The current developments with unmanned aerial vehicles ('UAVs') are revolutionizing many fields in civil applications, such as agriculture, environmental and visual inspections. The mining industry can also benefit from UAVs in many aspects, and the reconciliation through topographic control is an example. In comparison with traditional topography and maybe modern techniques such as laser scanning, aerial photogrammetry is cheaper, provides faster data acquisition and processing, and generates several high-quality products and impressive level of details in the outputs. However, despite the quality of the software currently available, there is an uncertainty intrinsic to the surfaces acquired by photogrammetry and this discrepancy needs to be assessed in order to validate the techniques applied. To understand the uncertainty, different surfaces were generated by UAVs for a small open pit quarry in southern Brazil. Wellestablished topographic surveying methodologies were used for geolocation support and comparison, namely the RTK (real-time kinetic) global navigation satellite system (GNSS) (here called conventional method) and laser scanning. The results showed consistency between the UAV surfaces with a few outliers in when vegetation, water and mobile objects during the flight missions. In comparison with the laser-scanned surface, the UAV results were less erratic surrounding the RTK points, showing that surfaces generated by photogrammetry can be a simpler and quicker alternative for mining reconciliation, presenting low uncertainty when compared to conventional methods.

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The common practice of reconciliation is based on definition of the mine call factor (MCF) ResumoA prática comum de reconciliação baseia-se na definição do mine call factor (MCF) e sua aplicação às estimativas dos modelos de longo e de curto prazo. O MCF expressa a diferença entre a produção prevista pelos modelos e a produção registrada na usina e, portanto, sua aplicação permite corrigir futuras estimativas. Esta é uma prática de reconciliação reativa. Entretanto a aplicação desses fatores às estimativas dos modelos pode mascarar as causas dos erros responsáveis pelas discrepâncias observadas. As causas reais de qualquer variân-cia só podem ser identificadas analisando-se as informações referentes a cada variância e, em seguida, modificando metodologias e processos. Este é o conceito de prognosticação, ou reconciliação pró-ativa, um processo iterativo de recalibração constante dos dados de entrada e dos cálculos. Portanto a prognosticação permite uma correção das metodologias de coleta de dados, e não, simplesmente, uma correção das estimativas dos modelos. O presente trabalho analisa as práticas de reconciliação realizadas em uma mina de ouro no Brasil e sugere um novo protocolo de amostragem, com base nos conceitos de prognosticação. Palavras-chave:Reconciliação, prognosticação, amostragem.

The definition of the search neighbourhood in kriging can have a significant impact on the resulting estimates. Stationary domains are usually estimated using a unique search strategy for the entire domain. However, the use of a global search neighbourhood ignores the local variations within each domain, i.e. all blocks are interpolated using a unique search strategy. In this paper, localised kriging parameter optimisation (LKPO) is proposed as an alternative methodology that considers the best 'local estimation parameter settings' block by block. The optimisation process is based on absolute error minimisation obtained in crossvalidation. Two datasets are presented, the first is a synthetic mineral deposit (2D) and the second is a gold deposit (3D). A wide variety of validation checks show that the use of local kriging parameters significantly improves the grade estimation, obtaining more precise and accurate results than the methodologies currently available in the geostatistical literature.

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