Point-based Model for Predicting Mineral Deposit Using GIS and Machine Learning
نویسندگان
چکیده
In this paper we present a novel Point-based pattern analysis for predicting Cassiterite (tin-ore) as a secondary mineral deposit in the Plateau Younger Granite Region of Nigeria (PYGR) using statistics, spatial analysis and machine learning techniques. Existing mining data points collected from field survey in the PYGR and the geological map of the region were digitized and converted into shape files using Arc-GIS. Spatial analysis was conducted using a distance distribution method to investigate the spatial mineral distribution patterns as well as correlations between mineral deposit points and geological features. A combination of spatial evidence map layers created using GIS represent the structure of the mineralisation indicators or attributes. Binary indicators are used to build a predictive model using machine learning techniques to predict the presence or absence of mineral deposits in the PYGR. Key words—GIS, machine learning, spatial analysis, younger granites, mineral deposit.
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