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Application of Artificial Neural Network for Gold–Silver Deposits Potential Mapping: A Case Study of Korea

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Abstract

The aim of this study is to analyze hydrothermal gold–silver mineral deposits potential in the Taebaeksan mineralized district, Korea, using an artificial neural network (ANN) and a geographic information system (GIS) environment. A spatial database considering 46 Au and Ag deposits, geophysical, geological, and geochemical data was constructed for the study area using the GIS. The geospatial factors were used with the ANN to analyze mineral potential. The Au and Ag mineral deposits were randomly divided into a training set (70%) to analyze mineral potential using ANN and a test set (30%) to validate predicted potential map. Four different training datasets determined from likelihood ratio and weight of evidence models were applied to analyze and validate the effect of training. Then, the mineral potential index (MPI) was calculated using the trained back-propagation weights, and mineral potential maps (MPMs) were constructed from GIS data for the four training cases. The MPMs were then validated by comparison with the test mineral occurrences. The validation results gave respective accuracies of 73.06, 73.52, 70.11, and 73.10% for the training cases. The comparison results of some training cases showed less sensitive to training data from likelihood ratio than weight of evidence. Overall, the training cases selected from 10% area with low and high index value of MPML and MPMW gave higher accuracy (73.52 and 73.10%) for MPMs than those (73.06 and 70.11%, respectively) from known deposits and 10% area with low index value of MPIL and MPIW.

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References

  • Agterberg, F. P., 1988, Application of recent developments of regression analysis in regional mineral resource evaluation, in Chung, C. F., Fabbri, A. G., and Sinding-Larsen, R., eds., Quantitative Analysis of Mineral and Energy Resources: D Reidel Publishing, Dordrecht, p. 1–28.

    Google Scholar 

  • Agterberg, F. P., and Bonham-Carter, G. F., 2005, Measuring performance of mineral-potential maps: Nat. Resour. Res., v. 14, p. 1–17.

    Article  Google Scholar 

  • Agterberg, F. P., Bonham-Carter, G. F., and Wright, D. F., 1990, Statistical pattern integration for mineral exploration, in Gaal, G., and Merriam, D. F., eds., Computer Applications in Resource Estimation Prediction and Assessment for Metals and Petroleum: Pergamon Press, Oxford, p. 1–21.

    Google Scholar 

  • An, P., and Moon, W. M., 1993, Evidential reasoning structure for integrating geophysical, geological and remote sensing data, Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS): IEEE, Tokyo, p. 2141–2144.

  • An, P., Moon, W. M., and Rencz, A. N., 1991, Application of fuzzy set theory to integrated mineral exploration: Can. J. Explor. Geophys., v. 27, p. 1–11.

    Google Scholar 

  • Behnia, P., 2007, Application of radial basis functional link networks to exploration for proterozoic mineral deposits in central Ira: Nat. Resour. Res., v. 16, p. 147–155.

    Article  Google Scholar 

  • Bonham-Carter, G. F., 1994, Geographic information systems for geoscientists: modeling with GIS, Computer Methods in the Geosciences 13: Pergamon Press, Oxford, pp. 398.

  • Bonham-Carter, G. F., Agterberg, F. P., and Wright, D. F., 1988, Integration of geological datasets for gold exploration in Nova Scotia: Photogram. Eng. Remote Sens., v. 54, p. 1585–1592.

    Google Scholar 

  • Bonham-Carter, G. F., Agterberg, F. P., and Wright, D. F., 1989, Weights of evidence modeling: a new approach to mapping mineral potential, in Agterberg, F. P., and Bonham-Carter, G. F., eds., Statistical Applications in the Earth Sciences: Geological Survey of Canada 98, p. 171–183.

  • Brown, W. M., Gedeon, T. D., Groves, D. I., and Barnes, R. G., 2000, Artificial neural networks: a new method for mineral prospectively mapping: Aust. J. Earth Sci., v. 47, p. 757–770.

    Article  Google Scholar 

  • Brown, W., Groves, D., and Gedeon, T., 2003, Use of fuzzy membership input layers to combine subjective geological knowledge and empirical data in a neural network method for mineral-potential mapping: Nat. Resour. Res., v. 12, p. 183–200.

    Article  Google Scholar 

  • 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., v. 13, p. 173–187.

    Article  Google Scholar 

  • Carranza, E. J. M., 2009, Objective selection of suitable unit cell size in data-driven modeling of mineral prospectivity: Comput. Geosci., v. 35, p. 2032–2046.

    Article  Google Scholar 

  • Carranza, E. J. M., and Hale, M., 2000, Geologically constrained probabilistic mapping of gold potential, Baguio district, Philippines: Nat. Resour. Res., v. 9, p. 237–253.

    Article  Google Scholar 

  • Carranza, E. J. M., Hale, M., and Faassen, C., 2008, Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity mapping: Ore Geol. Rev., v. 33, p. 536–558.

    Article  Google Scholar 

  • Carranza, E. J. M., Woldai, T., and Chikambwe, E. M., 2005, Application of data-driven evidential belief functions to prospectivity mapping for aquamarine-bearing pegmatites, Lundazi District, Zambia: Nat. Resour. Res., v. 14, p. 47–63.

    Article  Google Scholar 

  • Chi, K. H., Lee, J. S., Jin, M. S., Chi, S. J., and Park, S. H., 2001, Construction of GIS based geological database of South Korea Area: Korea Institute of Geoscience and Mineral Resources, Ministry of Science & Technology KR-01(T)-08, pp. 210.

  • Chung, C. F., and Agterberg, F. P., 1980, Regression models for estimating mineral resources from geological map data: Math. Geol., v. 12, p. 473–488.

    Article  Google Scholar 

  • D’Ercole, C., Groves, D. I., and Knox-Robinson, C. M., 2000, Using fuzzy logic in a Geographic Information System environment to enhance conceptually based prospectively analysis of Mississippi Valley-type mineralization: Aust. J. Earth Sci., v. 47, p. 913–927.

