Abstract Mine planning is dependent on the natural lithologic features and on the definition of their limits. The geological model is constantly updated during the life of the mine, based on all the information collected so far, plus the knowledge developed from the exploration stage up to the mine closure. As the mine progresses, the amount of available data increases, as well as the experience of the geological modeller and mine planner who deliver the short, medium, and long-term plans. This classical approach can benefit from the automation of the geological mapping on the mining faces and outcrops, improving the speed of repetitious work and avoiding exposure to intrinsic dangers like mining equipment, falling rocks, high wall proximity, among others. The use of photogrammetry to keep up with surface mining activities boarded in UAVs is a reality and the automated lithological classification using machine learning techniques is a low-cost evolution that might present accuracies above 90% of the contact zones and lithologies based on the automated dense point cloud classification when compared to the manual (or reality) classified model.

Abstract 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. Well-established 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.

A variabilidade do minério in-situ é um dos grandes problemas em todas as etapas pós-lavra, pois comportamentos erráticos de parâmetros físico-químicos podem acarretar grandes dificuldades para o processamento do minério. Pilhas de estoque tem sido utilizadas para reduzir a flutuabilidade de teores, servindo como um filtro da variabilidade. O principal parâmetro para a redução da variabilidade na pilha é a massa de minério estocado. Quanto maior a pilha, menor a variabilidade. Porém há limites viáveis para sua construção. O incorreto dimensionamento leva a perdas financeiras, devido aos altos custos de estoque e às perdas em recuperação e teores na usina de beneficiamento. Esse estudo propõe quantificar a redução da variabilidade de conteúdo de cinza e teor de enxofre em um depósito de carvão. A estimativa de teores em modelos de blocos por métodos tradicionais como krigagem ordinária não considera as incertezas associadas ao modelo geológico. Dessa forma, a utilização de simulação geoestatística é mais adequada para prever flutuações locais dos teores. Para as variáveis testadas, nesse estudo, foram analisadas as reduções de flutuação por meio de diferentes tamanhos de pilhas.

The in-situ grade variability imposes a series of problems during ore processing. Blending is commonly used to reduce this variability. The main factor to reduce head grade variability is the pile mass and size. The larger the pile, the lesser the reclaimed grades vary. However, there are practical limits to the pile length and mass. An incorrect design leads to financial losses due to either high capital costs for oversized piles or losses in plant recovery due to undersized piles incapable of attenuating grade fluctuations. Block estimation by traditional methods, such as ordinary krigging, does not properly incorporate the uncertainty associated with the estimates. Therefore, geostatistical simulation was selected. as it is the best suited to predict local grade fluctuations. The variables used to illustrate the proposed methodology were ash and sulfur content, for which the reduction of the fluctuation was analyzed for different pile sizes.

Esse estudo investiga a capacidade da técnica de perfilagem geofísica para prever a qualidade do carvão. Nesse contexto, alguns parâmetros químicos do carvão foram determinados por meio de análise laboratorial e, posteriormente, eles foram comparados contra registros de perfilagem geofísica (radiação gama natural e resistividade, especificamente). Os resultados mostraram uma forte correlação entre a emissão de radiação gama natural das camadas de carvão e seu teor de cinzas. Isto permitiu estabelecer um modelo linear simples para estimar teores de cinzas, baseando-se nos dados de perfilagem geofísica, com pequeno erro de estimativa (± 5% em média). Além disso, os resultados indicaram que não há correlação entre teor de enxofre ou voláteis e os registros geofísicos. Teores de cinza derivados de valores de gama natural podem ser usados, tal como uma informação secundária para avaliar qualidade de carvão durante a etapa de estimativa de recursos, quando são aplicados métodos geoestatísticos. A metodologia é ilustrada por meio de um estudo de caso em depósito de carvão, localizado na região sul do Brasil.

This study investigates geophysical logging as a tool to predict coal quality. Some of the coal's chemical parameters were determined by laboratory analysis and were compared against values derived from geophysical logging correlation (natural gamma radiation and resistivity versus ash content, specifically). The results showed a strong correlation between the coal's natural gamma emissions and their ash content. From this correlation, a simple linear model was obtained and used to estimate ash grades, directly from geophysical logging records. The error of these predictions is less than ± 5%. Additionally, results showed no correlation between the sulphur grade, or volatile matter, and the geophysical records. Ash grades derived from natural gamma ray values can be usedas secondary information to evaluate coal quality during resource estimation, when combined with lab analysis and appropriate geostatiscal methods. The methodology is illustrated by means of a case study at a coal deposit located in southern Brazil.