Identification of Buried Metal Ore Deposits using Geochemical Anomaly Filtering and Principal Factors of Power Spectrum

Over the past two decades, the frequency domain (FD) of the geochemical data has been studied by some researchers. Metal zoning is one of the challenging subjects in the mining exploration, where a new scenario has been proposed for solving this problem in FD. Three mineralization areas including the Dalli (Cu-Au), Zafarghand (Cu-Mo), and Tanurcheh (Au-Cu) mineralization areas are selected for this investigation. After transferring the surface geochemical data to FD, the geochemical signals obtained are filtered using the wavenumber-based filters. The high and moderate frequency signals are removed, and the residual signals are interpreted by the statistical method of principal component analysis (PCA). In order to discriminate the deep metal ore deposits, the principal factors of elemental power spectrum extracted by PCA are depicted in a novel diagram (PC۱ vs. PC۲). This approach indicates that the geochemical data in the Dalli and Zafarghand deep ore deposits have similar frequency behaviors. The Au, Mo, and Cu elements in these two areas are discriminated from the Au, Mo, and Cu mineralization elements of the Tanurcheh area as a deep non-mineralization zone in this diagram. This new criterion used for distinguishing the buried ore deposits and deep non-mineralization zones is properly confirmed by the exploratory deep drilled boreholes. The geochemical anomaly filtering demonstrates that the strong signatures of deep mineralization are associated with the low frequency geochemical signals at the surface, and the buried mineralization areas with weak surface anomaly can be identified using the geochemical FD data.

Keywords:

geochemical anomaly filtering , buried deposit , wavenumber-based filter , Power Spectrum