    Article  Google Scholar 

  • De Quadros, T. F. P., Koppe, J. C., Strieder, A. J., and Costa, J. F. C. L., 2006, Mineral-potential mapping: a comparison of weights-of-evidence and fuzzy methods: Nat. Resour. Res., v. 15, p. 49–65.

    Article  Google Scholar 

  • Eddy, B. G., Bonham-Carter, G. F., and Jefferson, C. W., 1995, Mineral resource assessment of the Parry Islands, high Arctic, Canada: a GIS-base fuzzy logic model, Proceedings Canadian Conference on GIS, CD ROM session C3, Paper 4.

  • Garrett, J., 1994, Where and why artificial neural networks are applicable in civil engineering: J. Comput. Civil Eng., v. 8, p. 129–130.

    Article  Google Scholar 

  • Harris, D., and Pan, G., 1999, Mineral favorability mapping: a comparison of artificial neural networks, logistic regression, and discriminant analysis: Nat. Resour. Res., v. 8, p. 93–109.

    Article  Google Scholar 

  • Harris, D., Zurcher, L., Stanley, M., Marlow, J., and Pan, G., 2003, A comparative analysis of favorability mappings by weights of evidence, probabilistic neural networks, discriminant analysis, and logistic regression: Nat. Resour. Res., v. 12, p. 241–255.

    Article  Google Scholar 

  • Hengl, T., 2006, Finding the right pixel size: Comput. Geosci., v. 32, p. 1283–1298.

    Article  Google Scholar 

  • Hines, J. W., 1997, Fuzzy and artificial neural approaches in engineering: Wiley, New York, pp. 201.

    Google Scholar 

  • Jianping, C., Gongwen, W., and Changbo, H., 2005, Quantitative prediction and evaluation of mineral resources based on GIS: a case study in Sanjiang region, southwestern China: Nat. Resour. Res., v. 14, p. 285–294.

    Article  Google Scholar 

  • Kim, J. H., Kee, W. S., and Seo, S. K., 1996, Geological structures of the Yeoryang-Imgye area, northern part of Mt. Taebaek Region, Korea: J. Geol. Soc. Korea, v. 32, p. 1–15.

    Google Scholar 

  • Kim, J. C., Koh, H. J., Lee, S. R., Lee, C. B., Choi, S. J., and Park, K. H., 2001, Explanatory note the Gangreung-Sokcho Sheet: Korean Institute of Geoscience and Mineral Resources KR-M 25-08 2001, pp. 76.

  • Knox-Robinson, C. M., 2000, Vectorial fuzzy logic: a novel technique for enhanced mineral prospectivity mapping, with reference to the orogenic gold mineralisation potential of the Kalgoorlie Terrane, Western Australia: Aust. J. Earth Sci., v. 47, p. 929–941.

    Article  Google Scholar 

  • Koh, S. M., Kim, S. Y., Lee, D. J., Kim, D. O., Lee, H. Y., Kim, Y. U., Yoo, J. H., Kim, Y. I., Ryoo, C. R., and Song, M. S., 2003, Construction of the data-base and assessment of domestic mineral resources III (area of 1:250,000 Seoul and Gangreung Geological Sheets): Ministry of Commerce, Industry and Energy KR-2002-C-14-2003-R, pp. 84.

  • Koo, S. B., Cho, J. D., Lee, T. S., Park, Y. S., Lim, M. T., Choi, J. H., Sung, N. H., Hwang, H. S., and Koh, I. S., 2001, Regional geophysical exploration: Korean Institute of Geoscience and Mineral Resources, Ministry of Commerce, industry and Energy KR-2000-R-11-2001-R, pp. 70.

  • Lee, S., Choi, J. W., and Woo, I., 2004, The effect of spatial resolution on the accuracy of landslide susceptibility mapping: a case study in Boun, Korea: Geosci. J., v. 8, p. 51–60.

  • Lee, C. H., and Park, H. I., 1996, Epithermal gold-silver mineralization and depositional environment carbonate-hosted replacement type Baegjeon Deposits, Korea: Econ. Environ. Geol., v. 29, p. 105–117.

    Google Scholar 

  • Lee, S., Ryu, J. H., and Kim, I. S., 2007, Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea: Landslide, v. 4, p. 327–338.

    Article  Google Scholar 

  • Lee, J. S., Seo, H. J., and Hwnag, I. H., 1998, Regional geochemical mapping of the Kangneung Sheet (1:250,000): Korean Institute of Geoscience and Mineral Resources, Korea Institute of Geology, Mining & Materials KR-98(C)-02, pp. 147.

  • Leite, E. P., and Souza Filho, C. R., 2009, Artificial neural networks applied to mineral potential mapping for copper-gold mineralizations in the Carajas Mineral Province, Brazil: Geophys. Prospect., v. 57, p. 1049–1065.

    Article  Google Scholar 

  • Luo, X., and Dimitrakopoulos, R., 2003, Data-driven fuzzy analysis in quantitative mineral resource assessment: Comput. Geosci., v. 29, p. 3–13.

    Article  Google Scholar 

  • Moon, W. M., 1990, Integration of geophysical and geological data using evidence theory function: IEEE Trans. Geosci. Remote Sens., v. 28, p. 711–720.

    Article  Google Scholar 

  • Moon, W. M., 1993, On mathematical representation and integration of multiple spatial geoscience data sets: Can. J. Remote Sens., v. 19, p. 63–67.

    Google Scholar 

  • Nykanen, V., 2008, Radial basis functional link nets used as a prospectivity mapping tool for orogenic gold deposits within the Central Lapland Greenstone Belt, Northern Fennoscandian Shield: Nat. Resour. Res., v. 17, p. 29–47.

    Article  Google Scholar 

  • Nykanen, V., and Raines, G. L., 2006, Quantitative analysis of scale of aeromagnetic data raises questions about geologic-map scale: Nat. Resour. Res., v. 15, p. 213–222.

    Article  Google Scholar 

  • Nykanen, V., and Salmirinne, H., 2007, Prospectivity analysis of gold using regional geophysical and geochemical data from the Central Lapland Greenstone Belt, Finland, in Ojala, V. J., ed., Gold in the Central Lapland Greenstone Belt: Geological Survey of Finland, Special Paper 44, p. 251–269.

