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Lingqing Yao’s PhD research addressed the major challenges in the mining industry of quantifying geological uncertainty of mineral deposits through developing state-of-the-art methods of high-order stochastic simulation and statistical machine learning. Specifically, this required spatial modeling of the related attributes of a mineral deposit. These models acted as inputs for stochastic optimization of industrial mining complexes, accounting for material supply uncertainty from mines and quantifying risk in production forecasting. Lingqing developed advanced stochastic simulation methods based on statistical machine learning to support mining decision-making under geological uncertainty. Currently, he conducts research on novel approaches characterizing complex spatial features of mineral deposits through high-order spatial statistics and high-order stochastic simulation models founded on random field theory, spatial modeling and prediction of geological attributes based on machine learning methods, data analytics and optimization methods in statistical learning.
Cahiers du GERAD
Training image free high-order stochastic simulation based on aggregated kernel statistics
A training-image free, high-order sequential simulation method is proposed herein, which is based on the efficient inference of high-order spatial statistics...BibTeX reference
A new computational model of high-order stochastic simulation based on spatial Legendre moments
Multiple-point simulations have been introduced over the past decade to overcome the limitations of second-order stochastic simulations in dealing with geolo...BibTeX reference