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Frontiers of Earth Science

Front. Earth Sci.    2015, Vol. 9 Issue (2) : 342-354     DOI: 10.1007/s11707-014-0465-4
RESEARCH ARTICLE |
The use of evidential belief functions for mineral potential mapping in the Nanling belt, South China
Yue LIU1,2,*(),Qiuming CHENG1,3,Qinglin XIA2,Xinqing WANG2
1. State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China
2. Faculty of Earth Resources, China University of Geosciences, Wuhan 430074, China
3. Department of Earth and Space Science and Engineering, Department of Geography, York University, Toronto M3J1P3, Canada
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Abstract

In this study, the evidential belief functions (EBFs) were applied for mapping tungsten polymetallic potential in the Nanling belt, South China. Seven evidential layers (e.g., geological, geochemical, and geophysical) related to tungsten polymetallic deposits were extracted from a multi-source geospatial database. The relationships between evidential layers and the target deposits were quantified using EBFs model. Four EBF maps (belief map, disbelief map, uncertainty map, and plausibility map) are generated by integrating seven evidential layers which provide meaningful interpretations for tungsten polymetallic potential. On the final predictive map, the study area was divided into three target zones of high potential, moderate potential, and low potential areas, among which high potential and moderate potential areas accounted for 17.8% of the total area, containing 81% of the total deposits. To evaluate the success rate accuracy, the receiver operating characteristic (ROC) curves and the area under the curves (AUC) for the belief map were calculated. The area under the curve is 0.81 which indicates that the capability for correctly classifying the areas with existing mineral deposits is satisfactory. The results of this study indicate that the EBFs were effectively used for mapping mineral potential and for managing uncertainties associated with evidential layers.