  • Oh, H. J., and Lee, S., 2008, Regional probabilistic and statistical mineral potential, mapping of gold–silver deposits using GIS in the Gangreung Area, Korea: Resource Geol., v. 58, p. 171–187.

    Article  Google Scholar 

  • Pan, G. C., 1996, Extended weights of evidence modeling for the pseudo-estimation of metalgrades: Nonrenew. Resour., v. 5, p. 53–76.

    Article  Google Scholar 

  • Paola, J. D., and Schowengerdt, R. A., 1995, A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery: Int. J. Remote Sens., v. 16, p. 3033–3058.

    Article  Google Scholar 

  • Park, H. I., Chang, H. W., and Jin, M. S., 1988, K-Ar ages of mineral deposits in the Taebaek Mountain district: J. Korean Inst. Mining Geol., v. 21, p. 57–67.

    Google Scholar 

  • Porwal, A., Carranza, E. J. M., and Hale, M., 2003, Artificial neural networks for mineral potential mapping: a case study from Aravalli Province, western India: Nat. Resour. Res., v. 12, p. 155–177.

    Article  Google Scholar 

  • Porwal, A., Carranza, E. J. M., and Hale, M., 2006, A hybrid fuzzy weights-of-evidence model for mineral potential mapping: Nat. Resour. Res., v. 15, p. 1–14.

    Article  Google Scholar 

  • Raines, G. L., 1999, Evaluation of weights of evidence to predict epithermal-gold deposits in the Great Basin of the Western United States: Nat. Resour. Res., v. 8, p. 257–276.

    Article  Google Scholar 

  • Raines, G. L., Connors, K. A., and Chorlton, L. B., 2007, Porphyry copper deposit tract definition—a global analysis comparing geologic map scales: Nat. Resour. Res., v. 16, p. 191–198.

    Article  Google Scholar 

  • Rencz, A. N., Harris, J. R., Watson, G. P., and Murphy, B., 1994, Data integration for mineral exploration in the Antigonish Highlands, Nova Scotia: application of GIS and remote sensing: Can. J. Remote Sens., v. 20, p. 257–267.

    Google Scholar 

  • Rigol-Sanchez, J. P., Chica-Olmo, M., and Abarca-Hernandez, F., 2003, Artificial neural networks as a tool for mineral potential mapping with GIS: Int. J. Remote Sens., v. 24, p. 1151–1156.

    Article  Google Scholar 

  • Roy, R., Cassard, D., Cobbold, P. R., Rossello, E. A., Bailly, L., and Lips, A. L. W., 2006, Predictive mapping for copper-gold magmatic-hydrothermal systems in NW Argentina: use of a regional-scale GIS, application of an expert-guided data-driven approach, and comparison with results from a continental-scale GIS: Ore Geol. Rev., v. 29, p. 260–286.

    Article  Google Scholar 

  • Singer, D. A., and Kouda, R., 1996, Application of a feed forward neural network in the search for Kuroko deposits in the Hokuroku District, Japan: Math. Geol., v. 28, p. 1017–1023.

    Article  Google Scholar 

  • Skabar, A. A., 2005, Mapping mineralization probabilities using multilayer perceptrons: Nat. Resour. Res., v. 14, p. 109–123.

    Article  Google Scholar 

  • Skabar, A., 2007, Modeling the spatial distribution of mineral deposits using neural networks: Nat. Resource Model., v. 20, p. 435–450.

    Article  Google Scholar 

  • Tangestani, M. H., and Moore, F., 2001, Porphyry copper potential mapping using the weights-of-evidence modeling a GIS northern Shahr-e-Babak Iran: Aust. J. Earth Sci., v. 48, p. 913–927.

    Google Scholar 

  • Tangestani, M. H., and Moore, F., 2002, The use of Dempster-Shafer model and GIS in integration of geoscientific data for porphyry copper potential mapping, north of Shahr-e-Babak, Iran: Int. J. Appl. Earth Observation Geoinformation, v. 4, p. 65–74.

    Article  Google Scholar 

  • Xu, S., Cui, Z. K., Yang, X. L., and Wang, G. J., 1992, A preliminary application of weights of evidence in gold exploration in Xionger mountain region, Henan province: Math. Geol., v. 24, p. 663–674.

    Article  Google Scholar 

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Acknowledgments

This research was supported by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources (KIGAM) funded by the Ministry of Science and Technology of Korea.

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Authors and Affiliations

  1. Geoscience Information Center, Korea Institute of Geoscience & Mineral Resources (KIGAM), 92, Gwahang-no, Yuseong-gu, Daejeon, 305-350, Korea

    Hyun-Joo Oh & Saro Lee

Authors
  1. Hyun-Joo Oh
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  2. Saro Lee
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Correspondence to Saro Lee.

Appendix: Spatial Relationship Between Mineral Deposits and Some Related Factors

Appendix: Spatial Relationship Between Mineral Deposits and Some Related Factors

Factor

Likelihood Ratio

Weight of Evidence

Classa

No. of Pixels

%Area

Mineral occ.

%Occ.

LSb

W +

W −

C

C/S(c)

Al (ppb)

26.00–44.15

116666

10.00 

3

9.38

0.94

−0.06

0.01

−0.07

−0.12

44.16–84.54

116651

10.00 

3

9.38

0.94

−0.06

0.01

−0.07

−0.12

84.55–103.39

116737

10.01 

4

12.50

1.25

0.22

−0.03

0.25

0.47

103.40–112.87

116716

10.01 

2

6.25

0.62

−0.47

0.04

−0.51

−0.70

112.88–119.29

116695

10.00 

7

21.88

2.19

0.78

−0.14

0.92

2.16

119.30–124.97

116601

10.00 

7

21.88

2.19

0.78

−0.14

0.92

2.16

124.98–133.04

116613

10.00 

1

3.13

0.31

−1.16

0.07

−1.24

−1.22

133.05–164.69

116594

10.00 

3

9.38

0.94

−0.06

0.01

−0.07

−0.12

164.70–231.11

116586

10.00 

2

6.25

0.63

−0.47

0.04

−0.51

−0.70

231.12–499.99

116579

9.99 

0

0.00

0.00

NaN

0.11

NaN

NaN

As (ppm)

