Skip to main content

Advertisement

SpringerLink
Log in

Application of the most competent knowledge-driven integration method for deposit-scale studies

  • Original Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

Abstract

The capability of a famous multi criteria decision making (MCDM) technique, which is called “the technique for order preference by similarity to ideal solution” (TOPSIS), was considered in this research for better and more precise evaluation porphyry copper-molybdenum deposit in deposit-scale studies. Various evidence layers are usually raster-based maps comprising geological, geochemical, and geophysical which are integrated to supply a mineral prospectivity mapping (MPM). Ten evidence layers with previous boreholes of the Zafarqand copper-molybdenum deposit (located in centeral region of the Urumieh-Dokhtar volcanic arc in Iran) are employed to explore the regions of interest as detailed entirely. TOPSIS can investigate a region of interest more exact than other knowledge-driven integration methods and so can save a lot money in deposit-scale studies due to using numerous alternative points. The final detailed map is produced by TOPSIS technique and the outputs are validated through comparison to field reconnaissance and twenty-four boreholes that have been categorized as three classes. DCCR value was 87.5% that demonstrates high level of statistical confidence. This proposed method established which has a high performance in procuring the final detailed map due to can avoid abortive additional drilling in a study area. In this regard, deposit-scale studies always require a reliable MPM for ultimate evaluations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Agterberg FP, Bonham-Carter GF (2005) Measuring the performance of mineral-potential maps. Nat Resour Res 14(1):1–17

    Article  Google Scholar 

  • An P, Moon W, Rencz A (1991) Integration of geological, geophysical, and remote sensing data using fuzzy set theory. Can J Explor Geophys 27(1):1–11

    Google Scholar 

  • Ataei M, Sereshki F, Jamshidi M, Jalali S (2008) Suitable mining method for Golbini No. 8 deposit in Jajarm (Iran) using TOPSIS method. Min Technol 117(1):1–5

    Article  Google Scholar 

  • Bonham-Carter GF (2014) Geographic information systems for geoscientists: modelling with GIS. Elsevier

    Google Scholar 

  • Carranza EJM (2008) Geochemical anomaly and mineral prospectivity mapping in GIS. Elsevier

    Google Scholar 

  • Carranza EJM (2009) Controls on mineral deposit occurrence inferred from analysis of their spatial pattern and spatial association with geological features. Ore Geol Rev 35(3–4):383–400

    Article  Google Scholar 

  • Carranza EJM, Hale M (2002) Where are porphyry copper deposits spatially localized? A case study in Benguet province, Philippines. Nat Resour Res 11(1):45–59

    Article  Google Scholar 

  • Carranza EJM, 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

    Article  Google Scholar 

  • Carranza EJM, Sadeghi M (2010) Predictive mapping of prospectivity and quantitative estimation of undiscovered VMS deposits in Skellefte district (Sweden). Ore Geol Rev 38(3):219–241

    Article  Google Scholar 

  • Carranza E, Woldai T, Chikambwe E (2005) Application of data-driven evidential belief functions to prospectivity mapping for aquamarine-bearing pegmatites, Lundazi district, Zambia. Nat Resour Res 14(1):47–63

    Article  Google Scholar 

  • Carranza E, Hale M, Faassen C (2008) Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity mapping. Ore Geol Rev 33(3–4):536–558

    Article  Google Scholar 

  • Cheng S, Chan CW, Huang GH (2002) Using multiple criteria decision analysis for supporting decisions of solid waste management. J Environ Sci Health, Part A 37(6):975–990

    Article  Google Scholar 

  • Chung C-JF, Moon WM (1991) Combination rules of spatial geoscience data for mineral exploration. Geoinformatics 2(2):159–169

    Article  Google Scholar 

  • Dağdeviren M, Yavuz S, Kılınç N (2009) Weapon selection using the AHP and TOPSIS methods under fuzzy environment. Expert Syst Appl 36(4):8143–8151

    Article  Google Scholar 

  • Ford A, Miller JM, Mol AG (2016) A comparative analysis of weights of evidence, evidential belief functions, and fuzzy logic for mineral potential mapping using incomplete data at the scale of investigation. Nat Resour Res 25(1):19–33

    Article  Google Scholar 

  • Gabr S, Ghulam A, Kusky T (2010) Detecting areas of high-potential gold mineralization using ASTER data. Ore Geol Rev 38(1):59–69

    Article  Google Scholar 

  • Lee S, Hwang J, Park I (2013) Application of data-driven evidential belief functions to landslide susceptibility mapping in Jinbu, Korea. CATENA 100:15–30

    Article  Google Scholar 

  • Mergias I, Moustakas K, Papadopoulos A, Loizidou M (2007) Multi-criteria decision aid approach for the selection of the best compromise management scheme for ELVs: The case of Cyprus. J Hazard Mater 147(3):706–717

    Article  Google Scholar 

  • Mirzaei M, Afzal P, Adib A, Khalajmasoumi M, Zarifi AZ (2014) Prospection of iron and manganese using index overlay. and fuzzy logic methods in balvard 1: 100,000 sheet, southeastern Iran. Iran J Earth Sci 6:1–11

    Google Scholar 

  • Moon WM (1990) Integration of geophysical and geological data using evidential belief function. IEEE Trans Geosci Remote Sens 28(4):711–720

