a critique on power spectrum – area fractal method for geochemical anomaly mapping

Power spectrum – area fractal (sa fractal) method has been frequently applied for geochemical anomaly mapping. some researchers have performed this method for separation of geochemical anomaly, background and noise and have delineated their distribution maps. in this research, surface geochemical data of zafarghand cumo mineralization area have been utilized and some defects of sa fractal method have been discussed. the surface geochemical data were transformed to the frequency domain using fourier transformation and the sa fractal method was performed on obtained cu power spectrum. 4 geochemical classes were distinguished on the basis of fractal diagram then these classes were separated using various filters and their signals were analyzed separately by principal component analysis (pca) and the situation of mineralization was interpreted. pca shows the low frequency geochemical signals have strongly been affected by the cu and mo mineralization process. in the end, the cu geochemical anomaly map based on this low frequency class was delineated using inverse fourier transformation. the deep borehole that was drilled in the center of this obtained anomaly shows there is a mineralization zone at the depth. the disadvantages of sa fractal method have been discussed using these obtained results.

A Critique on Power Spectrum – Area Fractal Method for Geochemical Anomaly Mapping

Power spectrum – area fractal (SA fractal) method has been frequently applied for geochemical anomaly mapping. Some researchers have performed this method for separation of geochemical anomaly, background and noise and have delineated their distribution maps. In this research, surface geochemical data of Zafarghand CuMo mineralization area have been utilized and some defects of SA fractal method have been discussed. The surface geochemical data were transformed to the frequency domain using Fourier transformation and the SA fractal method was performed on obtained Cu power spectrum. 4 geochemical classes were distinguished on the basis of fractal diagram then these classes were separated using various filters and their signals were analyzed separately by principal component analysis (PCA) and the situation of mineralization was interpreted. PCA shows the low frequency geochemical signals have strongly been affected by the Cu and Mo mineralization process. In the end, the Cu geochemical anomaly map based on this low frequency class was delineated using inverse Fourier transformation. The deep borehole that was drilled in the center of this obtained anomaly shows there is a mineralization zone at the depth. The disadvantages of SA fractal method have been discussed using these obtained results.