Skip to main content

Advertisement

SpringerLink
Log in

Integration of ASTER and Soil Survey Data by Principal Components Analysis and One-Class Support Vector Machine for Mineral Prospectivity Mapping in Kerkasha, Southwestern Eritrea

  • Original Paper
  • Published:
Natural Resources Research Aims and scope Submit manuscript

Abstract

This study evaluates the potential for mineral prospectivity mapping (MPM) within the Kerkesha area, southwestern Eritrea, using remote sensing and geochemical data analysis. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data were used for mapping zones of hydrothermal alteration, while assessment of geologic structures was based on automated extraction of lineaments from a digital elevation model (DEM). Integration of these alteration and structural datasets with surface geochemical data was carried out to identify pathfinder elements associated with Au–Cu–Zn mineralization as well as to evaluate and delineate anomalous regions in this relatively underexplored region of the Arabia Nubia Shield (ANS). Specifically, the modeling approach for the extraction and interpretation of mineralization-related spectral footprints was the selective principal component analysis (SPCA), and the lineament features extracted from DEM derivatives were integrated with the soil geochemical data and modeled by principal component analysis (PCA). The results revealed a NE–SW trend of lineaments, delineated zones of hydrothermal alteration indicating the presence of multi-deposit type mineralization, and identified pathfinder elements. In addition, Au–Cu–Zn anomalous zones were extracted by one-class support vector machine (OCSVM) and the performance of such classification was validated by the area under the curve (AUC) and the Youden index computed from receiver operating characteristic (ROC) curves and the Kruskal–Wallis test. The results showed significance in differences between the anomalous and non-anomalous zones and existence of a relationship between known mineral deposits and predicted anomalies. The proposed MPM shows promising results for robust automated delineation and understanding of mineralization processes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15

Similar content being viewed by others

References

  • Abd El Monsef, M., Slobodník, M., & Salem, I. A. (2018). Hydrothermal evolution of granitoid-hosted gold mineralization in gidami area: An example for orogenic-gold deposits in Egypt. Journal of African Earth Sciences, 146, 132–149.

    Google Scholar 

  • Abdelsalam, M. G., Stern, R. J., & Berhane, W. G. (2000). Mapping gossans in arid regions with Landsat TM and SIR-C images: the Beddaho Alteration Zone in northern Eritrea. Journal of African Earth Sciences, 30(4), 903–916.

    Google Scholar 

  • Abrams, M. (2000). The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER): Data products for the high spatial resolution imager on NASA’s Terra platform. International Journal of Remote sensing, 21(5), 847–859.

    Google Scholar 

  • Abrams, M., & Hook, S. J. (1995). Simulated ASTER data for geologic studies. IEEE Transactions on Geoscience and remote sensing, 33(3), 692–699.

    Google Scholar 

  • Abrams. (1999). ASTER User Handbook.

  • Afzal, P., Mirzaei, M., Yousefi, M., Adib, A., Khalajmasoumi, M., Zarifi, A. Z., et al. (2016). Delineation of geochemical anomalies based on stream sediment data utilizing fractal modeling and staged factor analysis. Journal of African Earth Sciences, 119, 139–149s.

    Google Scholar 

  • Aitchison, J. (1986). The statistical analysis of compositional data. Journal of the Royal Statistical Society: Series B (Methodological), 44(2), 139–160.

    Google Scholar 

  • Aliyari, F., Rastad, E., & Zengqian, H. (2007). Orogenic Gold Mineralization in the Qolqoleh Deposit, Northwestern Iran. Resource Geology, 57(3), 269–282.

    Google Scholar 

  • Alpha exploration. (2023). Alpha exploration. https://alpha-exploration.com/. Accessed 17 October 2023.

  • Andersson, U. B., Ghebreab, W., & Teklay, M. (2006). Crustal evolution and metamorphism in east-central Eritrea, south-east Arabian-Nubian Shield. Journal of African Earth Sciences, 44(1), 45–65.

    Google Scholar 

  • Argialas, D. P., & Mavrantza, O. D. (2004). Comparison of edge detection and Hough transformation techniques for the extraction of geologic features. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 34, Part XXX.

  • Balaram, V., & Sawant, S. S. (2022). Indicator minerals, pathfinder elements, and portable analytical instruments in mineral exploration studies. Minerals, 12(4), 394.

