The spectrum–area (S–A) fractal model is a powerful tool for decomposition of complex anomaly patterns of gridded geochemical data. Ordinary moving average interpolation techniques are commonly being used for gridding geochemical data; however, these methods suffer from two major drawbacks of (1) ignoring the locally high values and (2) smoothing the interpolated surface. Multifractal moving average interpolation methods have been developed to overcome the shortcomings of ordinary moving average methods. This study seeks to compare two sets of multifractal and ordinary gridded geochemical data using success rate curves and applies the S–A fractal model to decompose anomalous geochemical patterns. A set of stream sediment geochemical data in Ahar area, NW Iran, was used as a case study. Then, a mineralization-related multi-element geochemical signature was gridded by ordinary and multifractal approaches and considered for further analyses. The S–A fractal method was applied to decompose anomaly and background components of the resultant multi-element geochemical signature. Exploration targets were delimited and further evaluated using two bivariate statistical procedures of Student's t-value and normalized density index. The results revealed that (a) application of multifractal gridded data enhances the predicting ability of geochemical signatures, (b) application of S–A fractal model on multifractal gridded data allows for superior discrimination of geochemical anomalies, and (c) the multi-element geochemical anomalies in the Ahar area related to porphyry-Cu deposits were properly delineated through sequence application of multifractal interpolation and S–A fractal model.
