The use of evidential belief functions for mineral potential mapping in the Nanling belt, South China
Yue LIU, Qiuming CHENG, Qinglin XIA, Xinqing WANG
The use of evidential belief functions for mineral potential mapping in the Nanling belt, South China
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.
Dempster-Shafer theory of evidence / GIS / uncertainty / tungsten polymetallic deposit / ROC curve
[1] |
Abedi M, Norouzi GH, Fathianpour N (2013) Fuzzy outranking approach: a knowledge-driven method for mineral prospectivity mapping. Int J Appl Earth Obs Geoinformat, 21: 556–567
CrossRef
Google scholar
|
[2] |
Agterberg F P (1992). Combining indicator patterns in weights of evidence modeling for resource evaluation. Nonrenewable Resources, 1(1): 39–50
CrossRef
Google scholar
|
[3] |
Agterberg F P, Bonham–Carter G F, Cheng Q, Wright D F (1993). Weights of evidence model and weighted logistic regression in mineral potential mapping. In: Davis J C
|
[4] |
Agterberg F P, Cheng Q (2002). Conditional independence test of weights–of– evidence modeling. Nat Resour Res, 11(4): 249–255
CrossRef
Google scholar
|
[5] |
An P, Moon W M, Bonham–Carter G F (1992). On knowledge–based approach to integrating remote sensing, geophysical and geological information. Proceedings of International Geoscience and Remote Sensing Symposium (IGARSS), 34–38
|
[6] |
An P, Moon W M, Bonham–Carter G F (1994a). An objectoriented knowledge representation structure for exploration data integration. Nat Resour Res, 3(2): 132–145
CrossRef
Google scholar
|
[7] |
An P, Moon W M, Bonham–Carter G F (1994b). Uncertainty management integration of exploration data using the belief function. Nat Resour Res, 3(1): 60–71
CrossRef
Google scholar
|
[8] |
Behnia P (2007). Application of radial basis functional link networks to exploration for proterozoic mineral deposits in central Iran. Nat Resour Res, 16(2): 147–155
CrossRef
Google scholar
|
[9] |
Bonham–Carter G F, Agterberg F P, Wright D F (1989). Weights of evidence modelling: a new approach to mapping mineral potential. In Agterberg F P and Bonham–Carter G F eds. Statistical Applications in the Earth Sciences: Geol. Survey Canada Paper 89-9, 171–183
|
[10] |
Carranza E J M, Hale M (2001a). Logistic regression for geologically constrained mapping of gold potential, Baguio District. Phil.Exploration and Mining Geol., 10(3): 165–175
CrossRef
Google scholar
|
[11] |
Carranza E J M, Hale M (2001b). Geologically constrained fuzzy mapping of gold mineralization potential, Baguio District, Philippines. Nat Resour Res, 10(2): 125–136
CrossRef
Google scholar
|
[12] |
Carranza E J M, Hale M (2003). Evidential belief functions for data–driven geologically constrained mapping of gold potential, Baguio district, Philippines. Ore Geol Rev, 22(1–2): 117–132
CrossRef
Google scholar
|
[13] |
Carranza E J M (2004). Weights of evidence modeling of mineral potential: A case study using small number of prospects, Abra, Philippines. Nat Resour Res, 13(3): 173–187
CrossRef
Google scholar
|
[14] |
Carranza E J M, Woldai T, Chikambwe E M (2005). Application of data–driven evidential belief functions to potential mapping for aquamarine–bearing pegmatites, Lundazi district Zambia. Nat Resour Res, 14(1): 47–63
CrossRef
Google scholar
|
[15] |
Carranza E J M, Hale M, Faassen C (2008a). Selection of coherent deposit–type locations and their application in data–driven mineral prospectivity mapping. Ore Geol Rev, 33(3–4): 536–558
CrossRef
Google scholar
|
[16] |
Carranza E J M, Van Ruitenbeek F J A, Hecker C A, Van der Meijde M, Van derMeer F D (2008b). Knowledge-guided data-driven evidential belief modeling of mineral prospectivity in Cabo de Gata, SE Spain. Int J Appl Earth Obs Geoinf, 10(3): 374–387
CrossRef
Google scholar
|
[17] |
Carranza E J M (2014). Data-driven evidential belief modeling of mineral potential using few Prospects and evidence with missing values. Nat Resour Res
|
[18] |