    Google Scholar 

  • Barrie, C. T., Abdalla, M. A. F., & Hamer, R. D. (2016). Volcanogenic massive sulphide–oxide gold deposits of the Nubian Shield in Northeast Africa. In M. Bouabdellah & J. F. Slack (Eds.), Mineral deposits of North Africa (pp. 417–435). Springer International Publishing. https://doi.org/10.1007/978-3-319-31733-5_17

    Chapter  Google Scholar 

  • Barrie, C. T., Nielsen, F. W., & Aussant, C. H. (2007). The Bisha volcanic-associated massive sulfide deposit, western Nakfa terrane, Eritrea. Economic Geology, 102(4), 717–738.

    Google Scholar 

  • Bedell, R. L. (2001). Geological mapping with ASTER satellite: New global satellite data that is a significant leap in remote sensing geologic and alteration mapping. Special Publication Geology Society of Nevada, 33, 329–334.

    Google Scholar 

  • Berhe, S. M. (1990). Ophiolites in Northeast and East Africa: Implications for Proterozoic crustal growth. Journal of the Geological Society, 147(1), 41–57.

    Google Scholar 

  • Bierlein, F. P., McKeag, S., Reynolds, N., Bargmann, C. J., Bullen, W., Murphy, F. C., et al. (2016). The Jebel Ohier deposit—a newly discovered porphyry copper–gold system in the Neoproterozoic Arabian-Nubian Shield, Red Sea Hills, NE Sudan. Mineralium Deposita, 51(6), 713–724.

    Google Scholar 

  • Bierlein, F. P., Potma, W., Cernuschi, F., Brauhart, C., Robinson, J., Bargmann, C. J., et al. (2020). New Insights into the Evolution and Age of the Neoproterozoic Jebel Ohier Porphyry Copper Deposit, Red Sea Hills. Northeastern Sudan. Economic Geology, 115(1), 1–31.

    Google Scholar 

  • Burrough, P. A., McDonnell, R. A., & Lloyd, C. D. (2015). Principles of geographical information systems. Oxford University Press.

    Google Scholar 

  • Camilli, G., & Hopkins, K. D. (1978). Applicability of chi-square to 2 × 2 contingency tables with small expected cell frequencies. Psychological Bulletin, 85, 163–167.

    Google Scholar 

  • Carranza, E. J. M. (2011). Analysis and mapping of geochemical anomalies using logratio-transformed stream sediment data with censored values. Journal of Geochemical Exploration, 110(2), 167–185.

    Google Scholar 

  • Carranza, E. J. M., & Laborte, A. G. (2015). Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of Random Forests algorithm. Ore Geology Reviews, 71, 777–787.

    Google Scholar 

  • Cattell, R. B. (1966). The Scree test for the number of factors. Multivariate Behavioral Research, 1(2), 245–276.

    Google Scholar 

  • Chang, Z., Hedenquist, J. W., White, N. C., Cooke, D. R., Roach, M., Deyell, C. L., et al. (2011). Exploration tools for linked porphyry and epithermal deposits: Example from the Mankayan intrusion-centered Cu-Au District, Luzon, Philippines*. Economic Geology, 106(8), 1365–1398.

    Google Scholar 

  • Chen, X.-W., & Lin, X. (2014). Big data deep learning: Challenges and perspectives. IEEE access, 2, 514–525.

    Google Scholar 

  • Chen, Y., Lu, L., & Li, X. (2014). Application of continuous restricted Boltzmann machine to identify multivariate geochemical anomaly. Journal of Geochemical Exploration, 140, 56–63.

    Google Scholar 

  • Chen, Y., & Wu, W. (2017). Application of one-class support vector machine to quickly identify multivariate anomalies from geochemical exploration data. Geochemistry: Exploration, Environment, Analysis, 17(3), 231–238.

    Google Scholar 

  • Chen, Y., & Wu, W. (2019). Isolation forest as an alternative data-driven mineral prospectivity mapping method with a higher data-processing efficiency. Natural Resources Research, 28(1), 31–46.

    Google Scholar 

  • Chisambi, J., Haundi, T., & Tsokonombwe, G. (2021). Geologic structures associated with gold mineralization in the Kirk Range area in Southern Malawi. Open Geosciences, 13(1), 1345–1357.

    Google Scholar 

  • Cooley, T., Anderson, G. P., Felde, G. W., Hoke, M. L., Ratkowski, A., Chetwynd, J. H., et al. (2002). FLAASH, a MODTRAN4-based atmospheric correction algorithm, its application and validation (Vol. 3, pp. 1414–1418 vol.3). Presented at the International Geoscience and Remote Sensing Symposium (IGARSS). https://doi.org/10.1109/IGARSS.2002.1026134.

  • Crosta, A., & Moore, J., (1989). Enhancement of Landsat Thematic Mapper imagery for residual soil mapping in SW Minas Gerais State, Brazil: a prospecting case history in Greenstone belt terrain. In: Proceedings of the 7th ERIM Thematic Conference: Remote Sensing for Exploration Geology (pp. 1173–1187).

