Scenario reduction using machine learning techniques applied to conditional geostatistical simulation

Abstract

One of the basic factors in mine operational optimization is knowledge regarding mineral deposit features, which allows to predict its behavior. This could be achieved by conditional geostatistical simulation, which allows to evaluate deposit variability (uncertainty band) and its impacts on project economics. However, a large number of realizations could be computationally expensive when applied in a transfer function. The transfer function that was used in this study was the NPV net present value. Hence, there arises a necessity to reduce the number of realizations obtained by conditional geostatistical simulation in order to make the process more dynamic and yet maintain the uncertainty band. This study made use of machine-learning techniques, such as multidimensional scaling and hierarchical cluster analysis to reduce the number of realizations, based on the Euclidean distance between simulation grids. This approach was tested, generating 100 realizations by the sequential Gaussian simulation method in a database. Proving that similar uncertainty analysis results can be obtained from a smaller number of simulations previously selected by the methodology described in this study, when compared to all simulations.

Assessment of mine slopes stability conditions using a decision tree approach

“…The application of machine learning techniques has been increasing in mining, with positive impacts, in recent years. In addition to the studies already mentioned, some studies with machine learning appli-cations can be highlighted are, e.g., Klen & Lana (2014), Silva et al (2018), Baretta et al (2019), Okada et al (2019), Santos et al (2020). The methodology presented allows different users, target audiences in general, to apply the model quickly and accurately, optimizing decisions in mining operations.…”

Defining optimal drill-hole spacing: A novel integrated analysis from exploration to ore control

“…One concern associated with both simulation-based groups is their excessive computational requirements. Some authors have suggested the use of scenario reduction, where the objective function would be analysed from a subset m' sampled from m based on similarity or dissimilarity conditions (Armstrong et al, 2013;Okada et al, 2019;Usero, Misk, and Saldanha, 2019). The use of scenario reduction, however, is a disputed subject.…”

Constructing Branching Trees of Geostatistical Simulations

“…Arpat (2005), Suzuki and Caers (2008), Scheidt and Caers (2009a) and Scheidt and Caers (2009b) based their approaches on kernel clustering. More recently, Okada et al (2019) incorporated machine learning into their program.…”