Using Hyperspectral Images in Mapping Alteration Minerals

Öz

Geoscientists require fieldwork primarily when investigating underground resources. Elements and minerals of economic value can be mobilized in rocks enriched either primarily or secondarily through temperature, pressure, and tectonic activities by hydrothermal fluids. Hydrothermal solutions, becoming saturated over time, deposit ore minerals within fractures, faults, and discontinuities where they are chemically or physically suitable in geological environments. These dissolution and precipitation processes are also referred to as hydrothermal activities. Consequently, mineral transformations occur in rocks interacting with hydrothermal fluids, leading to the formation of newly created minerals known as alteration minerals (sulfate and clay minerals; such as alunite, kaolinite, sericite and Fe-bearing minerals such as hematite, limonite, etc.). Chemical components such as OH, Al, Mg, Fe, Cl, and CO3 present in the chemical compositions of minerals exhibit recognizable absorption values in certain sections of the electromagnetic spectrum. Remote sensing (RS) methods provide significant results in mapping these minerals due to their identifiable absorption values in specific segments of the electromagnetic spectrum. RS, along with associated image processing techniques, is applied to potentially prospective areas for mineral deposits. Images from multispectral (Landsat, ASTER, Worldview, etc.) or hyperspectral (Hyperion, PRISMA, HypSIS, etc.) satellites can be used for the purpose. Images are first subjected to some pre-processing such as geometric, radiometric and atmospheric. Afterwards, on the images; Image processing methods such as band ratio (BR), principal component analysis (PCA), minimum noise segmentation (MNF) and classification are applied.

Anahtar Kelimeler

• Hyperspectral imaging
• Alteration
• Mineral mapping
• Classification

Alterasyon Minerallerinin Haritalamasında Hiperspektral Görüntülerin Kullanılması

Öz

Yer bilimciler yeraltı kaynaklarını araştırırken öncelikle saha çalışmalarına ihtiyaç duyarlar. Araştırmacılar için yer altı kaynaklarının tespit edilmesinde yüzey verileri oldukça önem taşımaktadır. Ekonomik açıdan değerli olan element ve mineraller hidrotermal akışkanlar tarafından sıcaklık, basınç ve tektonik faaliyetlerle birincil ya da ikincil olarak zenginleştikleri kayaçlarda mobil hale getirilebilirler. Zamanla doygun hale gelen hidrotermal çözeltiler bünyesindeki cevher minerallerini kimyasal veya fiziksel olarak elverişli jeolojik ortamlarda bulundukları kırık, çatlak ve süreksizliklerde çökeltirler. Bu çözme ve çökeltme süreçleri hidrotermal faaliyet olarak da adlandırılır. Süreç sonucunda hidrotermal akışkanlarla etkileşime giren kayaçlarda mineral dönüşümleri gerçekleşir ve yeni oluşan bu minerallere alterasyon mineralleri (alunit, kaolinit, serizit vb. sülfat, kil mineralleri ve hematit, limonit vb. demirli mineraller) adı verilir. Minerallerin kimyasal bileşimlerinde bulunan OH, Al, Mg, Fe, Cl ve CO3 gibi kimyasal bileşenler elektromanyetik spektrumun belirli bölümlerinde tanınabilir absorbsiyon değerlerine sahip oldukları için bu minerallerin haritalanmasında uzaktan algılama (UA) yöntemleri dikkate değer sonuçlar verir. Maden yatakları açısından önemli bir veri kaynağı olan multispektral (Landsat, ASTER, Worldview vb.) veya hiperspektral (Hyperion, PRISMA, HypSIS vb görüntülerinden elde edilen mineral haritalarına amaca yönelik olarak görüntü işleme yöntemleri uygulanabilir. Görüntüler öncelikle geometrik, radyometrik ve atmosferik gibi bir takım ön işlemlere tabi tutulduktan sonra bant oranlama, temel bileşen analizi (PCA), minimum gürültü bölütlemesi (MNF) ve sınıflama gibi görüntü işleme yöntemleri uygulanır.

Anahtar Kelimeler

• Hiperspektral görüntüleme
• Alterasyon
• Mineral haritalama
• Sınıflama

Kaynakça

1. Abrams, M., & Hook, S. J. (2002). ASTER User Handbook Version 2. Jet Propulsion, 2003(23/09/2003), 135. Abrams2002NASA.pdf
2. Asokan, A., Anitha, J., Ciobanu, M., Gabor, A., Naaji, A., & Hemanth, D. J. (2020). Image Processing Techniques for Analysis of Satellite Images for Historical Maps Classification—An Overview. Applied Sciences 2020, Vol. 10, Page 4207, 10(12), 4207. https://doi.org/10.3390/APP10124207
3. Barnsley, M. J., Settle, J. J., Cutter, M. A., Lobb, D. R., & Teston, F. (2004). The PROBA/CHRIS mission: A low-cost smallsat for hyperspectral multiangle observations of the earth surface and atmosphere. IEEE Transactions on Geoscience and Remote Sensing, 42(7), 1512–1520. https://doi.org/10.1109/TGRS.2004.827260
4. Bierwirth, P. N. (2002). Evaluation of ASTER satellite data for geological applications. Consultancy Report to Geoscience Australia.
5. Boardman, J. W. (1993). Automating spectral unmixing of AVIRIS data using convex geometry concepts. JPL, Summaries of the 4th Annual JPL Airborne Geoscience Workshop. Volume 1: AVIRIS Workshop.
6. Brickey, D., Crowley, J., & Rowan, L. (1987). Analysis of airborne imaging spectrometer data for the Ruby Mountains, Montana, by use of absorption-band-depth images. JPL Proceedings of The, Undefined. https://ntrs.nasa.gov/citations/19880004389
7. Crowley, J. K., Brickey, D. W., & Rowan, L. C. (1989). Airborne imaging spectrometer data of the Ruby Mountains, Montana: Mineral discrimination using relative absorption band-depth images. Remote Sensing of Environment, 29(2), 121–134.
8. Dadon, A., Karnieli, A., Ben-Dor, E., & Beyth, M. (2012). Examination of spaceborne imaging spectroscopy data utility for stratigraphic and lithologic mapping. https://cris.bgu.ac.il/en/publications/examination-of-spaceborne-imaging-spectroscopy-data-utility-for-s-10

