3D Primary Geochemical Halo Modeling and Its Application to the Ore Prediction of the Jiama Polymetallic Deposit, Tibet, China
The identification of primary geochemical haloes can be used to predict mineral resources in deep-seated orebodies through the delineation of element distributions. The Jiama deposits a typical skarn–porphyry Cu–polymetallic deposit in the Gangdese metallogenic belt of Tibet. The Cu–polymetallic ska...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2021/6629187 |
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| author | Zhongping Tao Bingli Liu Ke Guo Na Guo Cheng Li Yao Xia Yaohua Luo |
| author_facet | Zhongping Tao Bingli Liu Ke Guo Na Guo Cheng Li Yao Xia Yaohua Luo |
| author_sort | Zhongping Tao |
| collection | DOAJ |
| description | The identification of primary geochemical haloes can be used to predict mineral resources in deep-seated orebodies through the delineation of element distributions. The Jiama deposits a typical skarn–porphyry Cu–polymetallic deposit in the Gangdese metallogenic belt of Tibet. The Cu–polymetallic skarn, Cu–Mo hornfels, and Mo ± Cu porphyry mineralization there exhibit superimposed geochemical haloes at depth. Three-dimensional (3D) primary geochemical halo modeling was undertaken for the deposit with the aim of providing geochemical data to describe element distributions in 3D space. An overall geochemical zonation of Zn(Pb) → Au → Cu(Ag) → Mo gained from geochemical cross-sections, together with dip-direction skarn zonation Pb–Zn(Cu) → Cu(Au–Ag–Mo) → Mo(Cu) → Cu–Mo(Au–Ag) and vertical zonation Cu–(Pb–Zn) → Mo–(Cu) → Mo–Cu–(Ag–Au–Pb–Zn) → Mo in the #24 exploration profile, indicates potential mineralization at depth. Integrated geochemical anomalies were extracted by kernel principal component analysis, which has the advantage of accommodating nonlinear data. A maximum-entropy model was constructed for deep mineral resources of uncertainty prediction. Three potential deep mineral targets are proposed on the basis of the obtained geochemical information and background. |
| format | Article |
| id | doaj-art-0233038441c04fb4a0f2363b60bb061a |
| institution | Kabale University |
| issn | 1468-8115 1468-8123 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
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| series | Geofluids |
| spelling | doaj-art-0233038441c04fb4a0f2363b60bb061a2025-02-03T01:27:07ZengWileyGeofluids1468-81151468-81232021-01-01202110.1155/2021/662918766291873D Primary Geochemical Halo Modeling and Its Application to the Ore Prediction of the Jiama Polymetallic Deposit, Tibet, ChinaZhongping Tao0Bingli Liu1Ke Guo2Na Guo3Cheng Li4Yao Xia5Yaohua Luo6Geomathematics Key Laboratory of Sichuan Province (Chengdu University of Technology), Chengdu 610059, ChinaGeomathematics Key Laboratory of Sichuan Province (Chengdu University of Technology), Chengdu 610059, ChinaGeomathematics Key Laboratory of Sichuan Province (Chengdu University of Technology), Chengdu 610059, ChinaGeomathematics Key Laboratory of Sichuan Province (Chengdu University of Technology), Chengdu 610059, ChinaGeomathematics Key Laboratory of Sichuan Province (Chengdu University of Technology), Chengdu 610059, ChinaChina Institute of Geo-Environment Monitoring, Beijing 100081, ChinaGeomathematics Key Laboratory of Sichuan Province (Chengdu University of Technology), Chengdu 610059, ChinaThe identification of primary geochemical haloes can be used to predict mineral resources in deep-seated orebodies through the delineation of element distributions. The Jiama deposits a typical skarn–porphyry Cu–polymetallic deposit in the Gangdese metallogenic belt of Tibet. The Cu–polymetallic skarn, Cu–Mo hornfels, and Mo ± Cu porphyry mineralization there exhibit superimposed geochemical haloes at depth. Three-dimensional (3D) primary geochemical halo modeling was undertaken for the deposit with the aim of providing geochemical data to describe element distributions in 3D space. An overall geochemical zonation of Zn(Pb) → Au → Cu(Ag) → Mo gained from geochemical cross-sections, together with dip-direction skarn zonation Pb–Zn(Cu) → Cu(Au–Ag–Mo) → Mo(Cu) → Cu–Mo(Au–Ag) and vertical zonation Cu–(Pb–Zn) → Mo–(Cu) → Mo–Cu–(Ag–Au–Pb–Zn) → Mo in the #24 exploration profile, indicates potential mineralization at depth. Integrated geochemical anomalies were extracted by kernel principal component analysis, which has the advantage of accommodating nonlinear data. A maximum-entropy model was constructed for deep mineral resources of uncertainty prediction. Three potential deep mineral targets are proposed on the basis of the obtained geochemical information and background.http://dx.doi.org/10.1155/2021/6629187 |
| spellingShingle | Zhongping Tao Bingli Liu Ke Guo Na Guo Cheng Li Yao Xia Yaohua Luo 3D Primary Geochemical Halo Modeling and Its Application to the Ore Prediction of the Jiama Polymetallic Deposit, Tibet, China Geofluids |
| title | 3D Primary Geochemical Halo Modeling and Its Application to the Ore Prediction of the Jiama Polymetallic Deposit, Tibet, China |
| title_full | 3D Primary Geochemical Halo Modeling and Its Application to the Ore Prediction of the Jiama Polymetallic Deposit, Tibet, China |
| title_fullStr | 3D Primary Geochemical Halo Modeling and Its Application to the Ore Prediction of the Jiama Polymetallic Deposit, Tibet, China |
| title_full_unstemmed | 3D Primary Geochemical Halo Modeling and Its Application to the Ore Prediction of the Jiama Polymetallic Deposit, Tibet, China |
| title_short | 3D Primary Geochemical Halo Modeling and Its Application to the Ore Prediction of the Jiama Polymetallic Deposit, Tibet, China |
| title_sort | 3d primary geochemical halo modeling and its application to the ore prediction of the jiama polymetallic deposit tibet china |
| url | http://dx.doi.org/10.1155/2021/6629187 |
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