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Small-scale mining usually operates under high geological uncertainty conditions. This turns mine planning into a complex and sometimes inaccurate task, resulting in low productivity and substantial variability in the quantity and quality of the mineral products. This research demonstrates how the application of a novel methodology that relies on traditional and low-cost geophysical methods can contribute to mine planning in small-scale mining. A combination of resistivity and induced polarization methods is applied to enhance mine planning decision-making in three small-scale mining operations. This approach allows for the acquisition of new data regarding local geological settings, supporting geological modelling and enhancing decision-making processes for mine planning in a timely and low-cost fashion. The results indicate time savings of up to 77% and cost reductions of up to 94% as compared with conventional methods, contributing to more effective mine planning and, ultimately, improving sustainability in small-scale mining.

The complex combination of controls, systems and human aspects presented in the mine value chain are today responsible for an increasing amount of digital data in the mining industry. In this scenario, it is imperative the use of reliable and intelligent systems that can store and process the data to predict mine equipment performance. The objective of this study is to improve the prediction of operational performance in mine equipment for the short-term planning. For this purpose, it is proposed a machine learning (ML) methodology to map the production process from data collection until planning, with replication of the generated routines for subsequent short-term period analysis. The methodology was applied to predict the operational performance of excavators during working shifts in an open pit copper mine located in Northern Brazil, considering a series of variables such as operational, geological, geographic, maintenance. 175 predictive models were generated during the study, which were tested through cross-validation to improve the model adjustment to the collected data. The results obtained using this methodology confirmed that the use of ML predictive models provides a better understanding of the operation and allocation of mine loading fleet through the use of fast and realistic predictive routines.