| Abstract: | In the current research to determine the mineralization pattern and discuss the mineralization components, the information of position - scale domain of geochemical data has been analyzed. A new method is proposed based on coupling discrete wavelet transforms (DWT) and principal component analysis (PCA) for mineralization elements forecasting applications. The results of this study indicate the potential of DWT–PCA method for geochemical data processing. Wavelet transform (WT), as a multi-spectral analysis method, can decompose the spatial and temporal signals into different frequencies. The features of mineralization can be identified using the position - scale domain of geochemical data that may not be achievable in spatial domain. The geochemical data from the Dalli region have been processed in the spatial domain using PCA. The surface geochemical data of 30 elements have been transformed to position–scale domain using two-dimensional discrete wavelet transform (2DDWT). Wavelet functions (WFs) of Haar, Coiflet2, Biorthogonal3.3 and Symlet7 have been applied separately to decompose the geochemical data to high and low frequencies in one level. To obtain more accurate and complete information of mineralization, a new index has been presented based on wavelet coefficients. Based on this new index, significant results have been obtained by using PCA of the index. The coefficients distribution map (CDM) as a new exploratory criterion has been generated based on 2DDWT to show the geochemical distribution map (GDM). Finally, the results of WT have been compared with the results of spatial domain and the best method of wavelet for interpretation of geochemical data has been introduced. The results of geochemical data analysis by DWT–PCA approach have been confirmed by the exploratory drillings in the study area. |
