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Abstract
Concerning the importance of carbonates in mineralization and the necessity of separation of important formations in remote sensing data, we try to separate carbonates into two groups including limestone and dolomite by using PCA on ASTER SWIR bands. As you know this analysis results can be used in compiling geological maps and GIS modeling for finding the favorable potential area.
Introduction
The case study in this paper is in one of the biggest zinc and lead mines in the middle east, its important is due to its high volume of its resources .This mine is located in 20km of west south of Isfahan province in Iran in Longitude of 51º 32'-51º 45' and latitude of 32° 28'-32º 37' in north of Irankuh cordillera.
Our purpose of this study is determine the best potential area for detail exploration, so at the first step this paper studies the important formations in mineralization by PCA analysis on ASTER images.
Geology
According to the figure (a): geology formations of this area includes :dolomite, limestone, marl ,river fan, shale, conglomerate and mixture of dolomite and limestone. In this figure limestone has been shown in light blue, while dolomite has been shown in red and pure limestone in deep blue.
In this mine, dolomite plays an important role in mineralization, and there is a great chance to find minerals such as Siderite, Smithsonite, Barite; even small lenses in this dolomite.
According to scientist’s research, both of north and south Irankuh have been formed by Meta somatic substitution in cretaceous limestone and Rastad (1981) believes that mineralization has been occurred by deposition and the type of it is stratification.
Figure (a): Geological map of this case study
Applying PCA Analysis in order to Separate Carbonates
As discussed before, dolomite has an important role in mineralization and in some areas, dolomite is mixed by limestone, therefore in order to separate Carbonates into limestone and dolomite, PCA analysis was applied. The benefit of this method is omitting Albedo and topography shadows from the images.
PCA was applied to subsets of four ASTER bands, using an adaptation of the Cro´sta technique proposed by Loughlin (1991). So in order to separate the dolomite from limestone, PCA analysis on 5,6,7,8 ASTER bands was done. The following table (1) shows the eigenvector statistics ,that with considering PCA properties we could find dolomite in PC4 with negative loading (figure b) that indicating that pixels likely to contain dolomite will be represented by low (dark) DN values in PC4.
To facilitate visualization, the PC4 image is then negated (by multiplying all pixels by -1), so that the target material is displayed as bright pixels in the respective abundance image. (Figure c)
Table (1): Eigenvector statistics for ASTER bands 5, 6, 8 and 7
Figure (b): PC4 in PCA Analysis that indicating That pixels likely to Contain dolomite will be Represented by Low (Dark) DN values
Figure (c):-PC4 in PCA analysis That indicating dolomite Dolomite as bright pixels.
For limestone separation from the other formations PCA analysis was performed on 4,6,8,9 SWIR ASTER. The following table (2) shows the eigenvector statistics of these bands. As concluded from this table, limestone is shown in -PC3 in bright pixels (figure d).
Table (2): Eigenvector statistics for ASTER bands 4, 6, 8 and 9
Figure (d): -PC3 in PCA analysis That indicating limestone as bright pixels
Accuracy assessment of the research
The geological map which was complied from this area, confirms the high accuracy of this analysis figure (a). Dolomite appears in red figure (a) is in accordance with figure (c). Also pure limestone in dark blue and mixed limestone in light blue have been shown in figure (a), that are in high accordance with figure (d).
Discussion and Conclusion
For separating carbonates into dolomite and limestone, we can use PCA analysis in order to map them. The benefit of this method is demonstrating dolomite and limestone according to their purity, that can be used in the future studies.
References
Separation of Carbonates by Using PCA on ASTER Bands
Shahab Poursaleh
Head of image processor at SSTEC (Satellite Science & Technology Engineering Co)
No.750, Gozar Farvardin, 10th street, South Piroozan Street
Tel: 0098-218086872-6
Email: Poursaleh@hotmail.com
Shahab Poursaleh
Head of image processor at SSTEC (Satellite Science & Technology Engineering Co)
No.750, Gozar Farvardin, 10th street, South Piroozan Street
Tel: 0098-218086872-6
Email: Poursaleh@hotmail.com
Abstract
Concerning the importance of carbonates in mineralization and the necessity of separation of important formations in remote sensing data, we try to separate carbonates into two groups including limestone and dolomite by using PCA on ASTER SWIR bands. As you know this analysis results can be used in compiling geological maps and GIS modeling for finding the favorable potential area.