  • Crosta, A. P., De Souza Filho, C. R., Azevedo, F., & Brodie, C. (2003). Targeting key alteration minerals in epithermal deposits in Patagonia, Argentina, using ASTER imagery and principal component analysis. International journal of Remote sensing, 24(21), 4233–4240.

    Google Scholar 

  • 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.

    Google Scholar 

  • Daviran, M., Ghezelbash, R., Niknezhad, M., Maghsoudi, A., & Ghaeminejad, H. (2023). Hybridizing K-means clustering algorithm with harmony search and artificial bee colony optimizers for intelligence mineral prospectivity mapping. Earth Science Informatics. https://doi.org/10.1007/s12145-023-01019-2

    Article  Google Scholar 

  • Diaz, L. R., Santos, D. C., Käfer, P. S., da Rocha, N. S., da Costa, S. T. L., Kaiser, E. A., & Rolim, S. B. A. (2021). Atmospheric correction of thermal infrared landsat images using high-resolution vertical profiles simulated by WRF model. Environmental Sciences Proceedings, 8(1), 27. https://doi.org/10.3390/ecas2021-10351

    Article  Google Scholar 

  • Drury, S. A., & Berhe, S. M. (1993). Accretion tectonics in northern Eritrea revealed by remotely sensed imagery. Geological Magazine, 130(2), 177–190.

    Google Scholar 

  • Drury, S. A., & De Souza Filho, C. R. (1998). Neoproterozoic terrane assemblages in Eritrea: Review and prospects. Journal of African Earth Sciences, 27(3), 331–348.

    Google Scholar 

  • Dubé, B., Gosselin, P. A., Mercier-Langevin, P., Hannington, M., & Galley, A. (2007). Gold-rich volcanogenic massive sulphide deposits. Geological Association of Canada, Mineral Deposits Division, Special Publication, 5, 75–94.

    Google Scholar 

  • El-Desoky, H. M., Soliman, N., Heikal, M. A., & Abdel-Rahman, A. M. (2021). Mapping hydrothermal alteration zones using ASTER images in the Arabian-Nubian Shield: A case study of the northwestern Allaqi District, South Eastern Desert, Egypt. Journal of Asian Earth Sciences: X, 5, 100060.

    Google Scholar 

  • Filzmoser, P., Hron, K., & Reimann, C. (2009). Principal component analysis for compositional data with outliers. Environmetrics: The Official Journal of the International Environmetrics Society, 20(6), 621–632.

    Google Scholar 

  • Fisher, L., Gazley, M. F., Baensch, A., Barnes, S. J., Cleverley, J., & Duclaux, G. (2014). Resolution of geochemical and lithostratigraphic complexity: a workflow for application of portable X-ray fluorescence to mineral exploration. Geochemistry: Exploration, Environment, Analysis, 14(2), 149–159.

    Google Scholar 

  • Fritz, H., Abdelsalam, M., Ali, K. A., Bingen, B., Collins, A. S., Fowler, A. R., et al. (2013). Orogen styles in the East African Orogen: A review of the Neoproterozoic to Cambrian tectonic evolution. Journal of African Earth Sciences, 86, 65–106.

    Google Scholar 

  • Gale, G. H. (2003). Vectoring volcanogenic massive sulphide deposits using rare earth elements and other pathfinder elements at the Ruttan mine, Manitoba (NTS 63B5). Report of activities, pp. 54–73.

  • Galvão, L. S., Almeida-Filho, R., & Vitorello, Í. (2005). Spectral discrimination of hydrothermally altered materials using ASTER short-wave infrared bands: Evaluation in a tropical savannah environment. International Journal of Applied Earth Observation and Geoinformation, 7(2), 107–114.

    Google Scholar 

  • Gazley, M. F., & Fisher, L. A. (2014). A review of the reliability and validity of portable X-ray fluorescence spectrometry (pXRF) data. In Mineral resource and ore reserve estimation–The AusIMM guide to good practice (vol. 69, p. 82).

  • Gazley, M. F., Caciagli, N., Hood, S. B., & McFarlane, A. (2019). Multivariate data analysis and machine learning – towards robust geometallurgical domains: examples from the Pacific Rim. PACRIM 2019 - Mineral Systems of the Pacific Rim Congress New Zealand, 3.

  • Gazley, M., Hood, S. B., & Cracknell, M. J. (2021). Soil-sample geochemistry normalised by class membership from machine-learnt clusters of satellite and geophysics data. Ore Geology Reviews, 139, 104442.

