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Chile is one of the major producers of copper in the world, and as such is responsible for 1.7 million tons of tailings per day. While the most commonly used deposit to store this type of mining waste is historically tailings sand dams, the mining industry has over the last two decades been inclined toward thickened tailings dams (TTD) because of their advantages in water resource recovery, lower environmental impact, and better physical and chemical stability over conventional deposits. Within the geotechnical area, one key requirement of TDD, is the need to monitor moisture content (w%) during operation, which is today mostly performed in situ - via conventional geotechnical or simple visual means by TTD operators - or off site, via remote sensing. In this work, an intelligent system is proposed that allows estimation of different classes of in-situ states and w% in TTD using Machine learning algorithms based on Artificial Neural Networks (ANN), Support Vector Machine (SVM) and Random Forest (RF). The results show an accuracy of between 94% and 97% in the classification task of the Dry, Semisolid, Plastic and Saturated classes, and between 0.356 and 0.378 of the MAE metric in the regression task, which is sufficient to estimate the w% with ML methods.

Estimation of Moisture Content in Thickened Tailings Dams: Machine Learning Techniques Applied to Remote Sensing Images