The Stanford Geostatistical Modeling Software (SGeMS). S-GeMS: the Stanford geostatistical modeling software: a tool for new algorithms development. S-GeMS (Stanford Geostatistical Modeling Software) is a new crossplatform software for geostatistics. Capitalizing on the flexibility of the C++ Geostatistical Template Library (GsTL), it offers the more common geostatistics algorithms, such as kriging of one or more variables, sequential and multiple-point simulations. This software was developed with two aims in mind: be reasonably comprehensive and user-friendly, and serve as a development platform into which new algorithms can easily be integrated. S-GeMS is indeed built around a system of plug-ins which allow new geostatistical algorithms to be integrated, import/export filters to be added, new griding systems to be used such as unstructured grids. The S-GeMS source code is made available to everyone to use and modify. It can be freely copied and redistributed.

References in zbMATH (referenced in 62 articles , 1 standard article )

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  1. Claudia Cappello, Sandra De Iaco, Donato Posa: covatest: An R Package for Selecting a Class of Space-Time Covariance Functions (2020) not zbMATH
  2. Jahandideh, Atefeh; Jafarpour, Behnam: Closed-loop stochastic oilfield optimization for hedging against geologic, development, and operation uncertainty (2020)
  3. Kostakis, Filippos; Mallison, Bradley T.; Durlofsky, Louis J.: Multifidelity framework for uncertainty quantification with multiple quantities of interest (2020)
  4. McKinley, Jennifer M. (ed.); Atkinson, Peter M. (ed.): A special issue on the importance of geostatistics in the era of data science (2020)
  5. Pedretti, Daniele: Heterogeneity-controlled uncertain optimization of pump-and-treat systems explained through geological entropy (2020)
  6. Rasera, Luiz Gustavo; Gravey, Mathieu; Lane, Stuart N.; Mariethoz, Gregoire: Downscaling images with trends using multiple-point statistics simulation: an application to digital elevation models (2020)
  7. Tang, Meng; Liu, Yimin; Durlofsky, Louis J.: A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems (2020)
  8. Yao, Lingqing; Dimitrakopoulos, Roussos; Gamache, Michel: High-order sequential simulation via statistical learning in reproducing kernel Hilbert space (2020)
  9. Arnold, Dan; Demyanov, Vasily; Rojas, Temistocles; Christie, Mike: Uncertainty quantification in reservoir prediction. I: Model realism in history matching using geological prior definitions (2019)
  10. de Carvalho, Joao Pedro; Dimitrakopoulos, Roussos; Minniakhmetov, Ilnur: High-order block support spatial simulation method and its application at a gold deposit (2019)
  11. Demyanov, Vasily; Arnold, Dan; Rojas, Temistocles; Christie, Mike: Uncertainty quantification in reservoir prediction. II: Handling uncertainty in the geological scenario (2019)
  12. Liu, Yang; Li, Jingfa; Sun, Shuyu; Yu, Bo: Advances in Gaussian random field generation: a review (2019)
  13. Liu, Yimin; Sun, Wenyue; Durlofsky, Louis J.: A deep-learning-based geological parameterization for history matching complex models (2019)
  14. Ma, Wei; Jafarpour, Behnam: Integration of soft data into multiple-point statistical simulation: re-assessing the probability conditioning method for facies model calibration (2019)
  15. Ma, Wei; Jafarpour, Behnam: Assessing multiple-point statistical facies simulation behavior for effective conditioning on probabilistic data (2019)
  16. SantibaƱez, Felipe; Silva, Jorge F.; Ortiz, JuliƔn M.: Sampling strategies for uncertainty reduction in categorical random fields: formulation, mathematical analysis and application to multiple-point simulations (2019)
  17. Cusini, Matteo; Fryer, Barnaby; van Kruijsdijk, Cor; Hajibeygi, Hadi: Algebraic dynamic multilevel method for compositional flow in heterogeneous porous media (2018)
  18. Rizzo, Calogero B.; de Barros, Felipe P. J.; Perotto, Simona; Oldani, Luca; Guadagnini, Alberto: Adaptive POD model reduction for solute transport in heterogeneous porous media (2018)
  19. Trehan, Sumeet; Durlofsky, Louis J.: Machine-learning-based modeling of coarse-scale error, with application to uncertainty quantification (2018)
  20. Volkov, Oleg; Bukshtynov, Vladislav; Durlofsky, Louis J.; Aziz, Khalid: Gradient-based Pareto optimal history matching for noisy data of multiple types (2018)

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