Chen Y, Pei R, Zhang H (1990). Mineral deposit of nonferrous metal and rare metal associated with Mesozoic granite in the Nanling region. Bulletin of the Chinese Academy of Geological Sciences, 20(1): 79–85 (in Chinese)
|
[19] |
Cheng Q, Agterberg F P (1999). Fuzzy weights of evidence method and its application in mineral potential mapping. Nat Resour Res, 8(1): 27–35
CrossRef
Google scholar
|
[20] |
Cheng Q (2012). Application of a newly developed boost weights of evidence model (BoostWofE) for mineral resource quantitative assessments. Journal of Jilin University, 42(6): 1976–1984 (Earth Science Edition)
|
[21] |
Chi Q, Wang X, Xu S (2012). Temporal and spatial distribution of tungsten and tin in South China Continent. Earth Sci Front, 19(3): 70–83 (in Chinese with English abstract)
|
[22] |
Chung C F, Fabbri A (1993). The representation of geoscience information for data integration. Nonrenewable Resources, 2(2): 122–139
CrossRef
Google scholar
|
[23] |
Chung C F, Fabbri A G (1999). Probabilistic prediction models for landslide hazard mapping. Photogramm Eng Remote Sensing, 65(12): 1389–1399
|
[24] |
Chung C F, Fabbri A (2003). Validation of spatial prediction models for landslide hazard mapping. Nat Hazards, 30(3): 451–472
CrossRef
Google scholar
|
[25] |
Dempster A P (1967). Upper and lower probabilities induced by a multivalued mapping. Ann Math Stat, 38(2): 325–339
CrossRef
Google scholar
|
[26] |
Dempster A P (1968). A generalization of Bayesian inference. J R Stat Soc, B, 30(2): 205–247
|
[27] |
Hsieh P S, Chen C H, Yang H J, Lee C Y (2008). Petrogenesis of the Nanling Mountains granites from South China: Constraints from systematic apatite geochemistry and whole-rock geochemical and Sr–Nd isotope compositions. J Asian Earth Sci, 33(5–6): 428–451
CrossRef
Google scholar
|
[28] |
Hu R, Bi X, Jiang G, Chen H, Peng J, Qi Y, Wu L, Wei W (2012). Mantle-derived noble gases in ore-forming fluids of the granite-related Yaogangxian tungsten deposit, Southeastern China. Miner Depos, 47(6): 623–632
CrossRef
Google scholar
|
[29] |
Hu R, Zhou M (2012). Multiple Mesozoic mineralization events in South China—an introduction to the thematic issue. Miner Depos, 47(6): 579–588
CrossRef
Google scholar
|
[30] |
Hua R, Chen P, Zhang W, Yao J, Lin J, Zhang Z, Gu S, Liu X, Qi H(2005). Metallogeneses related to Mesozoic granitoids in the Nanling Range, and their geodynamic settings. Acta Geologica Sinica (English Edition), 79(6): 801–811
|
[31] |
Leite E P, Filho C R D S (2009) Probabilistic neural networks applied to mineral potential mapping for platinum group elements in the Serra Leste region, Caraja’s Mineral Province, Brazil. Comput Geosci, 35: 675–687
CrossRef
Google scholar
|
[32] |
Lee S, Dan N T (2005). Probabilistic landslide susceptibility mapping on the Lai Chau province of Vietnam: focus on the relationship between tectonic fractures and landslides. Environmental Geology, 48(6): 778–787
CrossRef
Google scholar
|
[33] |
Lee S, Pradhan B (2007). Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides, 4(1): 33–41
CrossRef
Google scholar
|
[34] |
Li B (2011). Synchronization theory and tungsten-polymetallicmineralization distribution in the Qianlishan–Qitianling area, Southern Hunan. J. Earth Sci, 22: 726–736
CrossRef
Google scholar
|
[35] |
Li X, Li W, Wang X, Li Q, Liu Y, Tang G (2009). Role of mantle-derived magma in genesis of early Yanshanian granites in the Nanling Range, South China: in situ zircon Hf-O isotopic constraints. Science China Ser D, 52(9): 1262–1278
CrossRef
Google scholar
|
[36] |
Liu N, Yu C (2011). Analysis of onset and development of ore formation in Dajishan tungsten ore area, Jiangxi Province, China. J. Earth Sci, 22(1): 67–74
CrossRef
Google scholar
|
[37] |
Liu Y, Cheng Q, Xia Q, Wang X (2013a). Application of singularity analysis for mineral potential identification using geochemical data — a case study: Nanling W–Sn–Mo polymetallic metallogenic belt, South China. J Geochem Explor, 134: 61–72
CrossRef
Google scholar
|
[38] |