Introduction
The case study in this paper is in one of the biggest zinc and lead mines in the middle east, its important is due to its high volume of its resources .This mine is located in 20km of west south of Isfahan province in Iran in Longitude of 51º 32'-51º 45' and latitude of 32° 28'-32º 37' in north of Irankuh cordillera.
Our purpose of this study is determine the best potential area for detail exploration, so at the first step this paper studies the important formations in mineralization by PCA analysis on ASTER images.
Geology
According to the figure (a): geology formations of this area includes :dolomite, limestone, marl ,river fan, shale, conglomerate and mixture of dolomite and limestone. In this figure limestone has been shown in light blue, while dolomite has been shown in red and pure limestone in deep blue.
In this mine, dolomite plays an important role in mineralization, and there is a great chance to find minerals such as Siderite, Smithsonite, Barite; even small lenses in this dolomite.
According to scientist’s research, both of north and south Irankuh have been formed by Meta somatic substitution in cretaceous limestone and Rastad (1981) believes that mineralization has been occurred by deposition and the type of it is stratification.
Figure (a): Geological map of this case study
Applying PCA Analysis in order to Separate Carbonates
As discussed before, dolomite has an important role in mineralization and in some areas, dolomite is mixed by limestone, therefore in order to separate Carbonates into limestone and dolomite, PCA analysis was applied. The benefit of this method is omitting Albedo and topography shadows from the images.
PCA was applied to subsets of four ASTER bands, using an adaptation of the Cro´sta technique proposed by Loughlin (1991). So in order to separate the dolomite from limestone, PCA analysis on 5,6,7,8 ASTER bands was done. The following table (1) shows the eigenvector statistics ,that with considering PCA properties we could find dolomite in PC4 with negative loading (figure b) that indicating that pixels likely to contain dolomite will be represented by low (dark) DN values in PC4.
To facilitate visualization, the PC4 image is then negated (by multiplying all pixels by -1), so that the target material is displayed as bright pixels in the respective abundance image. (Figure c)
Table (1): Eigenvector statistics for ASTER bands 5, 6, 8 and 7
| PC1 | PC2 | PC3 | PC4 | |
| Band 5 | 0.481 | -0.331 | 0.235 | -0.777 |
| Band 6 | 0.559 | -0.458 | -0.289 | 0.629 |
| Band 7 | 0.475 | 0.040 | 0.879 | 0.011 |
| Band 8 | 0.481 | 0.824 | -0.297 | 0.037 |
Figure (b): PC4 in PCA Analysis that indicating That pixels likely to Contain dolomite will be Represented by Low (Dark) DN values
Figure (c):-PC4 in PCA analysis That indicating dolomite Dolomite as bright pixels.
For limestone separation from the other formations PCA analysis was performed on 4,6,8,9 SWIR ASTER. The following table (2) shows the eigenvector statistics of these bands. As concluded from this table, limestone is shown in -PC3 in bright pixels (figure d).
Table (2): Eigenvector statistics for ASTER bands 4, 6, 8 and 9
| PC1 | PC2 | PC3 | PC4 | |
| Band 4 | 0.452 | 0.791 | 0.058 | 0.407 |
| Band 6 | 0.583 | 0.052 | -0.428 | -0.688 |
| Band 8 | 0.500 | -0.292 | 0.809 | -0.101 |
| Band 9 | 0.453 | -0.534 | -0.399 | 0.592 |
Figure (d): -PC3 in PCA analysis That indicating limestone as bright pixels
Accuracy assessment of the research
The geological map which was complied from this area, confirms the high accuracy of this analysis figure (a). Dolomite appears in red figure (a) is in accordance with figure (c). Also pure limestone in dark blue and mixed limestone in light blue have been shown in figure (a), that are in high accordance with figure (d).
Discussion and Conclusion
For separating carbonates into dolomite and limestone, we can use PCA analysis in order to map them. The benefit of this method is demonstrating dolomite and limestone according to their purity, that can be used in the future studies.
References
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