1.01–14.58

116689

10.00 

0

0.00

0

NaN

0.11

NaN

NaN

14.59–21.78

116779

10.01 

8

25.00

2.5

0.92

−0.18

1.1

2.69

21.79–27.56

116734

10.01 

0

0.00

0

NaN

0.11

NaN

NaN

27.57–35.09

116702

10.00 

3

9.38

0.94

−0.07

0.01

−0.07

−0.12

35.10–43.43

116782

10.01 

1

3.13

0.31

−1.16

0.07

−1.24

−1.22

43.44–47.59

116901

10.02 

4

12.50

1.25

0.22

−0.03

0.25

0.47

47.60–49.47

116516

9.99 

0

0.00

0

NaN

0.11

NaN

NaN

49.48–49.99

65606

5.62 

3

9.38

1.67

0.51

−0.04

0.55

0.91

50.00

283729

24.32 

13

40.63

1.67

0.51

−0.24

0.76 

2.1

Ba (ppb)

2.00–3.99

117477

10.07 

0

0.00 

0.00 

NaN

0.11 

NaN

NaN

4.00–5.96

116734

10.01 

8

25.00 

2.50 

0.92 

−0.18 

1.10 

0.41 

5.97–7.04

117258

10.05 

2

6.25 

0.62 

−0.48 

0.04 

−0.52 

0.73 

7.05–7.86

116532

9.99 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

7.87–8.55

116787

10.01 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49 

8.56–9.61

116822

10.02 

4

12.50 

1.25 

0.22 

−0.03 

0.25 

0.53 

9.62 –10.87

116583

9.99 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

10.88–13.28

116120

9.96 

1

3.13 

0.31 

−1.16 

0.07 

−1.23 

1.02 

13.29–17.38

116242

9.97 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

17.39–200.97

115883

9.93 

3

9.38 

0.94 

−0.06 

0.01 

−0.06 

0.61 

Ca (ppm)

1.53–6.24

116712

10.01 

2

6.25 

0.62 

−0.47 

0.04 

−0.51 

0.73 

6.25–18.99

116637

10.00 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49

19.00–28.24

116714

10.01 

1

3.13 

0.31 

−1.16 

0.07 

−1.24 

1.02 

28.25–35.41

116742

10.01 

3

9.38 

0.94 

−0.07 

0.01 

−0.07 

0.61 

35.42–40.44

116662

10.00 

2

6.25 

0.62 

−0.47 

0.04 

−0.51 

0.73 

40.45–43.42

116679

10.00 

2

6.25 

0.62 

−0.47 

0.04 

−0.51 

0.73 

43.43–46.01

116621

10.00 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

46.02–48.04

117223

10.05 

4

12.50 

1.24 

0.22 

−0.03 

0.25 

0.53 

48.05–49.16

116647

10.00 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49 

49.17–50.00

115801

9.93 

5

15.63 

1.57 

0.45 

−0.07 

0.52 

0.49 

Cd (ppm)

1.0000–1.1008

116740

10.01 

3

9.38 

0.94 

−0.07 

0.01 

−0.07 

0.61 

1.1009–1.2239

116647

10.00 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

1.2240–1.3473

116690

10.00 

2

6.25 

0.62 

−0.47 

0.04 

−0.51 

0.73 

1.3474–1.4928

116699

10.00 

3

9.38 

0.94 

−0.07 

0.01 

−0.07 

0.61 

1.4929–1.6538

116626

10.00 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49 

1.6539–1.8480

116640

10.00 

4

12.50 

1.25 

0.22 

−0.03 

0.25 

0.53 

1.8481–1.9829

116621

10.00 

2

6.25 

0.63 

−0.47 

0.04 

−0.51 

0.73 

1.9830–2.2506

116610

10.00 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49 

2.2507–3.2164

116585

9.99 

1

3.13 

0.31 

−1.16 

0.07 

−1.24 

1.02 

3.2165–9.9992

116580

9.99 

4

12.50 

1.25 

0.22 

−0.03 

0.25 

0.53 

Cl− (ppm)

1.0106–2.2074

116644

10.00 

1

3.13 

0.31 

−1.16 

0.07 

−1.24 

1.02 

2.2075–2.4546

116681

10.00 

0

0.00 

0.00 

NaN

0.11 

NaN

NaN

2.4547–2.7386

116654

10.00 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

2.7387–2.9874

116642

10.00 

4

12.50 

1.25 

0.22 

−0.03 

0.25 

0.53 

2.9875–3.2353

116647

10.00 

1

3.13 

0.31 

−1.16 

0.07 

−1.24 

1.02 

3.2354–3.4804

116642

10.00 

7

21.88 

2.19 

0.78 

−0.14 

0.92 

0.43 

3.4805–3.8803

116637

10.00 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49 

3.8804–4.7479

116635

10.00 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49 

4.7480–5.9843

116628

10.00 

2

6.25 

0.63 

−0.47 

0.04 

−0.51 

0.73 

5.9844–27.6669

116628

10.00 

4

12.50 

1.25 

0.22 

−0.03 

0.25 

0.53 

Co (ppb)

1.0000–1.5665

116648

10.00 

4

12.50 

1.25 

0.22 

−0.03 

0.25 

0.53 

1.5666–2.5807

116657

10.00 

1

3.13 

0.31 

−1.16 

0.07 

−1.24 

1.02 

2.5808–1.9789

116722

10.01 

6

18.75 

1.87 

0.63 

−0.10 

0.73 

0.45 

1.9790–3.1012

116636

10.00 

1

3.13 

0.31 

−1.16 

0.07 

−1.24 

1.02 

3.1013–3.3506

116651

10.00 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

3.3507–3.6660

116656

10.00 

2

6.25 

0.62 

−0.47 

0.04 

−0.51 

0.73 

3.6661–3.9952

116621

10.00 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49 

3.9953–4.4250

116620

10.00 

7

21.88 

2.19 

0.78 

−0.14 

0.92 

0.43 

4.4251–5.0758

116620

10.00 

2

6.25 

0.63 

−0.47 

0.04 

−0.51 

0.73 

5.0759–9.9999

116607

10.00 

1

3.13 

0.31 

−1.16 

0.07 

−1.24 

1.02 

Cr (ppb)

