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 43 articles , 1 standard article )

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  1. Cusini, Matteo; Fryer, Barnaby; van Kruijsdijk, Cor; Hajibeygi, Hadi: Algebraic dynamic multilevel method for compositional flow in heterogeneous porous media (2018)
  2. 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)
  3. Trehan, Sumeet; Durlofsky, Louis J.: Machine-learning-based modeling of coarse-scale error, with application to uncertainty quantification (2018)
  4. Volkov, Oleg; Bukshtynov, Vladislav; Durlofsky, Louis J.; Aziz, Khalid: Gradient-based Pareto optimal history matching for noisy data of multiple types (2018)
  5. Yao, Lingqing; Dimitrakopoulos, Roussos; Gamache, Michel: A new computational model of high-order stochastic simulation based on spatial Legendre moments (2018)
  6. de Moraes, Rafael J.; Rodrigues, José R. P.; Hajibeygi, Hadi; Jansen, Jan Dirk: Multiscale gradient computation for flow in heterogeneous porous media (2017)
  7. Sandra de Iaco: The cgeostat Software for Analyzing Complex-Valued Random Fields (2017) not zbMATH
  8. Sun, Wenyue; Durlofsky, Louis J.: A new data-space inversion procedure for efficient uncertainty quantification in subsurface flow problems (2017)
  9. Eikrem, Kjersti Solberg; Nævdal, Geir; Jakobsen, Morten; Chen, Yan: Bayesian estimation of reservoir properties -- effects of uncertainty quantification of 4D seismic data (2016)
  10. Emerick, Alexandre Anozé: Towards a hierarchical parametrization to address prior uncertainty in ensemble-based data assimilation (2016)
  11. Lamghari, Amina; Dimitrakopoulos, Roussos: Network-flow based algorithms for scheduling production in multi-processor open-pit mines accounting for metal uncertainty (2016)
  12. Li, Hangyu; Durlofsky, Louis J.: Ensemble level upscaling for compositional flow simulation (2016)
  13. Vo, Hai X.; Durlofsky, Louis J.: Regularized kernel PCA for the efficient parameterization of complex geological models (2016)
  14. He, Jiajun; Durlofsky, L. J.: Constraint reduction procedures for reduced-order subsurface flow models based on POD-TPWL (2015)
  15. Mejer Hansen, Thomas; Skou Cordua, Knud; Mosegaard, Klaus: A general probabilistic approach for inference of Gaussian model parameters from noisy data of point and volume support (2015)
  16. Montiel, Luis; Dimitrakopoulos, Roussos: Optimizing mining complexes with multiple processing and transportation alternatives: an uncertainty-based approach (2015)
  17. Nejadi, Siavash; Leung, Juliana; Trivedi, Japan: Characterization of non-Gaussian geologic facies distribution using ensemble Kalman filter with probability weighted re-sampling (2015)
  18. Ţene, Matei; Wang, Yixuan; Hajibeygi, Hadi: Adaptive algebraic multiscale solver for compressible flow in heterogeneous porous media (2015)
  19. Vo, Hai X.; Durlofsky, Louis J.: Data assimilation and uncertainty assessment for complex geological models using a new PCA-based parameterization (2015)
  20. Mustapha, Hussein; Chatterjee, Snehamoy; Dimitrakopoulos, Roussos: CDFSIM: efficient stochastic simulation through decomposition of cumulative distribution functions of transformed spatial patterns (2014)

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