Identifying Hydrothermal Alterations Using Singularity Mapping of PCA Images Based on ASTER Data
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Identifying Hydrothermal Alterations Using Singularity Mapping of PCA Images Based on ASTER Data N. Ostadmahdi Aragh 1 & S. Mojeddifar 1 & M. Hemmati Chegeni 1 Received: 2 January 2020 / Accepted: 14 July 2020 # Society for Mining, Metallurgy & Exploration Inc. 2020
Abstract This work integrated selective principal component analysis (SPCA) with a singularity fractal model to map hydrothermal alterations of argillic, phyllic, and propylitic in the north-west of Kerman city in Iran. SPCA results were provided for short wave infrared (SWIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor to map phyllic and propylitic alterations. Also, the bands 5–7 were applied to map the argillic alteration zone. SPCA results could present useful information for alteration mapping but it does not show the purest pixels of alteration. Therefore, a fractal model of singularity was applied to highlight the different alteration pixels in the Kerman Cenozoic magmatic arc (KCMA). Thus, the singularity index of different hydrothermal alteration zones was produced. Comparing the obtained results with the field data showed that Kader, Serenu, Meiduk, and Abdar deposits were acceptably identified by alteration mapping. Also, it seems that the singularity index could not discriminate the hydrothermal alterations of argillic and phyllic. The same spectral signature of kaolinite and muscovite minerals is the main reason for misclassifications. Keywords ASTER . Singularity index . SPCA . Hydrothermal alteration
1 Introduction Porphyry copper deposits usually show four major kinds of hydrothermal alterations: potasic, phyllic, argillic, and propylitic. Various researches [1–9] were performed to study discrimination of alteration zones. They show that the conventional image-processing techniques could not differentiate various hydrothermal alteration regions. Therefore, separating hydrothermal alterations into regions that reflect specific mineralization is a challenging goal of image processing [10]. To address this challenge, several researchers attempted to improve traditional image processing techniques. For example, Gabr et al. [11] succeeded in discovering the high potential of the gold mineralization with the development of the band’s ratio and mineral extraction. Fereydooni and Mojeddifar [12] developed the DMF algorithm to discriminate the pure pixels of alteration minerals. They replaced a spectral signature– based filter (SSF) with the thresholding process in the matched filtering. The DMF was used for muscovite, alunite, * S. Mojeddifar [email protected] 1
Mining Engineering Department, Arak University of Technology, Daneshgah Street, Arak, Iran
chlorite, and kaolinite minerals to map alteration zones. The validation showed that the DMF algorithm discriminated the altered mineral with higher accuracy. Fereydooni et al. [13] improved the DMF procedure based on a pattern recognition network. The pattern recognition network used results of the DMF algorithm to measure the amount of kaol
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