| China Geological Survey Chinese Academy of Geological Sciences | Host |
| 科学出版社 | Publish |
| Citation: |
OUYANG Yuan, LIU Hong, LI Guangming, MA Dongfang, ZHANG Linkui, HUANG Hanxiao, ZHANG Jinghua, ZHANG Tengjiao, LIU Xiao, ZHAO Yinbing, LI Fu. 2023. Mineral search prediction based on Random Forest algorithm——A case study on porphyry-epithermal copper polymetallic deposits in the western Gangdise meatallogenic belt[J]. Geology in China, 50(2): 303-330. doi: 10.12029/gc20201026001
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Mineral search prediction based on Random Forest algorithm——A case study on porphyry-epithermal copper polymetallic deposits in the western Gangdise meatallogenic belt
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Abstract
The paper is the result of geological survey engineering.
Objective The core problem of prospecting prediction is the nonlinear relationship between mineral distribution and mineral-controlling geological factors. Big data and machine learning technology have shown great advantages in solving such complex nonlinear relationship problems. The prediction dataset of small-scale geochemical remote information has the characteristics of high and extremely unbalanced, which is difficult to adapt by traditional logical assumptions or statistical analysis. Therefore, this paper attempts to introduce the random forest algorithm into the field of small-scale prospecting to explore the application of big data and machine learning technology in small-scale mineralization prediction.
Methods In recent years, several Porphyry-epithermal copper polymetallic deposits (such as Luerma, Bolazha, Daruo, Hongshan, and Luobuzhen, etc.) have been discovered in the western Gangdise mineralized belt, which proved that the western Gangdise belt has great prospecting potential for porphyry and epithermal Cu-Au polymetallic deposits. Combined with the comprehensive information of typical deposits, regional geology, geophysics, geochemistry, and remote sensing, this paper uses the random forest method to carry out the prospecting prediction of porphyry and epithermal Cu-Au polymetallic deposits in the western Gangdise belt.
Results This work has delineated 11 porphyry copper polymetallic prospect areas (including 2 levels Ⅰ prospect areas, 3 level Ⅱ prospect areas, and 6 level Ⅲ prospect areas), of which Luobuzhen, Dajiacuo, Daruo, Balaza, Gaerqiong, and Budongla have great prospecting potential and are expected to find new ore deposits or points.
Conclusions The under-sampling random forest prediction model based on big data machine learning is expected to adapt to the high-dimensional and extremely unbalanced characteristics of prediction data of comprehensive geophysical and geochemical remote information and provide direction for regional prospecting prediction at the scale of the metallogenic belt. The prospective area determined in this work is expected to find new deposits (points), which opens a new vision for ore prospecting and exploration in the Gangdise metallogenic belt.
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References
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OUYANG Yuan, LIU Hong, LI Guangming, MA Dongfang, ZHANG Linkui, HUANG Hanxiao, ZHANG Jinghua, ZHANG Tengjiao, LIU Xiao, ZHAO Yinbing, LI Fu. 2023. Mineral search prediction based on Random Forest algorithm——A case study on porphyry-epithermal copper polymetallic deposits in the western Gangdise meatallogenic belt[J]. Geology in China, 50(2): 303-330. doi: 10.12029/gc20201026001
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Figure 1.
Mineral geological map of the Gangdise metallogenic belt (a, modified from Liu Hong et al., 2019a, b, 2020a; b, modified from Liu Hong et al., 2019c, 2020b)
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Figure 2.
Schematic diagram of random forest model
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Figure 3.
Schematic diagram of receiver operating characteristic curve(ROC)
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Figure 4.
Mineral geological map of the western Gangdise metallogenic belt (modified from Huang Hanxiao et al., 2019)
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Figure 5.
Summary of the geotectonic and metallogenic evolution of the western Gangdise metallogenic belt (modified from Huang Hanxiao et al., 2019)
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Figure 6.
The satellite gravity isogram map of the western Gangdise metallogenic belt(after the University of California public data)
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Figure 7.
The detail maps of satellite gravity wavelet analysis of the western Gangdise metallogenic (after the University of California public data)
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Figure 8.
The aeromagnetic △T pole isogram map of the western Gangdise metallogenic belt (after the AGRS public data)
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Figure 9.
The detail maps of aeromagnetic △T pole wavelet analysis of the western Gangdise metallogenic belt (after the AGRS public data)
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Figure 10.
The distribution map of inferred rock mass based on △T pole abnormal in the western Gangdise metallogenic belt
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Figure 11.
Cluster analysis chart of geochemical elements in the western Gangdise metallogenic belt
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Figure 12.
Component plot in rotated space of geochemical elements in the western Gangdise metallogenic belt
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Figure 13.
Geochemical maps (a, b) and factor score isogram maps (c, d) in the western Gangdise metallogenic belt
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Figure 14.
Geochemical subdivisions map in the western Gangdise metallogenic belt
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Figure 15.
Remote sensing prospecting information maps in the western Gangdise metallogenic belt
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Figure 16.
Remote sensing comprehensive prospecting information map in the western Gangdise metallogenic belt
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Figure 17.
The comparison of the ore units amountin data set uni (a) and the ore units amount in training subset unit (b) in the western Gangdise metallogenic belt
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Figure 18.
ROC curves of the data set under different models in the western Gangdise metallogenic belt
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Figure 19.
The isogram map of posterior probability in the western Gangdise metallogenic belt
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Figure 20.
The comparison of posterior probabilities for unit gridsto known ore spots in the western Gangdise metallogenic belt
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Figure 21.
The prospecting prediction map for the copper polymetallic deposits related to porphyry system in the western Gangdise metallogenic belt