Prediction of mineral deposit model and identification of mineralization trend in depth using frequency domain of surface geochemical data in Dalli Cu-Au porphyry deposit

In this research work, the frequency domain (FD) of surface geochemical data was analyzed to decompose the complex geochemical patterns related to different depths of the mineral deposit. In order to predict the variation in mineralization in the depth and identify the deep geochemical anomalies and blind mineralization using the surface geochemical data for the Dalli Cu-Au porphyry deposit, a newly developed approach was proposed based on the coupling Fourier transform and principal component analysis. The surface geochemical data was transferred to FD using Fourier transformation and high and low pass filters were performed on FD. Then the principal component analysis method was employed on these frequency bands separately. This new combined approach demonstrated desirably the relationship between the high and low frequencies in the surface geochemical distribution map and the deposit depth. This new combined approach is a valuable data-processing tool and pattern-recognition technique to identify the promising anomalies, and to determine the mineralization trends in the depth without drilling. The information obtained from the exploration drillings such as boreholes confirms the results obtained from this method. The new exploratory information obtained from FD of the surface geochemical distribution map was not achieved in the spatial domain. This approach is quite inexpensive compared to the traditional exploration methods.

Keywords:

Principal Component Analysis , Frequency Domain (FD) , 2D Fourier Transformation , Blind Mineralization , Pattern Recognition