Power spectrum– area fractal modeling of data obtained from spectral analysis of U spatial statistics to separate porphyry copper geochemical anomaly areas

In geochemical exploration studies, different anomaly separation methods, such as fractal methods and U-spatial statistics from the group of structural techniques, have been proposed for data interpretation and separation of anomalous zones. In this study,, these methods, were studied the spectral properties of U-values related to porphyry copper mineralization in Zafarqand region. The signals in the geochemical data and their variables indicate the status of the data variability in the location. To determine the variability of the U-statistics and to analyze its different frequencies, a new method was introduced and applied as the fractal method of the power spectrum– area of U-statistics. In this method, the U- statistics power spectrum data were divided into 5 different category, each of which belong to different frequency spectra. Multivariate principal component analysis method was used to determine the type of background or anomaly of these categories. Principal component analysis method was performed on the power spectrum matrix for the U values of all elements and for each category, separately. The results showed that Cu mineralization factor is present in frequency categories 1, 2 and 3 which also show low power spectrum values. These categories can be considered as categories of anomaly. The categories 4 and 5, based on principal component analysis, do not show a well mineralization effect. Therefore, these categories can be considered as background. Background frequency categories were filtered out of the anomaly categories and excluded from the data. Finally, the residual power spectrum was transferred to the location domain using the inverse Fourier transform and the anomaly map was obtained. In this map, the values are of parameter U and the location of the anomalies is well marked on it. The drilling results on these anomalies indicate the existence of deep mineralization.