Geostatistical Simulation
Mostrando 1-12 de 30 artigos, teses e dissertações.
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1. On the classification and treatment of outliers in a spatial context: A Bayesian Updating approach
Abstract Checking and treating extreme values is commonplace in modelling workflows. The main methods to manage outliers may be categorized into graphical, Kriging- and simulation-based approaches. While graphical methods usually classify outliers from a global perspective, geostatistical methods evaluate outliers in a local context. Ordinary-Kriging based a
REM, Int. Eng. J.. Publicado em: 2021-09
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2. Geostatistical simulations with heterotopic hard and soft data without modeling the linear model of coregionalization
Abstract Most mining decisions are based on models estimated/simulated given the information obtained from samples. During the exploration stage, samples are commonly taken using diamond drill holes which are accurate and precise. These samples are considered hard data. In the production stage, new samples are added. These last are cheaper and more abundant
REM, Int. Eng. J.. Publicado em: 2021-06
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3. NUGGET EFFECT INFLUENCE ON SPATIAL VARIABILITY OF AGRICULTURAL DATA
ABSTRACT Spatial variability description of soil chemical properties by thematic maps depends substantially on suitable geostatistical models. One of the parameters composing a geostatistical model is nugget effect. This study aimed to evaluate the simultaneous influence of nugget effect and sampling design on geostatistical model estimation and estimation o
Eng. Agríc.. Publicado em: 2020-02
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4. Soil physical and hydraulic properties in the Donato stream basin, RS, Brazil. Part 2: Geostatistical simulation
RESUMO A simulação geoestatística tem sido a técnica mais promissora e utilizada para a análise de incertezas de propriedades físico-hidráulicas do solo com grande heterogeneidade espacial. Este estudo realizou uma análise estocástica da condutividade hidráulica saturada (Ksat) e dos parâmetros da curva de retenção de água no solo na bacia do a
Rev. bras. eng. agríc. ambient.. Publicado em: 12/08/2019
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5. 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
REM, Int. Eng. J.. Publicado em: 2019-03
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6. RELATIONSHIP BETWEEN SAMPLE DESIGN AND GEOMETRIC ANISOTROPY IN THE PREPARATION OF THEMATIC MAPS OF CHEMICAL SOIL ATTRIBUTES
ABSTRACT Spatial variability depends on the sampling configuration and characteristics associated with the georeferenced phenomenon, such as geometric anisotropy. This study aimed to determine the influence of the sampling design on parameter estimation in an anisotropic geostatistical model and the spatial estimation of a georeferenced variable at unsampled
Eng. Agríc.. Publicado em: 2018-04
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7. Using multiple random walk simulation in short-term grade models
Abstract Geostatistical simulation comprises a variety of techniques which can help on the decision-making process for uncertainties. They allow the uncertainty assessment of function responses (which depend on the simulated inputs) commonly through a non-linear relationship (net present value, interest tax return, geometallurgical ore recovery...). However,
REM, Int. Eng. J.. Publicado em: 2017-06
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8. Signed distance function implicit geologic modeling
Abstract Prior to every geostatistical estimation or simulation study there is a need for delimiting the geologic domains of the deposit, which is traditionally done manually by a geomodeler in a laborious, time consuming and subjective process. For this reason, novel techniques referred to as implicit modelling have appeared. These techniques provide algori
REM, Int. Eng. J.. Publicado em: 2017-06
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9. A Geostatistical Framework for Estimating Compositional Data Avoiding Bias in Back-transformation
Abstract Estimation of some mineral deposits involves chemical species or a granulometric mass balance that constitute a closed constant sum (e.g., 100%). Data that add up to a constant are known as compositional data (CODA). Classical geostatistical estimation methods (e.g., kriging) are not satisfactory when CODA are used, since bias is expected when estim
Rem: Rev. Esc. Minas. Publicado em: 2016-06
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10. Aperfeiçoamento da estratégia de homogeneização em pilhas chevron utilizando simulação geoestatística = Improving blending strategies in chevron piles using geostatistical simulation / Improving blending strategies in chevron piles using geostatistical simulation
Um dos grandes problemas existentes na indústria mineira são as flutuações no teor do minério, que afetam, significativamente, o desempenho das plantas de beneficiamento. Assim, faz-se necessário um controle dessa variabilidade. Para tanto, pode-se usar combinações de minério provenientes de diferentes frentes de lavra e/ou pilhas de homogeneizaçã
Publicado em: 2010
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11. Simulação geoestatística aplicada ao planejamento de pilhas de homogeneização : um estudo de caso de reconciliação
A lucratividade da indústria mineira é diretamente dependente do planejamento adequado de todas as fases de extração e beneficiamento de minério. Cada passo deste processo por sua vez, também é dependente dos resultados da fase anterior. As usinas de beneficiamento, por exemplo, devem ser alimentadas por um material o mais homogêneo possível, o que
Publicado em: 2010
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12. Estudo comparativo de métodos geoestatísticos de estimativas e simulações estocásticas condicionais / Comparative study of geostatistical estimation methods and conditional stochastic simulations
Different geostatistical methods present themselves as the optimal solution to different realities according to the characteristics displayed by the data in analysis. Some of the most popular estimation methods include ordinary kriging and lognormal ordinary kriging, this last one involving the transformation of data from their original space to a Gaussian d
Publicado em: 2009