    Google Scholar 

  • Ghebreab, W., Greiling, R. O., & Solomon, S. (2009). Structural setting of Neoproterozoic mineralization, Asmara district, Eritrea. Journal of African Earth Sciences, 55(5), 219–235.

    Google Scholar 

  • Ghezelbash, R., Daviran, M., Maghsoudi, A., Ghaeminejad, H., & Niknezhad, M. (2023a). Incorporating the genetic and firefly optimization algorithms into K-means clustering method for detection of porphyry and skarn Cu-related geochemical footprints in Baft district, Kerman, Iran. Applied Geochemistry, 148, 105538.

    Google Scholar 

  • Ghezelbash, R., Maghsoudi, A., Shamekhi, M., Pradhan, B., & Daviran, M. (2023b). Genetic algorithm to optimize the SVM and K-means algorithms for mapping of mineral prospectivity. Neural Computing and Applications, 35(1), 719–733.

    Google Scholar 

  • Gorsevski, P. V. (2023). A free web-based approach for rainfall-induced landslide susceptibility modeling: Case study of Clearwater National Forest, Idaho, USA. Environmental Modelling & Software, 161, 105632.

    Google Scholar 

  • Gorsevski, P., Gessler, P., Foltz, R., & Elliot, W. (2006a). Spatial prediction of landslide hazard using logistic regression and ROC analysis. T. GIS, 10, 395–415.

    Google Scholar 

  • Gorsevski, P. V., Jankowski, P., & Gessler, P. E. (2006b). An heuristic approach for mapping landslide hazard by integrating fuzzy logic with analytic hierarchy process. Control and Cybernetics, 35(1), 121–146.

    Google Scholar 

  • Govett, G. J. S., Goodfellow, W. D., Chapman, R. P., & Chork, C. Y. (1975). Exploration geochemistry—Distribution of elements and recognition of anomalies. Journal of the International Association for Mathematical Geology, 7(5), 415–446.

    Google Scholar 

  • Grunsky, E. C. (1986). Recognition of alteration in volcanic rocks using statistical analysis of lithogeochemical data. Journal of Geochemical Exploration, 25(1), 157–183.

    Google Scholar 

  • Grunsky, E. C. (2010). The interpretation of geochemical survey data. Geochemistry: Exploration, Environment, Analysis, 10(1), 27–74.

    Google Scholar 

  • Grunsky, E. C., & de Caritat, P. (2020). State-of-the-art analysis of geochemical data for mineral exploration. Geochemistry: Exploration, Environment, Analysis, 20(2), 217–232.

    Google Scholar 

  • Grunsky, E. C., Mueller, U. A., & Corrigan, D. (2014). A study of the lake sediment geochemistry of the Melville Peninsula using multivariate methods: applications for predictive geological mapping. Journal of Geochemical Exploration, 141, 15–41.

    Google Scholar 

  • Hale, M. (1981). Pathfinder applications of arsenic, antimony and bismuth in geochemical exploration. In A. W. Rose & H. Gundlach (Eds.), Developments in Economic Geology (Vol. 15, pp. 307–323). Berlin: Elsevier. https://doi.org/10.1016/B978-0-444-42012-1.50024-6

    Chapter  Google Scholar 

  • Harris, J. R., Rencz, A. N., Ballantyne, B., & Sheridon, C. (1998). Mapping altered rocks using LANDSAT TM and lithogeochemical data: Sulphurets-Brucejack Lake district, British Columbia, Canada. Photogrammetric engineering and remote sensing, 64(4), 309–322.

    Google Scholar 

  • HarsanyiChang, J. C. C. (1994). Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach. IEEE Transactions on Geoscience and Remote Sensing, 32(4), 779–785.

    Google Scholar 

  • Hawkes, H. E., & Webb, J. S. (1963). Geochemistry in Mineral Exploration. Soil Science, 95(4), 283.

    Google Scholar 

  • Hedenquist, J. W., Arribas, A., & Reynolds, T. J. (1998). Evolution of an intrusion-centered hydrothermal system; Far Southeast-Lepanto porphyry and epithermal Cu-Au deposits, Philippines. Economic Geology, 93(4), 373–404.

    Google Scholar 

  • Hedenquist, J., & Taran, Y. (2013). Modeling the formation of advanced argillic lithocaps: Volcanic vapor condensation above porphyry intrusions. Economic Geology, 108, 1523–1540.

    Google Scholar 

  • Honarmand, M., Ranjbar, H., & Shahabpour, J. (2012). Application of principal component analysis and spectral angle mapper in the mapping of hydrothermal alteration in the Jebal-Barez Area, Southeastern Iran. Resource Geology, 62(2), 119–139.

