Artículo de publicación ISIStochastic simulation is increasingly used to map the spatial variability in the grades of elements of interest and to assess the uncertainty in the mineral resources and ore reserves. The practical implementation requires specifying a stochastic model, which describes the spatial distribution of the grades, and an algorithm to construct realizations of these grades, viewed as different possible outcomes or scenarios. In the case of the Gaussian random field model, a variety of algorithms have been proposed in the past decades, but their ability to reproduce the model statistics is often unequal. In this paper, we compare two such algorithms, namely the turning bands and the sequential algorithms. The comparison is hold through a synthetic case study and a real case study in a porphyry copper deposit located in southeastern Iran, in which it is of interest to jointly simulate the copper, molybdenum, silver, lead and zinc grades. Statistical testing and graphical validations are realized to check whether or not the realizations reproduce the features of the true grades, in particular their direct and cross variograms. Sequential simulation based on collocated cokriging turns out to poorly reproduce the cross variograms, while turning bands proves to be accurate in all the analyzed cases.Chilean Commission for Scientific and Technological Research 113008

The autonomous vehicle motion prediction literature is reviewed. Motion prediction is the most challenging task in autonomous vehicles and self-drive cars. These challenges have been discussed. Later on, the state-of-theart has reviewed based on the most recent literature and the current challenges are discussed. The state-of-the-art consists of classical and physical methods, deep learning networks, and reinforcement learning. prons and cons of the methods and gap of the research presented in this review. Finally, the literature surrounding object tracking and motion will be presented. As a result, deep reinforcement learning is the best candidate to tackle self-driving cars.

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The main objective of this study is to provide a practical and near-optimal mine production schedule for block caving operations considering operational uncertainty. The problem is defined in the context of goal programming optimization to meet the operational objectives, including tonnage and grade as daily production targets. The considered operational constraints include drawpoints and ore pass design, draw rate, mine production, and transportation capacities in different operational levels, tonnage and grade constraints, and mine production targets in the presence of several mining sectors. The developed model is verified and validated using historical operational data obtained from an actual block caving operation. The practicality and flexibility of the framework are examined through three different operational scenarios and compared with the real block caving operation mine plans and historical production data.

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