1.0000–1.1958 

116649

10.00 

6

18.75 

1.87 

0.63 

−0.10 

0.73 

0.45 

1.1959–1.3244

116645

10.00 

0

0.00 

0.00 

NaN

0.11 

NaN

NaN

1.3245–1.4319

116772

10.01 

2

6.25 

0.62 

−0.47 

0.04 

–0.51 

0.73 

1.4320–1.5656

116663

10.00 

6

18.75 

1.87 

0.63 

−0.10 

0.73 

0.45 

1.5657–1.8305

116650

10.00 

4

12.50 

1.25 

0.22 

−0.03 

0.25 

0.53 

1.8306–2.0343

116653

10.00 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

2.0344–2.3185

116625

10.00 

4

12.50 

1.25 

0.22 

−0.03 

0.25 

0.53 

2.3186–2.7629

116602

10.00 

1

3.13 

0.31 

−1.16 

0.07 

−1.24 

1.02 

2.7630–3.2865

116601

10.00 

6

18.75 

1.88 

0.63 

−0.10 

0.73 

0.45 

3.2866–9.9987

116578

9.99 

0

0.00 

0.00 

NaN

0.11 

NaN

NaN

Cu (ppb)

1.000–2.034

116889

10.02 

1

3.13 

0.31 

−1.17 

0.07 

−1.24 

1.02 

2.035–2.450

116787

10.01 

4

12.50 

1.25 

0.22 

−0.03 

0.25 

0.53 

2.451–2.744

116603

10.00 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49 

2.745–2.994

117174

10.05 

6

18.75 

1.87 

0.62 

−0.10 

0.73 

0.45 

2.995–3.262

116784

10.01 

6

18.75 

1.87 

0.63 

−0.10 

0.73 

0.45 

3.263–3.669

116566

9.99 

2

6.25 

0.63 

−0.47 

0.04 

−0.51 

0.73 

3.670–3.977

116422

9.98 

4

12.50 

1.25 

0.23 

−0.03 

0.25 

0.53 

3.978–4.710

116412

9.98 

2

6.25 

0.63 

−0.47 

0.04 

−0.51 

0.73 

4.711–7.695

116407

9.98 

1

3.13 

0.31 

−1.16 

0.07 

−1.23 

1.02 

7.696–2.9999

116394

9.98 

1

3.13 

0.31 

−1.16 

0.07 

−1.23 

1.02 

F− (ppm)

0.03–0.14

117101

10.04 

6

18.75 

1.87 

0.62 

−0.10 

0.73 

0.45 

0.15–0.15

116775

10.01 

2

6.25 

0.62 

−0.47 

0.04 

−0.51 

0.73 

0.16–0.16

117073

10.04 

3

9.38 

0.93 

−0.07 

0.01 

−0.08 

0.61 

0.17–0.17

117348

10.06 

3

9.38 

0.93 

−0.07 

0.01 

−0.08 

0.61 

0.18–0.18

117148

10.04 

2

6.25 

0.62 

−0.47 

0.04 

−0.52 

0.73 

0.19–0.20

116558

9.99 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49 

0.21–0.22

116117

9.95 

4

12.50 

1.26 

0.23 

−0.03 

0.26 

0.53 

0.23–0.24

116151

9.96 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

0.25–0.28

116321

9.97 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

0.29–1.99

115846

9.93 

1

3.13 

0.31 

−1.16 

0.07 

−1.23 

1.02 

Fe (ppm)

2.00–6.77  

117031

10.03 

2

6.25 

0.62 

−0.47 

0.04 

−0.51 

0.73 

6.78–7.86  

116771

10.01 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49 

7.87–8.88  

116611

10.00 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49 

8.89–9.91  

117384

10.06 

4

12.50 

1.24 

0.22 

−0.03 

0.24 

0.53 

9.92–11.12 

116592

10.00 

6

18.75 

1.88 

0.63 

−0.10 

0.73 

0.45 

11.13–12.99

116876

10.02 

1

3.13 

0.31 

−1.17 

0.07 

−1.24 

1.02 

13.00–15.76

116535

9.99 

2

6.25 

0.63 

−0.47 

0.04 

−0.51 

0.73 

15.77–21.24

116233

9.96 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

1.25–35.77

116234

9.96 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

35.78–99.99

116171

9.96 

1

3.13 

0.31 

−1.16 

0.07 

−1.23 

1.02 

K (ppm)

0.1201–0.3403

116712

10.01 

2

6.25 

0.62 

−0.47 

0.04 

−0.51 

0.73 

0.3404–0.4005

116798

10.01 

1

3.13 

0.31 

−1.16 

0.07 

−1.24 

1.02 

0.4006–0.4634

116644

10.00 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49 

0.4635–0.5461

116707

10.01 

2

6.25 

0.62 

−0.47 

0.04 

−0.51 

0.73 

0.5462–0.6365

116600

10.00 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49 

0.6366–0.7389

116663

10.00 

4

12.50 

1.25 

0.22 

−0.03 

0.25 

0.53 

0.7390–0.8133

116604

10.00 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49 

0.8134–0.9078

116604

10.00 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

0.9079–1.0807

116575

9.99 

2

6.25 

0.63 

−0.47 

0.04 

−0.51 

0.73 

10.808–4.7295

116531

9.99 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

Li (ppb)