    Google Scholar 

  • Hood, S. B., Cracknell, M. J., Gazley, M. F., & Reading, A. M. (2019). Improved supervised classification of bedrock in areas of transported overburden: Applying domain expertise at Kerkasha. Eritrea. Applied Computing and Geosciences, 3–4, 100001.

    Google Scholar 

  • Hron, K., Templ, M., & Filzmoser, P. (2010). Imputation of missing values for compositional data using classical and robust methods. Computational Statistics & Data Analysis, 54(12), 3095–3107.

    Google Scholar 

  • Iwasaki, A., & Tonooka, H. (2005). Validation of a crosstalk correction algorithm for ASTER/SWIR. IEEE Transactions on Geoscience and Remote Sensing, 43(12), 2747–2751. Presented at the IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2005.855066.

  • Johnson, P. R., Andresen, A., Collins, A. S., Fowler, A. R., Fritz, H., Ghebreab, W., et al. (2011). Late Cryogenian-Ediacaran history of the Arabian-Nubian Shield: a review of depositional, plutonic, structural, and tectonic events in the closing stages of the northern East African Orogen. Journal of African Earth Sciences, 61(3), 167–232.

    Google Scholar 

  • Johnson, P. R., Zoheir, B. A., Ghebreab, W., Stern, R. J., Barrie, C. T., & Hamer, R. D. (2017). Gold-bearing volcanogenic massive sulfides and orogenic-gold deposits in the Nubian Shield. South African Journal of Geology, 120(1), 63–76.

    Google Scholar 

  • Jolliffe, I. (2011). Principal component analysis (pp. 1094–1096). Springer.

    Google Scholar 

  • Kadel-Harder, I. M., Spry, P. G., McCombs, A. L., & Zhang, H. (2021). Identifying pathfinder elements for gold in bulk-rock geochemical data from the Cripple Creek Au–Te deposit: a statistical approach. Geochemistry: Exploration, Environment, Analysis. https://doi.org/10.1144/geochem2020-048

    Article  Google Scholar 

  • Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and psychological measurement, 20(1), 141–151.

    Google Scholar 

  • Khashgerel, B.-E., Kavalieris, I., & Hayashi, K. (2008). Mineralogy, textures, and whole-rock geochemistry of advanced argillic alteration: Hugo Dummett porphyry Cu–Au deposit, Oyu Tolgoi mineral district, Mongolia. Mineralium Deposita, 43, 913–932.

    Google Scholar 

  • Kruse, F. A., Lefkoff, A. B., Boardman, J. W., Heidebrecht, K. B., Shapiro, A. T., Barloon, P. J., & Goetz, A. F. H. (1993). The spectral image processing system (SIPS)—interactive visualization and analysis of imaging spectrometer data. Remote sensing of environment, 44(2–3), 145–163.

    Google Scholar 

  • Kwarteng, P., & Chavez, A. (1989a). Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis. Photogrammetric Engineering and Remote Sensing, 55(1), 339–348.

    Google Scholar 

  • Livo, K. E., Clark, R. N., & Knepper, D. H. (1993). SPVIEW; spectral plot program for accessing the USGS Digital Spectral Library database with MS-DOS personal computers, version 1.00. Open-File Report. https://doi.org/10.3133/ofr93593A.

  • Loughlin, W. P. (1991). Principal component analysis for alteration mapping. Photogrammetric Engineering and Remote Sensing, 57(9), 1163–1169.

    Google Scholar 

  • Lowell, J. D., & Guilbert, J. M. (1970). Lateral and vertical alteration-mineralization zoning in porphyry ore deposits. Economic geology, 65(4), 373–408.

    Google Scholar 

  • 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.

    Google Scholar 

  • Mars, J. C., & Rowan, L. C. (2010). Spectral assessment of new ASTER SWIR surface reflectance data products for spectroscopic mapping of rocks and minerals. Remote Sensing of Environment, 114(9), 2011–2025.

    Google Scholar 

  • Martín-Fernández, J. A., Hron, K., Templ, M., Filzmoser, P., & Palarea-Albaladejo, J. (2012). Model-based replacement of rounded zeros in compositional data: Classical and robust approaches. Computational Statistics & Data Analysis, 56(9), 2688–2704.

    Google Scholar 

  • Masoumi, F., Eslamkish, T., Honarmand, M., & Abkar, A. A. (2017). A comparative study of landsat-7 and landsat-8 data using image processing methods for hydrothermal alteration mapping: Landsat 7/8 for hydrothermal alteration mapping. Resource Geology, 67(1), 72–88.

