional spatial-spectral dual-branch model. Field verification shows that gold mineralization phenomena of varying degrees were found in four high-anomaly areas, including Yaoxi and Bashiganike, which confirms that the model can effectively solve the problem of extracting complex background geochemical anomaly information, providing reliable technical support and target area guidance for mineral exploration in covered areas.
- geochemical anomaly,
- dual-branch network model,
- deep learning,
- spatial-spectral features and spatial correlation,
- eastern Kunlun mountains,
- big data

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References(43)

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