1.0000–1.0041

116661

10.00 

6

18.75 

1.87 

0.63 

−0.10 

0.73 

0.45 

1.0042–1.1144

116662

10.00 

10

31.25 

3.12 

1.14 

−0.27 

1.41 

0.38 

1.1145−1.2670

116704

10.01 

4

12.50 

1.25 

0.22 

–0.03 

0.25 

0.53 

1.2671–1.4984

116661

10.00 

0

0.00 

0.00 

NaN

0.11 

NaN

NaN

1.4985–1.9352

116631

10.00 

2

6.25 

0.63 

−0.47 

0.04 

−0.51 

0.73 

1.9353–2.6544

116633

10.00 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

2.6545–3.5996

116624

10.00 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

3.5997–4.7935

116622

10.00 

2

6.25 

0.63 

−0.47 

0.04 

−0.51 

0.73 

4.7936–6.6524

116623

10.00 

1

3.13 

0.31 

−1.16 

0.07 

−1.24 

1.02 

6.6525–9.9999

116617

10.00 

1

3.13 

0.31 

−1.16 

0.07 

−1.24 

1.02 

Mg (ppm)

0.36–1.12

116873

10.02 

0

0.00 

0.00 

NaN

0.11 

NaN

NaN

1.13–2.50

117756

10.10 

8

25.00 

2.48 

0.91 

−0.18 

1.09 

0.41 

2.51–3.04

118493

10.16 

4

12.50 

1.23 

0.21 

−0.03 

0.23 

0.53 

3.05–3.64

117481

10.07 

3

9.38 

0.93 

−0.07 

0.01 

−0.08 

0.61 

3.65–4.41

116189

9.96 

1

3.13 

0.31 

−1.16 

0.07 

−1.23 

1.02 

4.42–5.26

116652

10.00 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49 

5.27–6.18

116279

9.97 

4

12.50 

1.25 

0.23 

−0.03 

0.25 

0.53 

6.19–7.30

115792

9.93 

5

15.63 

1.57 

0.45 

−0.07 

0.52 

0.49 

7.31–9.32

115912

9.94 

1

3.13 

0.31 

−1.16 

0.07 

−1.23 

1.02 

9.33–49.99

115011

9.86 

1

3.13 

0.32 

−1.15 

0.07 

−1.22 

1.02 

Mn (ppb)

1.00–1.26

118658

10.17 

4

12.50 

1.23 

0.21 

−0.03 

0.23 

0.53 

1.27–1.60

117500

10.07 

2

6.25 

0.62 

−0.48 

0.04 

−0.52 

0.73 

1.61–1.90

117854

10.10 

7

21.88 

2.17 

0.77 

−0.14 

0.91 

0.43 

1.91–2.38

118036

10.12 

4

12.50 

1.24 

0.21 

−0.03 

0.24 

0.53 

2.39–3.54

115883

9.93 

2

6.25 

0.63 

−0.46 

0.04 

−0.50 

0.73 

3.55–6.19

115970

9.94 

5

15.63 

1.57 

0.45 

−0.07 

0.52 

0.49 

6.20–11.26

115651

9.91 

1

3.13 

0.32 

−1.15 

0.07 

−1.23 

1.02 

11.27–25.24

115647

9.91 

2

6.25 

0.63 

−0.46 

0.04 

−0.50 

0.73 

25.25–67.60

115630

9.91 

4

12.50 

1.26 

0.23 

−0.03 

0.26 

0.53 

67.61–199.99

115609

9.91 

1

3.13 

0.32 

−1.15 

0.07 

−1.23 

1.02 

Na (ppm)

0.2200–0.5790

116685

10.00 

0

0.00 

0.00 

NaN

0.11 

NaN

NaN

0.5791–0.6504

116721

10.01 

1

3.13 

0.31 

−1.16 

0.07 

−1.24 

1.02 

0.6505–0.6959

116839

10.02 

3

9.38 

0.94 

−0.07 

0.01 

−0.07 

0.61 

0.6960–0.7287

116664

10.00 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

0.7288–0.7844

116629

10.00 

8

25.00 

2.50 

0.92 

−0.18 

1.10 

0.41 

0.7845–0.8366

116622

10.00 

2

6.25 

0.63 

−0.47 

0.04 

−0.51 

0.73 

0.8367–0.8943

116676

10.00 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

0.8944–0.9611

116614

10.00 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49 

0.9612–1.1210

116524

9.99 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

1.1211–4.1488

116464

9.98 

4

12.50 

1.25 

0.22 

−0.03 

0.25 

0.53 

Ni (ppb)

1.0001–5.3709

116644

10.00 

2

6.25 

0.62 

−0.47 

0.04 

−0.51 

0.73 

5.3710–8.8292

116646

10.00 

4

12.50 

1.25 

0.22 

−0.03 

0.25 

0.53 

8.8293–10.4420

116644

10.00 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

10.4421–11.6711

116651

10.00 

6

18.75 

1.87 

0.63 

−0.10 

0.73 

0.45 

11.6712–12.7538

116655

10.00 

1

3.13 

0.31 

−1.16 

0.07 

−1.24 

1.02 

12.7539–13.9820

116648

10.00 

2

6.25 

0.62 

−0.47 

0.04 

−0.51 

0.73 

13.9821–14.9556

116644

10.00 

1

3.13 

0.31 

−1.16 

0.07 

−1.24 

1.02 

14.9557–15.9219

116646

10.00 

1

3.13 

0.31 

−1.16 

0.07 

−1.24 

1.02 

15.9220–16.7251

116633

10.00 

7

21.88 

2.19 

0.78 

−0.14 

0.92 

0.43 

16.7252–19.9999

116627

10.00 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49 

Pb (ppb)

1.00–8.76

116772

10.01 

2

6.25 

0.62 

−0.47 

0.04 

−0.51 

0.73 

8.77–17.68

116678

10.00 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49 

17.69–21.65

116889

10.02 

0

0.00 

0.00 

NaN

0.11 

NaN

NaN

21.66–24.56

117006

10.03 

4

12.50 

1.25 

0.22 

−0.03 

0.25 

0.53 

24.57–27.30

116743

10.01 

4

12.50 

1.25 

0.22 

−0.03 

0.25 

0.53 

27.31–30.38

116786

10.01 

2

6.25 

0.62 

−0.47 

0.04 

−0.51 

0.73 

30.39–33.10

116634

10.00 

1

3.13 

0.31 

−1.16 

0.07 

−1.24 

1.02 

33.11–36.51

116709

10.01 

4

12.50 

1.25 

0.22 

−0.03 

0.25 

0.53 

36.52–39.37

116345

9.97 

5

15.63 

1.57 

0.45 

−0.06 

0.51 

0.49 

39.38–49.99

115876

9.93 

5

15.63 

1.57 

0.45 

−0.07 

0.52 

0.49 

Si (ppm)

