| 基于卷积自编码网络的夏河—合作地区金矿定量预测 |
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| 关键词:deep learning convolutional auto-encode compositional data analysis quantitative mineral resources prediction |
| 基金项目:国家重点研发计划课题(编号: 2017YFC0601505);国家自然科学基金项目(编号: 42072322);四川省科技厅项目(编号: 2022NSFSC0510) |
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| 摘要: |
| Quantitative Gold Resources Prediction in Xiahe–Hezuo Area Based on Convolutional Auto-Encode Network |
| The Xiahe–Hezuo area of Gansu province has complex geological structures and abundant gold mineral resources, is an important metallogenic belt within the West Qinling Mountains. At present, a certain number of gold polymetallic deposits have been found in this area, and there is still a good prospecting potential of gold mineralization. Therefore, this paper uses the Xiahe–Hezuo area as a research area. Geochemical associations are quantitatively extracted by the method of compositional data analysis. Based on the geological structure and geochemical anomalies, multiple metallogenic information is integrated for building the mineral exploration model. And then, the Convolutional Auto-Encode network (CAE) method is used for regional gold resource prediction. Finally, 7 exploration targets are delineated. The result shows an excellent prediction performance (AUC=0.90), and the targets deserve further research. |
| LIU Bing-li,XIE Miao,KONG Yun-hui,TANG Rui,YU Zheng-bo,LUO De-jiang.2023.Quantitative Gold Resources Prediction in Xiahe–Hezuo Area Based on Convolutional Auto-Encode Network[J].Acta Geoscientica Sinica,44(5):877-886. |
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