Abstarct: The geochemical data can be transferred to other domains such as frequency and position – scale domains using Fourier transform (FT) and wavelet transform (WT). The investigations made in this thesis show that the analysis of geochemical data in frequency and position-scale domains can provide new exploratory information that may not be revealed in the spatial domain (SD). The surface geochemical data in Zafarghand Cu–Mo porphyry, Dalli Cu-Au porphyry and Shand Mahmood Sb mineralization areas, as case studies, have been used in this thesis. The geochemical data in frequency domain (FD) have been analyzed using principal component analysis (PCA). This analysis has identified the mineralizing elements much better than the results of SD. This method has classified the FD of geochemical data in two components and has separated the mineralization elements of Au, Cu and Mo from other elements properly. Therefore the combined approach of FT – PCA has been presented as a new dimension reduction method in analysis of geochemical data. The Fourier analysis has higher ability for identification of mineralization elements and analysis of geochemical data than SD. In this research, the FD of surface geochemical data has been analyzed to decompose the complex geochemical patterns related to different depths of mineral deposit. In order to prediction of mineral deposit model and identification of variations of mineralization and their trend in the depth, the frequency coefficients method (FCM) has been presented baxsed on frequency distribution of elements. The geochemical halos of mineral deposits at different depths impress on frequency distribution of elements in the surface. This investigation demonstrates that FCM is a powerful technique for identification of deep and blind mineral deposits in FD of geochemical data. This new approach has desirably demonstrated the relationship between high and low frequencies in surface geochemical distribution map and depth of deposit. FCM is a valuable data processing tool and pattern recognition technique to identify the promising anomalies and to determine the mineralization trends in depth without exploration drilling. The introduced technique makes possible the distinction between blind mineralization and zone of dispersed ore mineralization using surface geochemical data. The results of FCM show that there is a zone of dispersed ore mineralization in Shand Mahmood area that are confirmed by the results of drilled borehole. This research demonstrates that the position-scale domain of geochemical data has also important exploratory information. PCA has been performed on approximate and details components in two levels of Haar discrete WT and a new index has been presented baxsed on wavelet coefficients and their results have been interpreted. Significant results have been obtained using PCA on position – scale domain of geochemical data. Information obtained from the exploration drillings such as trenches and boreholes confirm the results of FT and WT. The results of this research show that the geochemical data can successfully be analyzed in frequency and position – scale domains.