Resumo

ATIF, I.; CAWOOD, F.T.  e  MAHBOOB, M.A.. Modelling and analysis of the Brumadinho tailings disaster using advanced geospatial analytics. J. S. Afr. Inst. Min. Metall. [online]. 2020, vol.120, n.7, pp.405-414. ISSN 2411-9717.  http://dx.doi.org/10.17159/2411-9717/1196/2020.

On 25 January 2019, one of the most significant and deadliest tailings dam failures in history occurred at Brumadinho Córrego do Feijão iron ore mine in Brazil. Twelve million cubic metres of tailings travelling at 120 km/h destroyed a total of 109 buildings, 36 belonging to Vale and 73 local residences. More than 259 people died. Some farmlands were wiped out and left under a sea of mud and tailings up to 8 m deep. Seven sections of local roads, one main road, and one railway bridge were severely damaged. In this research, a GIS-based tailings spill path (TSP) model was developed using the Python programming language for predicting the potential tailings flow path - before failure of the tailings storage facility (TSF). The pre- and post-failure satellite images of the Brumadinho disaster were processed and analysed to map the damaged infrastructure and to extract digital footprints of the tailings waste and flow path. This model was then compared with the post-failure satellite images. The TSP model is capable of generating the possible path of tailings flow and other important outputs like a processed digital elevation model (DEM), processed satellite image, down-path slope direction, and flow accumulation. The model was tested and validated for the Brumadinho and Samarco tailing disasters. The results are very promising and correlate well with the actual tailings spills. The methodology adopted in this research is robust, advanced, and can be applied to other tailings dams for hazard and risk assessment in case of their possible failure. The lack of high-resolution post-disaster satellite images and other topographical data were the main limitations of this research, which if available, could improve the modelling results.

Palavras-chave : Geospatial modelling; tailings dam failure; Brazil dam collapse; mining disaster; Brumadinho; Samarco tailings; tailings management.

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