Results 1 to 10 of about 31,794 (205)
Identification of high-permeability subsurface structures with multiple point geostatistics and normal score ensemble Kalman filter [PDF]
Journal of Hydrology, 2017 Alluvial aquifers are often characterized by the presence of braided high-permeable paleo-riverbeds, which constitute an interconnected preferential flow network whose localization is of fundamental importance to predict flow and transport dynamics ...
Matteo Camporese +2 more
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GSTools v1.3: a toolbox for geostatistical modelling in Python [PDF]
Geoscientific Model Development, 2022 Geostatistics as a subfield of statistics accounts for the spatial correlations encountered in many applications of, for example, earth sciences. Valuable information can be extracted from these correlations, also helping to address the often encountered
S. Müller +8 more
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Applied Computing and Geosciences, 2022 Multiple-point statistics algorithms allow modeling spatial variability from training images. Among these techniques, the Direct Sampling (DS) algorithm has advanced capabilities, such as multivariate simulations, treatment of non-stationarity, multi ...
Przemysław Juda +2 more
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Projection Pursuit Multivariate Sampling of Parameter Uncertainty
Applied Sciences, 2022 The efficiency of sampling is a critical concern in Monte Carlo analysis, which is frequently used to assess the effect of the uncertainty of the input variables on the uncertainty of the model outputs.
Oktay Erten +2 more
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GStatSim V1.0: a Python package for geostatistical interpolation and conditional simulation [PDF]
Geoscientific Model Development, 2023 The interpolation of geospatial phenomena is a common problem in Earth science applications that can be addressed with geostatistics, where spatial correlations are used to constrain interpolations.
E. J. MacKie +9 more
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An Attempt to Boost Posterior Population Expansion Using Fast Machine Learning Algorithms
Frontiers in Artificial Intelligence, 2021 In hydrogeology, inverse techniques have become indispensable to characterize subsurface parameters and their uncertainty. When modeling heterogeneous, geologically realistic discrete model spaces, such as categorical fields, Monte Carlo methods are ...
Przemysław Juda +2 more
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Mining, 2022 This study illustrates the application of conditional simulations to calculate the uncertainty associated with the thickness of bauxite ores. The bauxite deposit of Rondon do Pará in northern Pará State, Brazil, is characterized by a well-defined ...
Saulo B. de Oliveira +2 more
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Assessment of uncertainty for coal quality-tonnage curves through minimum spatial cross-correlation simulation [PDF]
International Journal of Mining and Geo-Engineering, 2018 Coal quality-tonnage curves are helpful tools in optimum mine planning and can be estimated using geostatistical simulation methods. In the presence of spatially cross-correlated variables, traditional co-simulation methods are impractical and time ...
Babak Sohrabian +2 more
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Geostatistics and its potential in Agriculture 4.0
Revista Ciência Agronômica, 2021 In order to meet future food demands and ensure sustainability new technologies have been incorporated into agriculture. Some researchers believe that we are living in the fourth agricultural revolution or Agriculture 4.0.
Marcos Sales Rodrigues +4 more
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Comparison of three recent discrete stochastic inversion methods and influence of the prior choice
Comptes Rendus. Géoscience, 2022 Groundwater flow depends on subsurface heterogeneity, which often calls for categorical fields to represent different geological facies. The knowledge about subsurface is however limited and often provided indirectly by state variables, such as hydraulic
Juda, Przemysław +2 more
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