    Google Scholar 

  • Mavrogonatos, C., Voudouris, P., Spry, P. G., Melfos, V., Klemme, S., Berndt, J., et al. (2018). Mineralogical study of the advanced argillic alteration zone at the Konos Hill Mo–Cu–Re–Au porphyry prospect, NE Greece. Minerals, 8(11), 479.

    Google Scholar 

  • Milu, V., Milesi, J.-P., & Leroy, J. L. (2004). Rosia Poieni copper deposit, Apuseni Mountains, Romania: Advanced argillic overprint of a porphyry system. Mineralium Deposita, 39, 173–188.

    Google Scholar 

  • Mojeddifar, S., Ranjbar, H., & Nezamabadi-pour, H. (2013). Adaptive Neuro-Fuzzy Inference System application for hydrothermal alteration mapping using ASTER data. Journal of Mining & Environment, 4(2), 83–96.

    Google Scholar 

  • Muntean, J. L., & Einaudi, M. T. (2001). Porphyry-Epithermal Transition: Maricunga Belt, Northern Chile. Economic Geology, 96(4), 743–772.

    Google Scholar 

  • NASA/JPL (2009). ASTER User Advisory (updated: January 14, 2009). https://lpdaac.usgs.gov/news/aster-user-advisory-updated-january-14-2009/. Accessed 16 October 2023.

  • Ndossi, M. I., & Avdan, U. (2016). Inversion of land surface temperature (LST) using terra ASTER data: A comparison of three algorithms. Remote Sensing, 8(12), 993.

    Google Scholar 

  • Ninomiya, Y. (2003). A stabilized vegetation index and several mineralogic indices defined for ASTER VNIR and SWIR data. In International Geoscience and Remote Sensing Symposium (IGARSS) (Vol. 3, p. 1554). https://doi.org/10.1109/IGARSS.2003.1294172.

  • Noori, L., Pour, A. B., Askari, G., Taghipour, N., Pradhan, B., Lee, C.-W., & Honarmand, M. (2019). Comparison of different algorithms to map hydrothermal alteration zones using ASTER remote sensing data for polymetallic vein-type ore exploration: Toroud-Chahshirin Magmatic Belt (TCMB), North Iran. Remote Sensing, 11(5), 495.

    Google Scholar 

  • Nude, P. M., Asigri, J. M., Yidana, S. M., Arhin, E., Foli, G., & Kutu, J. M. (2012). Identifying pathfinder elements for gold in multi-element soil geochemical data from the Wa-Lawra Belt, Northwest Ghana: A multivariate statistical approach. International Journal of Geosciences, 3(1), 62–70.

    Google Scholar 

  • Ostertagová, E., Ostertag, O., & Kováč, J. (2014). Methodology and Application of the Kruskal-Wallis Test. Applied Mechanics and Materials, 611, 115–120.

    Google Scholar 

  • Pearson, N. C., Livo, K. E., Driscoll, R. L., Lowers, H. A., Hoefen, T. M., Swayze, G. A., et al. (2017). USGS Spectral Library Version 7 Data. U.S. Geological Survey. https://doi.org/10.5066/F7RR1WDJ

    Article  Google Scholar 

  • Perelló, J., Brockway, H., & García, A. (2020). A minimum Thanetian (Paleocene) age for the African Surface in the Eritrean highlands, Northeast Africa. Journal of African Earth Sciences, 164, 103782.

    Google Scholar 

  • Perry, J. L., & Vincent, R. K. (2009). ASTER brightness and ratio codes for minerals: application to lithologic mapping in west central Powder river basin, Wyoming. Reviews in Economic Geology, 16, 143–168.

    Google Scholar 

  • Pour, A. B., & Hashim, M. (2012). The application of ASTER remote sensing data to porphyry copper and epithermal gold deposits. Ore Geology Reviews, 44, 1–9.

    Google Scholar 

  • Rajendran, S., & Nasir, S. (2017). Characterization of ASTER spectral bands for mapping of alteration zones of volcanogenic massive sulphide deposits. Ore Geology Reviews, 88, 317–335.

    Google Scholar 

  • Rockwell, B. W., & Hofstra, A. H. (2008). Identification of quartz and carbonate minerals across northern Nevada using ASTER thermal infrared emissivity data—Implications for geologic mapping and mineral resource investigations in well-studied and frontier areas. Geosphere, 4(1), 218–246.

    Google Scholar 

  • Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., & Chica-Rivas, M. (2015). Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Reviews, 71, 804–818.

