Application of Gaussian Mixture Model and Geostatistical Co-simulation for Resource Modeling of Geometallurgical Variables

Madenova, Y.; Madani, Nasser

doi:10.1007/s11053-020-09802-4

Cited by 12 publications

(2 citation statements)

References 59 publications

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“…Number of parameters in the last Conv layer = ((kernel size) × stride × (number of filters from previous layer) + 1) × (number of filters) (6) Number of parameters in the last Conv Layer = ((3 × 3) × 1 × 32 + 1) × 1 = 289 (7) where the number of filters is equal to 1, so the shape of the output is [(None, 256, 256, 1)] and the total number of all parameters is 63,345.…”

Section: U-net Architecturementioning

confidence: 99%

Addressing Geological Challenges in Mineral Resource Estimation: A Comparative Study of Deep Learning and Traditional Techniques

et al. 2023

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Spatial prediction of orebody characteristics can often be challenging given the commonly complex geological structure of mineral deposits. For example, a high nugget effect can strongly impact variogram modelling. Geological complexity can be caused by the presence of structural geological discontinuities combined with numerous lithotypes, which may lead to underperformance of grade estimation with traditional kriging. Deep learning algorithms can be a practical alternative in addressing these issues since, in the neural network, calculation of experimental variograms is not necessary and nonlinearity can be captured globally by learning the underlying interrelationships present in the dataset. Five different methods are used to estimate an unsampled 2D dataset. The methods include the machine learning techniques Support Vector Regression (SVR) and Multi-Layer Perceptron (MLP) neural network; the conventional geostatistical methods Simple Kriging (SK) and Nearest Neighbourhood (NN); and a deep learning technique, Convolutional Neural Network (CNN). A comparison of geologic features such as discontinuities, faults, and domain boundaries present in the results from the different methods shows that the CNN technique leads in terms of capturing the inherent geological characteristics of given data and possesses high potential to outperform other techniques for various datasets. The CNN model learns from training images and captures important features of each training image based on thousands of calculations and analyses and has good ability to define the borders of domains and to construct its discontinuities.

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Section: U-net Architecturementioning

confidence: 99%

Addressing Geological Challenges in Mineral Resource Estimation: A Comparative Study of Deep Learning and Traditional Techniques

et al. 2023

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“…For example, Yuan et al [17] introduced the Gaussian mixture regression model for quality prediction in multiphase/ multimode processes. Madenova and Madani [18] applied Gaussian mixture model-based clustering for partitioning the Fe ore deposit into the geometallurgical clusters with similar properties. e traditional model, such as t-student and Gaussian mixture models assume that all the mixture components follow the same parametric distribution form, which is almost not the case in the real applications.…”

Section: Introductionmentioning

confidence: 99%

Sequential Truncation of R-Vine Copula Mixture Model for High-Dimensional Datasets

Alanazi

2021

International Journal of Mathematics and Mathematical Sciences

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Uncovering hidden mixture dependencies among variables has been investigated in the literature using mixture R-vine copula models. They provide considerable flexibility for modeling multivariate data. As the dimensions increase, the number of the model parameters that need to be estimated is increased dramatically, which comes along with massive computational times and efforts. This situation becomes even much more complex and complicated in the regular vine copula mixture models. Incorporating the truncation method with a mixture of regular vine models will reduce the computation difficulty for the mixture-based models. In this paper, the tree-by-tree estimation mixture model is joined with the truncation method to reduce computational time and the number of parameters that need to be estimated in the mixture vine copula models. A simulation study and real data applications illustrated the performance of the method. In addition, the real data applications show the effect of the mixture components on the truncation level.

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Spatial Interpolation and Conditional Map Generation Using Deep Image Prior for Environmental Applications

Rakotonirina,

Guridi,

Honeine

et al. 2024

Math Geosci

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Application of Gaussian Mixture Model and Geostatistical Co-simulation for Resource Modeling of Geometallurgical Variables

Cited by 12 publications

References 59 publications

Addressing Geological Challenges in Mineral Resource Estimation: A Comparative Study of Deep Learning and Traditional Techniques

Addressing Geological Challenges in Mineral Resource Estimation: A Comparative Study of Deep Learning and Traditional Techniques

Sequential Truncation of R-Vine Copula Mixture Model for High-Dimensional Datasets

Spatial Interpolation and Conditional Map Generation Using Deep Image Prior for Environmental Applications

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