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.