Liu Y, Xia Q, Cheng Q, Wang X (2013b). Application of singularity theory and logistic regression model for tungsten polymetallic potential mapping. Nonlinear Process Geophys, 20(4): 445–453
CrossRef
Google scholar
|
[39] |
Liu Y, Cheng Q, Xia Q, Wang X (2014a). Mineral potential mapping for tungsten polymetallic deposits in the Nanling metallogenic belt, South China. J. Earth Sci, 25(4): 689–700
CrossRef
Google scholar
|
[40] |
Liu Y, Cheng Q, Xia Q, Wang X (2014b). Identification of REE mineralization-related geochemical anomalies using fractal/multifractal methods in the Nanling belt, South China. Environ Earth Sci
|
[41] |
Liu Y, Cheng Q, Xia Q, Wang X (2014c). Multivariate analysis of stream sediment data from Nanling metallogenic belt, South China. Geochem Explor Environ Anal,
|
[42] |
Luo X, Dimitrakopoulos R (2003). Data-driven fuzzy analysis in quantitative mineral resource assessment. Comput Geosci, 29(1): 3–13
CrossRef
Google scholar
|
[43] |
Mao J, Xie G, Guo C, Chen Y (2007). Large-scale tungsten-tin mineralization in the Nanling region South China: Metallogenic ages and corresponding geodynamic processes. Acta Petrol Sin, 23(10): 2329–2338 (in Chinese with English abstract)
|
[44] |
Mao J, Xie G, Cheng Y, Chen Y (2009). Mineral deposit models of Mesozoic ore deposits in South China. Geological Review, 55(3): 347–354 (in Chinese with English abstract)
|
[45] |
Moon W M (1989). Integration of remote sensing and geological/geophysical data using Dempster-Shafer approach. Proceedings of International Geoscience and Remote Sensing Symposium (IGARSS), 838–841
|
[46] |
Nykänen V, Ojala V J (2007). Spatial analysis techniques as successful mineral-potential mapping tools for orogenic gold deposits in the Northern Fennoscandian Shield, Finland. Nat Resour Res, 16(2): 85–92
CrossRef
Google scholar
|
[47] |
Oh H J, Lee S (2010). Application of artificial neural network for gold-silver deposits potential mapping: A case study of Korea. Nat Resour Res, 19(2): 103–124
CrossRef
Google scholar
|
[48] |
Pei R, Wang Y, Wang H (2009). Ore-forming specialty of the tectono- magmatic zone in Nanling region and its emplacement dynamics for metallogenic series of W–Sn polymetallic deposits. Geology in China, 36(3): 483–489 (in Chinese with English abstract)
|
[49] |
Peng J, Zhou M, Hu R, Shen N, Yuan S, Bi X, Du A, Qu W (2006). Precise molybdenite Re–Os and mica Ar–Ar dating of the Mesozoic Yaogangxian tungsten deposit, central Nanling district, South China. Miner Depos, 41(7): 661–669
CrossRef
Google scholar
|
[50] |
Porwal A, Carranza E J M, Hale M (2003a). Knowledge-driven and data-driven fuzzy models for predictive mineral potential mapping. Nat Resour Res, 12(1): 1–25
CrossRef
Google scholar
|
[51] |
Porwal A, Carranza E J M, Hale M (2003b). Artificial neural networks for mineral-potential mapping: a case study from Aravalli Province, Western India. Nat Resour Res, 12(3): 155–171
CrossRef
Google scholar
|
[52] |
Porwal A, González-Álvarez I, Markwitz V, McCuaig T C, Mamuse A (2010). Weights-of-evidence and logistic regression modeling of magmatic nickel sulfide prospectivity in the Yilgarn Craton, Western Australia. Ore Geol Rev, 38(3): 184–196
CrossRef
Google scholar
|
[53] |
Qin B (1987). A geological interpretation on the regional gravity and magnetic anomalies in Nanling Area. Hunan Geology, 1: 1–15 (in Chinese with English abstract)
|
[54] |
Shafer G (1976). A Mathematical Theory of Evidence. Princeton: Princeton Univ. Press, 297
|
[55] |
Shu X, Wang X, Tao S, Xu X, Dai M (2011). Trace elements, U–Pb ages and Hf isotopes of zircons from Mesozoic granites in the western Nanling Range, South China: Implications for petrogenesis and W–Sn mineralization. Lithos, 127(3–4): 468–482
CrossRef
Google scholar
|
[56] |
Yuan Z, Wu C, Xu L, Ni Y (1993). The distribution of trace elements in granitoids in the Nanling Region of China. Chinese Journal of Geochemistry, 12(3): 193–205
CrossRef
Google scholar
|
/
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