10.801–16.979

116655

10.00 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

16.980–18.317

116728

10.01 

0

0.00 

0.00 

NaN

0.11 

NaN

NaN

18.318–19.271

116675

10.00 

2

6.25 

0.62 

−0.47 

0.04 

−0.51 

0.73 

19.272–20.521

116693

10.00 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49 

20.522–21.914

116619

10.00 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49 

21.915–23.443

116686

10.00 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

23.444–25.021

116607

10.00 

1

3.13 

0.31 

−1.16 

0.07 

−1.24 

1.02 

25.022–27.559

116627

10.00 

4

12.50 

1.25 

0.22 

−0.03 

0.25 

0.53 

27.560–31.012

116583

9.99 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

31.013–96.079

116565

9.99 

6

18.75 

1.88 

0.63 

−0.10 

0.73 

0.45 

Sr (ppb)

8.00–20.48

116702

10.00 

3

9.38 

0.94 

−0.07 

0.01 

−0.07 

0.61 

20.49–42.65

116644

10.00 

6

18.75 

1.87 

0.63 

−0.10 

0.73 

0.45 

42.66–57.42

116749

10.01 

1

3.13 

0.31 

−1.16 

0.07 

−1.24 

1.02 

57.43–66.48

116649

10.00 

2

6.25 

0.62 

−0.47 

0.04 

−0.51 

0.73 

66.49–71.81

116821

10.02 

2

6.25 

0.62 

−0.47 

0.04 

−0.51 

0.73 

71.82–76.94

116630

10.00 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

76.95–84.38

116686

10.00 

7

21.88 

2.19 

0.78 

−0.14 

0.92 

0.43 

84.39–96.47

116540

9.99 

4

12.50 

1.25 

0.22 

−0.03 

0.25 

0.53 

96.48–134.78

116509

9.99 

4

12.50 

1.25 

0.22 

−0.03 

0.25 

0.53 

134.79–499.92

116508

9.99 

0

0.00 

0.00 

NaN

0.11 

NaN

NaN

V (ppb)

10.000–10.001

116806

10.01 

4

12.50 

1.25 

0.22 

−0.03 

0.25 

0.53 

10.002–10.320

116672

10.00 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49 

10.321–10.744

116623

10.00 

4

12.50 

1.25 

0.22 

−0.03 

0.25 

0.53 

10.745–11.616

116648

10.00 

1

3.13 

0.31 

−1.16 

0.07 

−1.24 

1.02 

11.617–12.435

116656

10.00 

4

12.50 

1.25 

0.22 

−0.03 

0.25 

0.53 

12.436–14.190

116633

10.00 

4

12.50 

1.25 

0.22 

−0.03 

0.25 

0.53 

14.191–15.335

116625

10.00 

1

3.13 

0.31 

−1.16 

0.07 

−1.24 

1.02 

15.336–17.900

116593

10.00 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49 

17.901–20.623

116598

10.00 

4

12.50 

1.25 

0.22 

−0.03 

0.25 

0.53 

20.624–99.985

116584

9.99 

0

0.00 

0.00 

NaN

0.11 

NaN

NaN

W (ppb)

1.000–2.152

116858

10.02 

1

3.13 

0.31 

−1.16 

0.07 

−1.24 

1.02 

2.153–2.458

116646

10.00 

2

6.25 

0.62 

−0.47 

0.04 

−0.51 

0.73 

2.459–2.683

116776

10.01 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49 

2.684–2.988

116706

10.01 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49 

2.989–3.363

116762

10.01 

0

0.00 

0.00 

NaN

0.11 

NaN

NaN

3.364–4.015

116577

9.99 

5

15.63 

1.56 

0.45 

−0.06 

0.51 

0.49 

4.016–4.478

116788

10.01 

4

12.50 

1.25 

0.22 

−0.03 

0.25 

0.53 

4.479–4.946

116606

10.00 

6

18.75 

1.88 

0.63 

−0.10 

0.73 

0.45 

4.947–6.530

116366

9.98 

4

12.50 

1.25 

0.23 

−0.03 

0.25 

0.53 

6.531–49.994

116353

9.98 

0

0.00 

0.00 

NaN

0.11 

NaN

NaN

Zn (ppb)

1.00–3.28

117143

10.04 

4

12.50 

1.24 

0.22 

−0.03 

0.25 

0.53 

3.29–4.34

117519

10.08 

3

9.38 

0.93 

−0.07 

0.01 

−0.08 

0.61 

4.35–5.21

117200

10.05 

1

3.13 

0.31 

−1.17 

0.07 

−1.24 

1.02 

5.22–6.13

116683

10.00 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

6.14–7.22

116931

10.02 

3

9.38 

0.94 

−0.07 

0.01 

−0.07 

0.61 

7.23–8.81

116420

9.98 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

8.82–11.02

116562

9.99 

2

6.25 

0.63 

−0.47 

0.04 

−0.51 

0.73 

11.03–13.62

116052

9.95 

3

9.38 

0.94 

−0.06 

0.01 

−0.07 

0.61 

13.63–21.96

115998

9.94 

4

12.50 

1.26 

0.23 

−0.03 

0.26 

0.53 

21.97–49.99

115930

9.94 

6

18.75 

1.89 

0.63 

−0.10 

0.74 

0.45 

Magnetic anomaly (nT)