9. Gersman, R., Ben-Dor, E., Beyth, M., Avigad, D., Abraha, M., & Kibreab, A. (2008). Mapping of hydrothermally altered rocks by the EO‐1 Hyperion sensor, Northern Danakil Depression, Eritrea. International Journal of Remote Sensing, 29(13), 3911–3936. https://doi.org/10.1080/01431160701874587
10. Goetz, A. F. H., & Srivastava, V. (1985). Mineralogical Mapping in the Cuprite Mining District, Nevada. Proc. of the Airborne Imaging Spectrometer Data Anal. Workshop.
11. Goetz, A. F. H., Vane, G., Solomon, J. E., & Rock, B. N. (1985). Imaging Spectrometry for Earth Remote Sensing. Science, 228(4704), 1147–1153. https://doi.org/10.1126/SCIENCE.228.4704.1147
12. Green, A. A., Berman, M., Switzer, P., & Craig, M. D. (1988). A Transformation for Ordering Multispectral Data in Terms of Image Quality with Implications for Noise Removal. IEEE Transactions on Geoscience and Remote Sensing, 26(1), 65–74. https://doi.org/10.1109/36.3001
13. Hewson, R. D., Cudahy, T. J., Mizuhiko, S., Ueda, K., & Mauger, A. J. (2005). Seamless geological map generation using ASTER in the Broken Hill-Curnamona province of Australia. Remote Sensing of Environment, 99(1–2), 159–172. https://doi.org/10.1016/J.RSE.2005.04.025
14. Hubbard, B. E., Crowley, J. K., & Zimbelman, D. R. (2003). Comparative alteration mineral mapping using visible to shortwave infrared (0.4-2.4 μm) Hyperion, ALI, and ASTER imagery. IEEE Transactions on Geoscience and Remote Sensing, 41(6 PART I), 1401–1410. https://doi.org/10.1109/TGRS.2003.812906
15. Kalinowski, A., & Oliver, S. (2004). ASTER mineral index processing manual. Tech. Rep., Geoscience Australia.
16. Kruse, F. (2002). Comparison of AVIRIS and Hyperion for Hyperspectral Mineral Mapping.
17. Kruse, F. A., Boardman, J. W., & Huntington, J. F. (2003). Comparison of airborne hyperspectral data and EO-1 Hyperion for mineral mapping. IEEE Transactions on Geoscience and Remote Sensing, 41(6 PART I), 1388–1400. https://doi.org/10.1109/TGRS.2003.812908
18. Kruse, F. A., & Perry, S. L. (2007). Regional mineral mapping by extending hyperspectral signatures using multispectral data. IEEE Aerospace Conference Proceedings. https://doi.org/10.1109/AERO.2007.353059
19. Kruse, F., Kierein-Young, K. S., & Boardman, J. (1990). Mineral mapping at Cuprite, Nevada with a 63-channel imaging spectrometer.
20. Mars, J. C., & Rowan, L. C. (2006). Regional mapping of phyllic- and argillic-altered rocks in the zagros magmatic arc, Iran, using advanced spaceborne thermal emission and reflection radiometer (ASTER) data and logical operator algorithms. Geosphere, 2(3), 161–186. https://doi.org/10.1130/GES00044.1
21. Michaux, S., P., & O’Connor, L. (2020). How to Set Up and Develop a Geometallurgical Program- GTK Open Work File Report 72/2019. 245.
22. Pearlman, J. S., Barry, P. S., Segal, C. C., Shepanski, J., Beiso, D., & Carman, S. L. (2003). Hyperion, a space-based imaging spectrometer. IEEE Transactions on Geoscience and Remote Sensing, 41(6 PART I), 1160–1173. https://doi.org/10.1109/TGRS.2003.815018
23. Porter, W. M., & Enmark, H. T. (1987). A System Overview Of The Airborne Visible/Infrared Imaging Spectrometer (Aviris). Imaging Spectroscopy II, 0834, 22. https://doi.org/10.1117/12.942280
24. Rowan, L. C., Mars, J. C., & Simpson, C. J. (2005a). Lithologic mapping of the Mordor, NT, Australia ultramafic complex by using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Remote Sensing of Environment, 99(1–2), 105–126. https://doi.org/10.1016/J.RSE.2004.11.021
25. Rowan, L. C., Mars, J. C., & Simpson, C. J. (2005b). Lithologic mapping of the Mordor, NT, Australia ultramafic complex by using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Remote Sensing of Environment, 99(1–2), 105–126.
26. Vane, G. (1987). First Results From The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Imaging Spectroscopy II, 0834, 166. https://doi.org/10.1117/12.942296
27. Vane, G., Goetz, A. F. H., & Wellman, J. B. (1984). Airborne imaging spectrometer: A new tool for remote sensing. IEEE Transactions on Geoscience and Remote Sensing, GE-22(6), 546–549. https://doi.org/10.1109/TGRS.1984.6499168
28. Vane, G., Green, R. O., Chrien, T. G., Enmark, H. T., Hansen, E. G., & Porter, W. M. (1993). The airborne visible/infrared imaging spectrometer (AVIRIS). Remote Sensing of Environment, 44(2–3), 127–143. https://doi.org/10.1016/0034-4257(93)90012-M