Identification of geochemical anomalies associated with Cu mineralization by applying spectrum-area multi-fractal and wavelet neural network methods in Shahr-e-Babak mining area, Kerman, Iran
Identification of geochemical anomalies associated with Cu mineralization by applying spectrum-area multi-fractal and wavelet neural network methods in Shahr-e-Babak mining area, Kerman, Iran
عنوان فارسی :
Identification of geochemical anomalies associated with Cu mineralization by applying spectrum-area multi-fractal and wavelet neural network methods in Shahr-e-Babak mining area, Kerman, Iran
عنوان انگلیسی :
Identification of geochemical anomalies associated with Cu mineralization by applying spectrum-area multi-fractal and wavelet neural network methods in Shahr-e-Babak mining area, Kerman, Iran
چکیده انگلیسی:
The Shahr-e-Babak district, as the studied area, is known for its large Cu resources. It is located in the southern side of the Central Iranian volcano–sedimentary complex in SE Iran. Shahr-e-Babak is currently facing a shortage of resources, and therefore, mineral exploration in the deeper and peripheral spaces has become a high priority in this area. This work aims to identify the geochemical anomalies associated with the Cu mineralization using the Spectrum–Area (S–A) multi-fractal and Wavelet Neural Network (WNN) methods. At first, the Factor Analysis (FA) is applied to integrate the multi-geochemical variables of a regional stream sediment dataset related to major mineralization elements in the studied area. Then the S–A model is applied to decompose the mixed geochemical patterns obtained from FA and compare with the results obtained from the WNN method. The S–A model, based on the distinct anisotropic scaling properties, reveals the local anomalies due to the consideration of the spatial characteristics of the geochemical variables. Most of the research works show that the capability (i.e. classification, pattern matching, optimization, and prediction) of an ANN considering its successful application is suitable for inheriting uncertainties and imperfections that are found in mining engineering problems. In this paper, an alternative method is presented for mineral prospecting based on the integration of wavelet theory and ANN or wavelet network. The results obtained for the WNN method are in a good agreement with the known deposits, indicating that the WNN method with Morlet transfer function consists of a highly complex ability to learn and track unknown/undefined complicated systems. The hybrid method of FA, S–A, and WNN employed in this work is useful to identify anomalies associated with the Cu mineralization for further exploration of mineral resources.
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