−145 to 101

128137

10.99 

3

9.38 

0.85 

−0.16 

0.02 

−0.18 

0.61 

−100 to 92

121586

10.42 

4

12.50 

1.20 

0.18 

−0.02 

0.21 

0.53 

−91 to 83

118890

10.19 

6

18.75 

1.84 

0.61 

−0.10 

0.71 

0.45 

−82 to 76

131697

11.29 

4

12.50 

1.11 

0.10 

−0.01 

0.12 

0.53 

−75 to 68

118478

10.16 

3

9.38 

0.92 

−0.08 

0.01 

−0.09 

0.61 

−67 to 59

115975

9.94 

4

12.50 

1.26 

0.23 

−0.03 

0.26 

0.53 

−58 to 49

115502

9.90 

0

0.00 

0.00 

NaN

0.10 

NaN

NaN

−48 to 32

110107

9.44 

4

12.50 

1.32 

0.28 

−0.03 

0.32 

0.53 

−31 to 9

105926

9.08 

2

6.25 

0.69 

−0.37 

0.03 

−0.40 

0.73 

−8 to 153

100140

8.59 

2

6.25 

0.73 

−0.32 

0.03 

−0.34 

0.73 

Distance from fault (m)

0–120

119087

10.21 

0

0.00 

0.00 

NaN

0.11 

NaN

NaN

123–256

118526

10.16 

4

12.50 

1.23 

0.21 

−0.03 

0.23 

0.53 

258–408

118732

10.18 

3

9.38 

0.92 

−0.08 

0.01 

−0.09 

0.61 

416–577

117138

10.04 

7

21.88 

2.18 

0.78 

−0.14 

0.92 

0.43 

579–771

115748

9.92 

5

15.63 

1.57 

0.45 

−0.07 

0.52 

0.49 

774–993

115764

9.92 

2

6.25 

0.63 

−0.46 

0.04 

−0.50 

0.73 

994–1268

115499

9.90 

3

9.38 

0.95 

−0.05 

0.01 

−0.06 

0.61 

1271–1632

115411

9.89 

6

18.75 

1.90 

0.64 

−0.10 

0.74 

0.45 

1633–2292

115313

9.89 

0

0.00 

0.00 

NaN

0.10 

NaN

NaN

2294–6224

115220

9.88 

2

6.25 

0.63 

−0.46 

0.04 

−0.50 

0.73 

Lithology

Ogl

1064

0.09

0

0.00 

0.00 

NaN

0.00 

NaN

NaN

lgr

4841

0.42

0

0.00 

0.00 

NaN

0.00 

NaN

NaN

Di

14

0.00

0

0.00 

0.00 

NaN

0.00 

NaN

NaN

Hagr

245

0.02

0

0.00 

0.00 

NaN

0.00 

NaN

NaN

Hb

2281

0.20

2

6.25 

31.96 

3.46 

−0.06 

3.53 

4.83 

Oyb

1022

0.09

0

0.00 

0.00 

NaN

0.00 

NaN

NaN

Qr

49757

4.27

2

6.25 

1.47 

0.38 

−0.02 

0.40 

0.55 

Qd

533

0.05

0

0.00 

0.00 

NaN

0.00 

NaN

NaN

Kad

136

0.01

0

0.00 

0.00 

NaN

0.00 

NaN

NaN

Kbd

881

0.08

1

3.13 

41.37 

3.72 

−0.03 

3.75 

3.69 

Kfl

3

0.00

0

0.00 

0.00 

NaN

0.00 

NaN

NaN

Kgp

359

0.03

0

0.00 

0.00 

NaN

0.00 

NaN

NaN

Kh

262

0.02

0

0.00 

0.00 

NaN

0.00 

NaN

NaN

Kj

792

0.07

0

0.00 

0.00 

NaN

0.00 

NaN

NaN

Kqp

520

0.04

0

0.00 

0.00 

NaN

0.00 

NaN

NaN

Ksgr

9862

0.85

0

0.00 

0.00 

NaN

0.01 

NaN

NaN

Jigr

19233

1.65

0

0.00 

0.00 

NaN

0.02 

NaN

NaN

Jgr

3466

0.30

0

0.00 

0.00 

NaN

0.00 

NaN

NaN

Jbs

584

0.05

0

0.00 

0.00 

NaN

0.00 

NaN

NaN

Jbc

3969

0.34

0

0.00 

0.00 

NaN

0.00 

NaN

NaN

TRn

20281

1.74

0

0.00 

0.00 

NaN

0.02 

NaN

NaN

TRn1

20837

1.79

0

0.00 

0.00 

NaN

0.02 

NaN

NaN

TRn2

12158

1.04

0

0.00 

0.00 

NaN

0.01 

NaN

NaN

TRn3

6944

0.60

0

0.00 

0.00 

NaN

0.01 

NaN

NaN

TRg

53754

4.61

0

0.00 

0.00 

NaN

0.05 

NaN

NaN

Ps

18150

1.56

0

0.00 

0.00 

NaN

0.02 

NaN

NaN

Ch

69942

6.00

0

0.00 

0.00 

NaN

0.06 

NaN

NaN

Oj

78322

6.71

1

3.13 

0.47 

−0.76 

0.04 

−0.80 

−0.79 

Omg

215666

18.49

8

25.00 

1.35 

0.30 

−0.08 

0.38 

0.94 

Odu

89243

7.65

4

12.50 

1.63 

0.49 

−0.05 

0.54 

1.02 

Od

6794

0.58

0

0.00 

0.00 

NaN

0.01 

NaN

NaN

CEw

129104

11.07

3

9.38 

0.85 

−0.17 

0.02 

−0.18 

−0.30 

CEp

112818

9.67

5

15.63 

1.62 

0.48 

−0.07 

0.55 

1.13 

CEm

58514

5.02

2

6.25 

1.25 

0.22 

−0.01 

0.23 

0.32 

CEj

17535

1.50

0

0.00 

0.00 

NaN

0.02 

NaN

NaN

PCEt

103955

8.91

2

6.25 

0.70 

−0.35 

0.03 

−0.38 

−0.53 

Jugr

52597

4.51

2

6.25 

1.39 

0.33 

−0.02 

0.34 

0.47 

  1. aUsing the quantile classification method.
  2. bLikelihood ratio.

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Oh, HJ., Lee, S. Application of Artificial Neural Network for Gold–Silver Deposits Potential Mapping: A Case Study of Korea. Nat Resour Res 19, 103–124 (2010). https://doi.org/10.1007/s11053-010-9112-2

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  • DOI: https://doi.org/10.1007/s11053-010-9112-2

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