Modeling regolith thickness in iron formations using machine learning techniques.

• We apply the Random Forest algorithm to predict regolith thickness. • The most important covariates were the drainage density and east–west direction. • The average regolith thickness for the Quadrilátero Ferrífero was 125 m. • The model explained only about 40% of the regolith thickness variation. The Quadrilátero Ferrífero (QF) in Brazil is a region of great economic, social and environmental importance, involving conflicts of interest due to land use and heavily pressured by iron mining and urban sprawl. This is an area of environmental importance due to supporting rupestrian vegetation areas on ferruginous substrates and springs from important watersheds. Thus, studies that can bring more knowledge about this region becomes important to support future decisions based on technical information. We used Random Forests algorithm and several databases to model the regolith thickness of the entire QF region and we also created an individual model to predict regolith thickness in the lithostratigraphic unit Minas Supergroup that contains most of the drillhole samples. The regolith thickness modeled for the QF region presented an average of 125.32 m and R2 of 0.38, and the specific model for the Minas Supergroup also predicted the average regolith thickness of 125.39 m and R2 of 0.39. These values are in accordance with the average regolith thickness (124 m) data from the drillhole database obtained from exploratory programs for iron ore in the QF region. The most important predictive covariates included drainage density, east–west direction, terrain texture and vertical distance from drainage. This study is the first attempt to model the regolith thickness in this important region and the analysis of model uncertainty can orient future studies. [ABSTRACT FROM AUTHOR]