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Mapping Alterations associated with Porphyry Copper Ores using ASTER Multispectral Imaging Based on Deep Learning
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Ahmad Rajabi   , Reza Shahhoseini * |
| University of Tehran |
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Abstract: (1925 Views) |
| One of the applications of remote sensing is to study and classify the alteration areas, which is one of the fastest methods to explore the porphyry copper deposit, determine its accumulation center and location of drilling points. The aim of this study is to identify argillic, phyllite, and propylitic alterations in small exploration ranges and to determine porphyry copper accumulation area as well. In this regard, an algorithm on the basis of deep convolutional cane crusts was designed. In the proposed algorithm, first, preprocessings such as geometric and spectral correction and repairing and training data amplification were performed in order to prepare RGB and SWIR data of the ASTER sensor to enter the chip. The proposed convolutional shear chip (CNN) has a coder-decoder structure that in the coding stage different and efficient features are extracted at different scales and in the decoding stage the generated features are combined to estimate the alteration regions. Then, the desired network was implemented for the images of the studied exploratory area called "Customs Mouth" located in Jiroft city, and the alteration areas of the region were extracted. For field evaluation of the results, lithological and geochemical methods were used on 84 samples. By merging the network results, extracting the geometric structure of the alterations and locating it on the fine copper and gold interpolation map of the region, and examining the lithological results, the alterations of the region with a statistical accuracy of sensitivity parameters: 0.943, F1 score: 0.472, IoU: 0.896 and lithographic detection accuracy 92% and an average copper grade above 4% were identified in these areas. The digging trenches map to extract mineral deposits was obtained on the basis of the detected alterations. |
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| Keywords: Porphyry copper deposit, Geometric structures, Alteration, Remote sensing, Deep Learning |
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Full-Text [PDF 3233 kb]
(832 Downloads)
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Type of Study: Research |
Subject:
RS
Received: 2021/09/2 | Accepted: 2022/02/19 | ePublished ahead of print: 2022/02/20 | Published: 2022/03/7
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