Prospectivity modeling of porphyry-Cu deposits by identification and integration of efficient mono-elemental geochemical signatures. (February 2016)

• Record Type: Journal Article
• Authors: Parsa, Mohammad; Maghsoudi, Abbas; Yousefi, Mahyar; Sadeghi, Martiya
• Is Part Of: Journal of African earth sciences. Volume 114(2016:Feb.)
• Page Start: 228
• Page End: 241
• Publication Date: 2016-02
• Subjects: Mono-elemental geochemical signatures -- Prediction-area (P-A) plot -- Success-rate curve -- Normalized density -- Stream sediment data -- Prospectivity modeling -- Exploration success; Earth sciences -- Africa -- Periodicals; Earth sciences -- Middle East -- Periodicals; Geology -- Africa -- Periodicals; Geology -- Middle East -- Periodicals; Sciences de la terre -- Afrique -- Périodiques; Sciences de la terre -- Moyen-Orient -- Périodiques; Géologie -- Afrique -- Périodiques; Géologie -- Moyen-Orient -- Périodiques; Earth sciences; Geology; Africa; Middle East; Periodicals; Electronic journals; 556.05
• DOI: 10.1016/j.jafrearsci.2015.12.007
• Languages: English
• ISSNs: 1464-343X

Abstract

Abstract: Dispersion pattern of geochemical elements in stream sediment data of a study area is affected by several factors, e.g., geological and geomorphological characteristics of the area. In this paper, we demonstrated recognition of efficient and inefficient mono-elemental geochemical signatures in a study area and exclusion of inefficient elements is worthwhile for increasing the probability of exploration success. For this, we adapted prediction-area (P-A) plot, normalized density and success-rate curve as tools that evaluate the ability of geochemical signatures in prediction of undiscovered mineral deposits and in delimiting exploration targets. After identification of efficient and inefficient elements, we combined efficient indicator elements to generate an effective prospectivity model. To illustrate the procedure we used a stream sediment data set for prospecting porphyry-Cu deposits in the Noghdouz area, Iran. Highlights: Prediction-area plot and success-rate curve are proposed to recognize efficient geochemical signatures. Recognizing efficient and inefficient indicator elements increases the exploration success. Integration of efficient indicator layers generates reliable target areas.