    Article  Google Scholar 

  • Moradi M, Basiri S, Kananian A, Kabiri K (2015) Fuzzy logic modeling for hydrothermal gold mineralization mapping using geochemical, geological, ASTER imageries and other geo-data, a case study in Central Alborz, Iran. Earth Sci Inform 8(1):197–205

    Article  Google Scholar 

  • Nabighian MN (1972) The analytic signal of two-dimensional magnetic bodies with polygonal cross-section: its properties and use for automated anomaly interpretation. Geophysics 37(3):507–517

    Article  Google Scholar 

  • Nabighian MN (1974) Additional comments on the analytic signal of two-dimensional magnetic bodies with polygonal cross-section. Geophysics 39(1):85–92

    Article  Google Scholar 

  • Nabighian MN (1984) Toward a three-dimensional automatic interpretation of potential field data via generalized Hilbert transforms: Fundamental relations. Geophysics 49(6):780–786

    Article  Google Scholar 

  • Nykänen V, Salmirinne H (2007) Prospectivity analysis of gold using regional geophysical and geochemical data from the Central Lapland Greenstone Belt, Finland. Geological Survey of Finland 44:251–269

    Google Scholar 

  • Opricovic S, Tzeng G-H (2004) Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur J Oper Res 156(2):445–455

    Article  Google Scholar 

  • Porwal A, Carranza E, Hale M (2004) A hybrid neuro-fuzzy model for mineral potential mapping. Math Geol 36(7):803–826

    Article  Google Scholar 

  • Sabbaghi H (2018) A combinative technique to recognise and discriminate turquoise stone. Vib Spectrosc 99:93–99

    Article  Google Scholar 

  • Sabbaghi H, Moradzadeh A (2018) ASTER spectral analysis for host rock associated with porphyry copper-molybdenum mineralization. J Geol Soc India 91(5):627–638

    Article  Google Scholar 

  • Sabbaghi H, Tabatabaei SH (2020) A combinative knowledge-driven integration method for integrating geophysical layers with geological and geochemical datasets. J Appl Geophys 172:103915

    Article  Google Scholar 

  • Samimi Namin F, Shahriar K, Ataee-Pour M, Dehghani H (2008) A new model for mining method selection of mineral deposit based on fuzzy decision making. J South Afr Inst Min Metall 108(7):385–395

    Google Scholar 

  • Singer DA, Kouda R (1996) Application of a feedforward neural network in the search for Kuroko deposits in the Hokuroku district, Japan. Math Geol 28(8):1017–1023

    Article  Google Scholar 

  • Tavana M, Hatami-Marbini A (2011) A group AHP-TOPSIS framework for human spaceflight mission planning at NASA. Expert Syst Appl 38(11):13588–13603

    Google Scholar 

  • Yousefi M, Carranza EJM (2015a) Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping. Comput Geosci 74:97–109

    Article  Google Scholar 

  • Yousefi M, Carranza EJM (2015b) Prediction–area (P–A) plot and C–a fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling. Comput Geosci 79:69–81

    Article  Google Scholar 

  • Yousefi M, Carranza EJM (2015c) Geometric average of spatial evidence data layers: a GIS-based multi-criteria decision-making approach to mineral prospectivity mapping. Comput Geosci 83:72–79

    Article  Google Scholar 

  • Yousefi M, Nykänen V (2016) Data-driven logistic-based weighting of geochemical and geological evidence layers in mineral prospectivity mapping. J Geochem Explor 164:94–106

    Article  Google Scholar 

  • Agterberg F, Bonham-Carter G (1999) Logistic regression and weights of evidence modeling in mineral exploration, p. 490

  • Bonham-Carter GF (1989) Weights of evidence modeling: a new approach to mapping mineral potential. Statistical applications in the earth sciences, 171–183

  • Bonham-Carter, G.F. (1994) Geographic information systems for geoscientists-modeling with GIS. Computer methods in the geoscientists 13, 398

  • Chen S-J, Hwang C-L (1992) Fuzzy multiple attribute decision making, pp. 289–486, Springer

  • Elyasi G (2009) Mineral potential mapping in detailed stage using GIS in one of exploration prospects of Kerman Province. Master of Science Thesis, University of Tehran (published in Persian)

  • Hwang C-L, Yoon K (1981) Multiple attribute decision making, pp. 58–191, Springer

  • Pan G, Harris DP (2000) Information synthesis for mineral exploration (spatial information systems)

  • Porwal A, Carranza E, Hale M (2003) Artificial neural networks for mineral-potential mapping: a case study from Aravalli Province, Western India. Nat Resour Res 12(3), 155-171

Download references

Acknowledgements

The authors would like to thank the Aflak Geological Survey and Mineral Exploration Company for providing borehole samples and laboratory studies. Many thanks for The Land Processes Distributed Active Archive Center (LPDAAC) at NASA for providing ASTER data and National Iranian Copper Industries Company (NICIC) to assist us in this work.

Author information

Authors and Affiliations

  1. Department of Mining Engineering, Isfahan University of Technology, Isfahan, 84156883111, Iran

    Hamid Sabbaghi & Seyed Hassan Tabatabaei

Authors
  1. Hamid Sabbaghi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seyed Hassan Tabatabaei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hamid Sabbaghi.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Responsible editor: Narasimman Sundararajan

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sabbaghi, H., Tabatabaei, S.H. Application of the most competent knowledge-driven integration method for deposit-scale studies. Arab J Geosci 15, 1057 (2022). https://doi.org/10.1007/s12517-022-10217-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12517-022-10217-z

Keywords

Search

Navigation