Recent studies have pointed out that the widespread iron deposits in southwestern Fujian metallogenic belt(SFMB)(China) are skarn-type deposits associated with the Yanshanian granites. There is still excellent potenti...Recent studies have pointed out that the widespread iron deposits in southwestern Fujian metallogenic belt(SFMB)(China) are skarn-type deposits associated with the Yanshanian granites. There is still excellent potential for mineral exploration because large areas in this belt are covered by forest. A new predictive model for mapping skarn-type Fe deposit prospectivity in this belt was developed and focused on in this study, using five criteria as evidence:(1) the contact zones of Yanshanian granites(GRANITE);(2) the contact zones within the late Paleozoic marine sedimentary rocks and the carbonate formations(FORMATION);(3) the NE-NNE-trending faults(FAULT);(4) the zones of skarn alterations(SKARN); and(5) the aeromagnetic anomaly(AEROMAGNETIC). The fuzzy weights of evidence(FWof E) method, developed from the classical weights of evidence(Wof E) and based on fuzzy sets and fuzzy probabilities, could provide smaller variances and more accurate posterior probabilities and could effectively minimize the uncertainty caused by omitted or wrongly assigned data and be more flexible than the Wof E. It is an efficient and widely used method for mineral potential mapping. Random forests(RF) is a new and useful method for data-driven predictive mapping of mineral prospectivity method, and needs further scrutiny. Both prospectivity results respectively using the FWof E and RF methods reveal that the prediction model for the skarn-type Fe deposits in the SFMB is successful and efficient. Both methods suggested that the GRANITE and FORMATION are the most valuable evidence maps, followed by SKARN, AEROMAGNETIC, and FAULT. This is coincident with the skarn-type Fe deposit mineral model in the SFMB. The unstable performance experienced when FORMATION was omitted might indicate that the highest uncertainty and risk in follow-up exploration is related to the sequences. In addition, the performance of the RF method for the skarn-type Fe deposits prospectivity in the SFMB is better than the FWof E; therefore, it could be used to guide further exploration of skarn-type Fe prospects in the SFMB.展开更多
基金the joint financial support from a research project on "Quantitative models for prediction of strategic mineral resources in China" (Grant No. 201211022) by China Geological Surveythe National Natural Science Foundation of China (Grant Nos. 41372007, 41430320 & 41522206)the Program for New Century Excellent Talents in University (Grant No. NCET-13-1016)
文摘Recent studies have pointed out that the widespread iron deposits in southwestern Fujian metallogenic belt(SFMB)(China) are skarn-type deposits associated with the Yanshanian granites. There is still excellent potential for mineral exploration because large areas in this belt are covered by forest. A new predictive model for mapping skarn-type Fe deposit prospectivity in this belt was developed and focused on in this study, using five criteria as evidence:(1) the contact zones of Yanshanian granites(GRANITE);(2) the contact zones within the late Paleozoic marine sedimentary rocks and the carbonate formations(FORMATION);(3) the NE-NNE-trending faults(FAULT);(4) the zones of skarn alterations(SKARN); and(5) the aeromagnetic anomaly(AEROMAGNETIC). The fuzzy weights of evidence(FWof E) method, developed from the classical weights of evidence(Wof E) and based on fuzzy sets and fuzzy probabilities, could provide smaller variances and more accurate posterior probabilities and could effectively minimize the uncertainty caused by omitted or wrongly assigned data and be more flexible than the Wof E. It is an efficient and widely used method for mineral potential mapping. Random forests(RF) is a new and useful method for data-driven predictive mapping of mineral prospectivity method, and needs further scrutiny. Both prospectivity results respectively using the FWof E and RF methods reveal that the prediction model for the skarn-type Fe deposits in the SFMB is successful and efficient. Both methods suggested that the GRANITE and FORMATION are the most valuable evidence maps, followed by SKARN, AEROMAGNETIC, and FAULT. This is coincident with the skarn-type Fe deposit mineral model in the SFMB. The unstable performance experienced when FORMATION was omitted might indicate that the highest uncertainty and risk in follow-up exploration is related to the sequences. In addition, the performance of the RF method for the skarn-type Fe deposits prospectivity in the SFMB is better than the FWof E; therefore, it could be used to guide further exploration of skarn-type Fe prospects in the SFMB.