Keywords Dempster-Shafer theory of evidence      GIS      uncertainty      tungsten polymetallic deposit      ROC curve     
Corresponding Authors: Yue LIU   
Online First Date: 12 December 2014    Issue Date: 30 April 2015
 Cite this article:   
Yue LIU,Qiuming CHENG,Qinglin XIA, et al. The use of evidential belief functions for mineral potential mapping in the Nanling belt, South China[J]. Front. Earth Sci., 2015, 9(2): 342-354.
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http://journal.hep.com.cn/fesci/EN/10.1007/s11707-014-0465-4
http://journal.hep.com.cn/fesci/EN/Y2015/V9/I2/342
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Fig.1  Simplified geologic map of the Nanling belt, South China, showing tungsten polymetallic deposits.
Fig.2  Maps in the spatial database for mineral potential mapping, showing tungsten polymetallic deposits. (a) NE trending faults; (b) NW trending faults; (c) EW trending faults; (d) SN tending faults.
Fig.3  Maps in the spatial database for mineral potential mapping, showing tungsten polymetallic deposits. (a) geochemical element association; (b) lithostratigraphic contacts; (c) aeromagnetic.
Evidential layer Class description N(Cij)–N(CijD) N(CijD) Bel Dis Unc
Aeromagnetic 0–10% 18,749 15 0.072 0.100 0.828
Aeromagnetic 10%–20% 18,743 20 0.099 0.100 0.801
Aeromagnetic 20%–30% 18,747 17 0.083 0.100 0.817
Aeromagnetic 30%–40% 18,742 23 0.116 0.100 0.784
Aeromagnetic 40%–50% 18,741 23 0.116 0.100 0.784
Aeromagnetic 50%–60% 18,737 27 0.139 0.100 0.761
Aeromagnetic 60%–70% 18,862 28 0.144 0.100 0.756
Aeromagnetic 70%–80% 18,703 19 0.094 0.100 0.806
Aeromagnetic 80%–90% 18,701 20 0.099 0.100 0.801
Aeromagnetic 90%–100% 18,713 8 0.037 0.100 0.863
Contacts 0–1 km 41,942 133 0.671 0.125 0.204
Contacts 1–2 km 15,603 23 0.139 0.125 0.736
Contacts 2–3 km 14,553 12 0.074 0.125 0.801
Contacts 3–4 km 10,660 8 0.067 0.125 0.808
Contacts 4–5 km 11,609 3 0.022 0.125 0.852
Contacts 5–6 km 8,211 1 0.011 0.125 0.864
Contacts 6–7 km 7,339 0 0.000 0.125 0.875
Contacts >7 km 77,521 20 0.015 0.125 0.859
F3 0–10% 17,689 1 0.003 0.100 0.896
F3 10%–20% 20,148 2 0.006 0.100 0.894
F3 20%–30% 21,295 3 0.009 0.100 0.891
F3 30%–40% 18,992 9 0.030 0.100 0.870
F3 40%–50% 18,884 9 0.030 0.100 0.870
F3 50%–60% 19,287 10 0.033 0.100 0.867
F3 60%–70% 19,399 19 0.065 0.100 0.835
F3 70%–80% 17,792 27 0.106 0.100 0.794
F3 80%–90% 17,080 30 0.126 0.100 0.774
F3 90%–100% 16,872 90 0.592 0.100 0.309
NE trending fault 0–1 km 9,742 18 0.100 0.077 0.823
NE trending fault 1–2 km 7,235 18 0.136 0.077 0.787
NE trending fault 2–3 km 9,276 19 0.112 0.077 0.811
NE trending fault 3–4 km 8,890 15 0.090 0.077 0.833
NE trending fault 4–5 km 10,712 22 0.113 0.077 0.810
NE trending fault 5–6 km 8,866 17 0.104 0.077 0.819
NE trending fault 6–7 km 8,239 8 0.050 0.077 0.873
NE trending fault 7–8 km 8,766 13 0.079 0.077 0.845
NE trending fault >8 km 8,367 4 0.024 0.077 0.899
NW trending fault 0–1 km 7,838 10 0.067 0.077 0.856
NW trending fault 1–2 km 6,168 5 0.042 0.077 0.881
NW trending fault 2–3 km 5,568 7 0.066 0.077 0.857
NW trending fault 3–4 km 87,771 44 0.018 0.077 0.905
NW trending fault 4–5 km 64,241 111 0.347 0.111 0.542
NW trending fault 5–6 km 26,040 29 0.152 0.111 0.737
NW trending fault 6–7 km 24,903 23 0.123 0.111 0.766
NW trending fault >7 km 15,668 14 0.120 0.111 0.769
SN trending fault 0–1 km 15,756 10 0.083 0.111 0.806
SN trending fault 1–2 km 8,975 3 0.044 0.111 0.845
SN trending fault 2–3 km 6,981 3 0.057 0.111 0.832
SN trending fault 3–4 km 6,505 2 0.041 0.111 0.848
SN trending fault 4–5 km 18,369 5 0.034 0.111 0.855
SN trending fault 5–6 km 20,455 37 0.191 0.125 0.685
SN trending fault 6–7 km 12,117 16 0.129 0.125 0.746
SN trending fault 7–8 km 15,054 32 0.224 0.125 0.651
SN trending fault 8–9 km 12,245 9 0.069 0.125 0.806
SN trending fault 9–10 km 14,686 28 0.197 0.125 0.678
SN trending fault >10 km 10,579 8 0.072 0.125 0.803
EW trending fault 0–1 km 9,519 7 0.070 0.125 0.805
EW trending fault 1–2 km 92,783 63 0.048 0.125 0.827
EW trending fault 2–3 km 19,288 34 0.142 0.091 0.768
EW trending fault 3–4 km 12,161 11 0.066 0.091 0.843
EW trending fault 4–5 km 13,858 31 0.182 0.091 0.727
EW trending fault 5–6 km 12,862 20 0.120 0.091 0.790
EW trending fault 6–7 km 14,804 24 0.126 0.091 0.783
EW trending fault 7–8 km 11,852 8 0.049 0.091 0.860
EW trending fault 8–9 km 10,688 11 0.076 0.091 0.833
EW trending fault 9–10 km 11,044 4 0.026 0.091 0.883
EW trending fault 10–11 km 10,334 19 0.143 0.091 0.767
EW trending fault 11–12 km 9,498 5 0.038 0.091 0.871
EW trending fault >12 km 61,049 33 0.032 0.091 0.877
Tab.1  Results of EBFs calculated from seven evidential layers
Fig.4  Integration results using EBFs. (a) the belief map; (b) the disbelief map; (c) the uncertainty map; (d) the plausibility map.
Fig.5  (a) Variations of cumulative tungsten polymetallic deposits with belief probabilities; (b) Variations of cumulative tungsten polymetallic deposits with cumulative percent of study area.
Fig.6  Final tungsten polymetallic potential map based on the belief map.
Fig.7  Success rate curve for the belief map.
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