Bibliographic Details
| Title: |
Data-driven and knowledge-driven MARCOS method to Cu porphyry prospectivity modelling, a case study, Shahr-e-Babak area, southeastern Iran54. |
| Alternate Title: |
Modeliranje potencijala Cu porfirita korištenjem MARCOS metode vođene podatcima i znanjem, studija slučaja, područje Shahr-e-Babak, jugoistočni Iran. |
| Authors: |
Jahantigh, Moslem1, Ramazi, Hamidreza1 ramazi@aut.ac.ir |
| Source: |
Rudarsko-Geološko-Naftni Zbornik. 2026, Vol. 41 Issue 3, p37-54. 18p. |
| Subject Terms: |
*Multiple criteria decision making, *Prospecting, *Physiographic provinces |
| Geographic Terms: |
Iran |
| Abstract (English): |
The present study aims to compare the performance of data-driven and knowledge-driven Multi-Criteria Decision-Making (MCDM) in producing a mineral potential model in the Shahr-e-Babak study area in south-eastern Iran. To achieve this goal, eight evidential layers, including geological, Cu signature, principal component analysis, argillaceous alteration, phyllic alteration, iron oxide alteration (Gossan), airborne geophysics layers, and linear structures, were preprocessed and produced. To produce the optimal model, first, all layers were scaled and shifted to the zero to one interval. To create the mineral potential model in the area, the Measurement Alternatives and Ranking according to Compromise Solution (MARCOS) method was introduced. For the exploration control layer weighting, two methods were used: the area prediction rate (P-A) method and the Analytic Hierarchy Process (AHP) method. Then, the results were compared with the Multi-Objective Optimization by Ratio Analysis (MOORA) method, which is a proven method in mineral potential assessment. To compare these methods, two methods - area prediction rate and the area under the curve (AUC) - were used. The findings show that the data-driven MARCOS approach provides the best performance and displays the best mineral potential model. The normalized density for the data-driven MARCOS, data-driven MOORA, knowledge-driven MARCOS, and knowledge-driven MOORA methods is equal to 3.00, 2.84, 2.7, and 2.57, respectively. The AUC for the data-driven MARCOS, data-driven MOORA, knowledge-driven MARCOS, and knowledge-driven MOORA methods is equal to 0.939, 0.938, 0.933, and 0.932, respectively. [ABSTRACT FROM AUTHOR] |
| Abstract (Bosnian): |
Cilj je ove studije usporediti učinkovitost višekriterijskoga odlučivanja (MCDM) vođenoga podatcima i znanjem u izradi modela mineralnoga potencijala u području istraživanja Shahr-e-Babak u jugoistočnome Iranu. Kako bi se postigao taj cilj, obrađeno je i izrađeno osam slojeva, uključujući geološku podlogu, sadržaj bakra, analizu glavnih komponenti, argilične alteracije, hidrotermalne alteracije, alteracije željezovih oksida (Gossan), geofizičke podatke i linearne strukture. Za izradu optimalnoga modela prvo su svi slojevi skalirani i pomaknuti na interval od nule do jedan. Za izradu modela mineralnoga potencijala u istraživanome području uvedena je MARCOS metoda (alternativna mjerenja i rangiranje prema kompromisnome rješenju). Za ponderiranje kontrolnoga sloja korištene su dvije metode: metoda stope predviđanja površine (P-A) i metoda analitičkoga hijerarhijskog procesa (AHP). Zatim su rezultati uspoređeni s metodom višekriterijske optimizacije analizom omjera (MOORA) koja je dokazanu u procjeni mineralnoga potencijala. Za usporedbu ovih metoda korištene su dvije metode - stopa predviđanja površine i površina ispod krivulje (AUC). Rezultati su pokazali da MARCOS pristup vođen podatcima pruža najbolje performanse i prikazuje najbolji model mineralnoga potencijala. Normalizirana gustoća za MARCOS metodu vođenu podatcima, MOORA metodu vođenu podatcima, MARCOS metodu vođenu znanjem te MOORA metodu vođenu znanjem jednaka je 3,00, 2,84, 2,7 i 2,57, dok su AUC vrijednosti iznosile 0,939, 0,938, 0,933 i 0,932. [ABSTRACT FROM AUTHOR] |
| Database: |
Energy & Power Source |