    Google Scholar 

  • Rowan, L. C., & Mars, J. C. (2003). Lithologic mapping in the Mountain Pass, California area using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. Remote Sensing of Environment, 84(3), 350–366.

    Google Scholar 

  • Ruiz-Armenta, J. R., & Prol-Ledesma, R. M. (1998). Techniques for enhancing the spectral response of hydrothermal alteration minerals in Thematic Mapper images of Central Mexico. International Journal of Remote Sensing, 19(10), 1981–2000.

    Google Scholar 

  • Sadeghi, M., Billay, A., & Carranza, E. J. M. (2015). Analysis and mapping of soil geochemical anomalies: Implications for bedrock mapping and gold exploration in Giyani area, South Africa. Journal of Geochemical Exploration, 154, 180–193.

    Google Scholar 

  • Safari, M., Maghsoudi, A., & Pour, A. B. (2018). Application of Landsat-8 and ASTER satellite remote sensing data for porphyry copper exploration: a case study from Shahr-e-Babak, Kerman, south of Iran. Geocarto international, 33(11), 1186–1201.

    Google Scholar 

  • Salehi, T., & Tangestani, M. (2018). Large-scale mapping of iron oxide and hydroxide minerals of Zefreh porphyry copper deposit, using Worldview-3 VNIR data in the Northeastern Isfahan, Iran. International Journal of Applied Earth Observation and Geoinformation, 73, 156–169.

    Google Scholar 

  • Sander, M. V., & Einaudi, M. T. (1990). Epithermal deposition of gold during transition from propylitic to potassic alteration at Round Mountain, Nevada. Economic Geology, 85(2), 285–311.

    Google Scholar 

  • Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural computation, 13(7), 1443–1471.

    Google Scholar 

  • Sekertekin, A., & Arslan, N. (2019). Monitoring thermal anomaly and radiative heat flux using thermal infrared satellite imagery—A case study at Tuzla geothermal region. Geothermics, 78, 243–254.

    Google Scholar 

  • Sheikhrahimi, A., Pour, A. B., Pradhan, B., & Zoheir, B. (2019). Mapping hydrothermal alteration zones and lineaments associated with orogenic gold mineralization using ASTER data: A case study from the Sanandaj-Sirjan Zone, Iran. Advances in Space Research, 63(10), 3315–3332.

    Google Scholar 

  • Sillitoe, R. H. (2000). Styles of high-sulphidation gold, silver and copper mineralisation in porphyry and epithermal environments. In Proceedings of the Australasian Institute of Mining and Metallurgy (Vol. 305, pp. 19–34).

  • Sinclair, A. J. (1974). Selection of threshold values in geochemical data using probability graphs. Journal of Geochemical Exploration, 3(2), 129–149.

    Google Scholar 

  • Stern, R. J. (1994). Neoproterozoic (900–550 Ma) arc assembly and continental collision in the East Africa orogen: Implications for the consolidation of Gondwanaland. Annual Review of Earth and Planetary Sciences, 22, 319–351.

    Google Scholar 

  • Tangestani, M. H., Mazhari, N., & Agar, B. (2005). Mapping the porphyry copper alteration zones at the Meiduk areas, SE Iran, using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. In SPIE Europe International Symposium of Remote Sensing, 19–22 September, Bruges, Belgium, (vol. 59830, pp. 1–10). https://doi.org/10.1117/12.626505.

  • Tayebi, M. H., Tangestani, M. H., & Vincent, R. K. (2014). Alteration mineral mapping with ASTER data by integration of coded spectral ratio imaging and SOM neural network model. Turkish Journal of Earth Sciences, 23(6), 627–644.

    Google Scholar 

  • Teklay, M., (1997). Petrology, geochemistry, and geochronology of Neoproterozoic magmatic arc rocks from Eritrea: implication for crustal evolution in the southern Nubian Shield. Department of Mines, Eritrea, Memoir 1, p. 125.

  • Templ, M., Hron, K., & Filzmoser, P. (2011). robCompositions: An R-package for Robust Statistical Analysis of Compositional Data. In Compositional Data Analysis (pp. 341–355). John Wiley & Sons, Ltd. https://doi.org/10.1002/9781119976462.ch25.

  • Templ, M., Filzmoser, P., & Reimann, C. (2008). Cluster analysis applied to regional geochemical data: Problems and possibilities. Applied Geochemistry, 23(8), 2198–2213.

    Google Scholar 

  • Testa, F. J., Villanueva, C., Cooke, D. R., & Zhang, L. (2018). Lithological and hydrothermal alteration mapping of epithermal, porphyry and tourmaline breccia districts in the Argentine Andes using ASTER imagery. Remote Sensing, 10(2), 203.

