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Application of maximum entropy for mineral prospectivity mapping in heavily vegetated areas of Greater Kurile Chain with Landsat 8 data

2022; Elsevier BV; Volume: 142; Linguagem: Inglês

10.1016/j.oregeorev.2022.104758

1872-7360

S.L. Shevyrev, Emmanuel John M. Carranza,

Soil Geostatistics and Mapping

Exploration for strategic mineral resources, like precious metals, in remote areas of the Greater Kurile Chain is challenging. Climate and weather conditions, dissected relief, soil and forest cover in this region impede acquisition of geological data. Mineral exploration requires development of fast and cost-effective methods that allow processing of insufficient and fragmented data like spaceborne images. Consideration of specific defoliation technique of directed principal components (DPC) and mineral mapping as well as application of assessment technique of Maximum Entropy (MaxEnt, also Logistic Regression, logit) led us to use Landsat 8 OLI for modeling of mineralization distribution in the Kunashir and Iturup Islands. DPC analysis is an image enhancement technique that processes two band ratio images. One of these images contains information of the component of interest (e.g., hydrothermal alteration), the other image contains information related to the spectrally interfering component (e.g., vegetation). Computed DPCs with loadings of opposite signs on either of input images, highlight unique contributions of each band ratio. Therefore, DPC analysis allows to decrease influence of noise related to vegetation. MaxEnt is a general-purpose technique of spatial prognosis based on incomplete data; its key feature is to assess the degree of target presence or absence by approximating the probability distribution of maximum entropy or nearest to uniform distribution based on a set of constraints taken from the incomplete data. Mapping based on DPC analysis in this work aimed to outline hydrothermal alteration associated with known mineralization. Division of the study area into zones according to their mineral prospectivity was then achieved with configured, trained and validated MaxEnt model using the datasets pertaining to a part of the Kunashir Island, which is better studied and explored. The trained predictive model was then applied to the less explored Iturup Island. As a result, the predictive model rendered a mineral prospectivity map, which correctly outlines the locations of the known mineralization. The application of the specific computation techniques to spectral data processing allows assessment of the contribution of Landsat 8 band ratios into the directed principal components and their implementation for cost effective prospectivity mapping including known and probable locations of unknown Au–Ag-bearing zones.