H. Mahdiyanfar - Department of Mining Engineering, University of Gonabad, Gonabad, Iran

Detection of deep and hidden mineralization using the surface geochemical data is a challenging subject in the mineral exploration. In this work, a novel scenario based on the spectrum–area fractal analysis (SAFA) and the principal component analysis (PCA) has been applied to distinguish and delineate the blind and deep Mo anomaly in the Dalli Cu–Au porphyry mineralization area. The Dalli mineral deposit is located on the volcanic–plutonic belt of Sahand–Bazman in the central part of Iran. The geochemical data was transformed to the frequency domain using the Fourier transformation, and SAFA was applied for classification of geochemical frequencies and detection of geochemical populations. The very low-frequency signals in the fractal method were separated using the low-pass filter function and were interpreted using PCA. This scenario demonstrates that the Mo element has an important role in the mineralization phase in the very low-frequency signals that are related to the deep mineralization; it is an important innovation in this work. Then the Mo geochemical anomaly has been mapped using the inverse Fourier transformation. This research work shows that the high-power spectrum values in SAFA are related to the background elements and the deep mineralization. Two exploratory boreholes drilled inside and outside the deep Mo anomaly area properly confirm the results of the proposed approach.

Power Spectrum–Area Fractal Analysis, Anomaly separation, Principal Component Analysis, blind geochemical anomaly, Pattern Recognition