    Google Scholar 

  • Thani-Ashanti. (2013). Eritrea Exploration Memorandum. Dated 14 April 2013.

  • Wang, J., Zhou, Y., & Xiao, F. (2020). Identification of multi-element geochemical anomalies using unsupervised machine learning algorithms: A case study from Ag–Pb–Zn deposits in north-western Zhejiang, China. Applied Geochemistry, 120, 104679.

    Google Scholar 

  • Wang, J., & Zuo, R. (2022). Model averaging for identification of geochemical anomalies linked to mineralization. Ore Geology Reviews, 146, 104955.

    Google Scholar 

  • Watanabe, Y., Aoki, M., & Yamamoto, K. (1997). Geology, age and style of the advanced argillic alteration in the Kobui area, Southwestern Hokkaido, Japan. Resource Geology, 47(5), 263–281.

    Google Scholar 

  • Woldehaimanot, B. (2000). Tectonic setting and geochemical characterization of Neoproterozoic volcanics and granitoids from the Adobha Belt, northern Eritrea. Journal of African Earth Sciences, 30(4), 817–831.

    Google Scholar 

  • Xiong, Y., & Zuo, R. (2016). Recognition of geochemical anomalies using a deep autoencoder network. Computers & Geosciences, 86, 75–82.

    Google Scholar 

  • Xiong, Y., & Zuo, R. (2020). Recognizing multivariate geochemical anomalies for mineral exploration by combining deep learning and one-class support vector machine. Computers & Geosciences, 140, 104484.

    Google Scholar 

  • Yousefi, M., Kamkar-Rouhani, A., & Carranza, E. J. M. (2014). Application of staged factor analysis and logistic function to create a fuzzy stream sediment geochemical evidence layer for mineral prospectivity mapping. Geochemistry: Exploration, Environment, Analysis, 14(1), 45–58. https://doi.org/10.1144/geochem2012-144

    Article  Google Scholar 

  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.

    Google Scholar 

  • Zekri, H., Cohen, D. R., Mokhtari, A. R., & Esmaeili, A. (2019). Geochemical prospectivity mapping through a feature extraction-selection classification scheme. Natural Resources Research, 28(3), 849–865.

    Google Scholar 

  • Zhang, S., Xiao, K., Carranza, E. J. M., Yang, F., & Zhao, Z. (2019). Integration of auto-encoder network with density-based spatial clustering for geochemical anomaly detection for mineral exploration. Computers & Geosciences, 130, 43–56.

    Google Scholar 

  • Zuo, R., Carranza, E. J. M., & Wang, J. (2016). Spatial analysis and visualization of exploration geochemical data. Earth-Science Reviews, 158, 9–18.

    Google Scholar 

  • Zuo, R., Xiong, Y., Wang, J., & Carranza, E. J. M. (2019). Deep learning and its application in geochemical mapping. Earth-Science Reviews, 192, 1–14.

    Google Scholar 

Download references

Acknowledgements

This research stems from the master’s thesis work of Finhas Tsehaye Zerai, who is thankful for the support provided by the School of Earth, Environment and Society at Bowling Green State University. We wish to thank Alpha Exploration Ltd. for providing data for this study, and especially to Mr. Alasdair Smith, Dr. Michael Gazley, Dr. Shawn Wood, and Mr. Tewelde Haile for their support. The authors would like to thank the two anonymous reviewers, who provided helpful suggestions and excellent additions to the manuscript.

Author information

Authors and Affiliations

  1. School of Earth, Environment & Society, Bowling Green State University, Bowling Green, OH, 43403, USA

    Finhas Tsehaye Zerai, Pece V. Gorsevski, Kurt S. Panter & John Farver

  2. Department of Earth Sciences, Faculty of Sciences, Shiraz University, Shiraz, Iran

    Majid H. Tangestani

Authors
  1. Finhas Tsehaye Zerai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pece V. Gorsevski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kurt S. Panter
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. John Farver
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Majid H. Tangestani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Finhas Tsehaye Zerai.

Ethics declarations

Conflict of Interest

The authors declare that they have no known competing financial interests or personal relationships that influenced the work reported in this paper.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 189 kb)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zerai, F.T., Gorsevski, P.V., Panter, K.S. et al. Integration of ASTER and Soil Survey Data by Principal Components Analysis and One-Class Support Vector Machine for Mineral Prospectivity Mapping in Kerkasha, Southwestern Eritrea. Nat Resour Res 32, 2463–2493 (2023). https://doi.org/10.1007/s11053-023-10268-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11053-023-10268-3

Keywords

Search

Navigation