Environment
H. Mahdiyanfar
Abstract
Detection of deep and hidden mineralization using the surface geochemical data is a challenging subject in the mineral exploration. In this work, a novel scenario based on the spectrum–area fractal analysis (SAFA) and the principal component analysis (PCA) has been applied to distinguish and delineate ...
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Detection of deep and hidden mineralization using the surface geochemical data is a challenging subject in the mineral exploration. In this work, a novel scenario based on the spectrum–area fractal analysis (SAFA) and the principal component analysis (PCA) has been applied to distinguish and delineate the blind and deep Mo anomaly in the Dalli Cu–Au porphyry mineralization area. The Dalli mineral deposit is located on the volcanic–plutonic belt of Sahand–Bazman in the central part of Iran. The geochemical data was transformed to the frequency domain using the Fourier transformation, and SAFA was applied for classification of geochemical frequencies and detection of geochemical populations. The very low-frequency signals in the fractal method were separated using the low-pass filter function and were interpreted using PCA. This scenario demonstrates that the Mo element has an important role in the mineralization phase in the very low-frequency signals that are related to the deep mineralization; it is an important innovation in this work. Then the Mo geochemical anomaly has been mapped using the inverse Fourier transformation. This research work shows that the high-power spectrum values in SAFA are related to the background elements and the deep mineralization. Two exploratory boreholes drilled inside and outside the deep Mo anomaly area properly confirm the results of the proposed approach.
Exploitation
H. Shahi
Abstract
Discrimination of the blind and dispersed mineralization deposits is a challenging problem in geochemical exploration. The frequency domain (FD) of the surface geochemical data can solve this important issue. This new exploratory information can be achieved using the interpretation of FD of geochemical ...
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Discrimination of the blind and dispersed mineralization deposits is a challenging problem in geochemical exploration. The frequency domain (FD) of the surface geochemical data can solve this important issue. This new exploratory information can be achieved using the interpretation of FD of geochemical data, which is impossible in spatial domain. In this research work, FD of the surface geochemical data is analyzed to decompose the complex geochemical patterns related to the mineral deposits. In order to identify the dispersed mineralization zone in the Chichakloo Pb–Zn deposit, a newly developed approach is proposed based on the coupling of two-dimensional Fourier transform (2DFT) and principal component analysis (PCA). The surface geochemical data is transferred to FD using 2DFT, and two low-pass filters are designed and performed on FD. Then the PCA method is employed on these frequency bands (FBs) separately. This proposed scenario desirably illustrates the relationship between the low frequencies in the surface geochemical distribution map (GDM) and the deep deposits. The informations obtained from the detailed exploration and the exploration drillings such as boreholes confirm the results obtained from this method. This new combined approach is a valuable data-processing tool and pattern-recognition technique in geochemical explorations. This approach is quite inexpensive compared to the traditional exploration methods.
H. Shahi; R. Ghavami Riabi; A. Kamkar Ruhani; H. Asadi Haroni
Abstract
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 ...
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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.