Assigning realistic weights to targeting criteria in order to synthesize various geo-spatial datasets is one of the most important challenging tasks for mineral prospectivity modeling (MPM). Techniques for multiple criteria decision-making (MCDM), like MPM, are deeply concerned with combining a large-scale exploration dataset into a single evaluation model for localizing prospects of a certain deposit type. In this paper, we develop the data-driven TOPSIS procedure, as a GIS-based MCDM technique for MPM. Because weighting and integrating various exploration evidence layers are influenced by intricacy and vagueness of ore mineralization process, imprecise selection of targeting criteria may reduce the possibility of exploration success. To address this problem, we applied prediction–area plot for prioritizing, recognizing and weighting efficient and inefficient targeting criteria. In addition, normalized density (Nd) index was then used for assigning significant weights to fractal-based discretized classes of each targeting criterion. After recognition of efficient and inefficient targeting criteria, data-driven TOPSIS procedure was adapted based on participation of only efficient targeting criteria as well as all targeting criteria for porphyry-Cu prospectivity in Varzaghan district, NW Iran. For quantitative assessment, a success rate curve for each of the two prospectivity models generated in this study was drawn. The results prove the superiority of the predictive model based on using efficient targeting criteria.
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The authors are grateful to the Associate Editor and two anonymous reviewers for their constructive comments/edits, which considerably improved this paper. The senior author is greatly indebted to Mr. Daviran for his generous assistance in the preparation of paper maps.
Appendix: Software Procedure for Data-Driven TOPSIS Prospectivity Modeling
Appendix: Software Procedure for Data-Driven TOPSIS Prospectivity Modeling
A MATLAB-based program was used for calculation of data-driven TOPSIS prospectivity scores (M + j ) and the full coding is available as follows:
Initially, the logistically transformed continuous raster maps of targeting criteria (evidential layers of geology, geochemistry, geophysics and remote sensing) must be converted to shape points with spatial coordinates [X (easting) and Y (northing)] using ArcGIS software.
Discretization of continuous values of different evidential layers should be performed using appropriate classification methods (e.g., C–A fractal model) in order to obtain significant thresholds.
In this step, based on the discretized classes and known mineral occurrences, the prediction–area (P–A) plots are drawn. The intersection point of each plot is used for assigning weight (We) to the corresponding targeting criterion using prediction rate (Pr) and occupied area (Oa). By this, the significant (We> 0) and non-significant (We< 0) targeting criteria are identified.
Then, normalized density index (Nd) is applied to calculate normalized weights (Nw) of discretized classes per criterion.
Construct a decision matrix (X = (Xij)n*m) in which the columns exhibit the targeting criteria, while the rows exhibit the options. In this step, the derived weights of P–A plots (We) in step 3 are assigned to each criterion. In addition, the calculated Nw values for different classes of each criterion in step 4 are replaced by original values of pixels in accordance with the obtained thresholds in step 2.
A MATLAB-based program of TOPSIS is codded and implemented for ranking and weighting of the options. Thus, data-driven TOPSIS prospectivity scores (M + j ) are calculated as:
Exporting X, Y and M + j vectors from MATLAB workspace into an excel sheet and then converting the excel sheet to a shape file in the ArcGIS software. The shape file must then be converted to a raster grid with appropriate cell size. This raster map is the final prospectivity model of data-driven TOPSIS.
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Ghezelbash, R., Maghsoudi, A. & Carranza, E.J.M. An Improved Data-Driven Multiple Criteria Decision-Making Procedure for Spatial Modeling of Mineral Prospectivity: Adaption of Prediction–Area Plot and Logistic Functions. Nat Resour Res 28, 1299–1316 (2019). https://doi.org/10.1007/s11053-018-9448-6
- Data-driven TOPSIS
- C–A fractal
- P–A plot
- Normalized density
- Success rate curve