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 32 articles , 1 standard article )

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  1. Sandra de Iaco: The cgeostat Software for Analyzing Complex-Valued Random Fields (2017)
  2. Sun, Wenyue; Durlofsky, Louis J.: A new data-space inversion procedure for efficient uncertainty quantification in subsurface flow problems (2017)
  3. Eikrem, Kjersti Solberg; Nævdal, Geir; Jakobsen, Morten; Chen, Yan: Bayesian estimation of reservoir properties -- effects of uncertainty quantification of 4D seismic data (2016)
  4. Lamghari, Amina; Dimitrakopoulos, Roussos: Network-flow based algorithms for scheduling production in multi-processor open-pit mines accounting for metal uncertainty (2016)
  5. Vo, Hai X.; Durlofsky, Louis J.: Regularized kernel PCA for the efficient parameterization of complex geological models (2016)
  6. He, Jiajun; Durlofsky, L.J.: Constraint reduction procedures for reduced-order subsurface flow models based on POD-TPWL (2015)
  7. 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)
  8. Montiel, Luis; Dimitrakopoulos, Roussos: Optimizing mining complexes with multiple processing and transportation alternatives: an uncertainty-based approach (2015)
  9. Nejadi, Siavash; Leung, Juliana; Trivedi, Japan: Characterization of non-Gaussian geologic facies distribution using ensemble Kalman filter with probability weighted re-sampling (2015)
  10. Ţene, Matei; Wang, Yixuan; Hajibeygi, Hadi: Adaptive algebraic multiscale solver for compressible flow in heterogeneous porous media (2015)
  11. Mustapha, Hussein; Chatterjee, Snehamoy; Dimitrakopoulos, Roussos: CDFSIM: efficient stochastic simulation through decomposition of cumulative distribution functions of transformed spatial patterns (2014)
  12. Rousset, Matthieu A.H.; Huang, Chung K.; Klie, Hector; Durlofsky, Louis J.: Reduced-order modeling for thermal recovery processes (2014)
  13. Straubhaar, Julien; Malinverni, Duccio: Addressing conditioning data in multiple-point statistics simulation algorithms based on a multiple grid approach (2014)
  14. Tan, Xiaojin; Tahmasebi, Pejman; Caers, Jef: Comparing training-image based algorithms using an analysis of distance (2014)
  15. Vo, Hai X.; Durlofsky, Louis J.: A new differentiable parameterization based on principal component analysis for the low-dimensional representation of complex geological models (2014)
  16. Walker, Matthew; Curtis, Andrew: Varying prior information in Bayesian inversion (2014)
  17. Wang, Yixuan; Hajibeygi, Hadi; Tchelepi, Hamdi A.: Algebraic multiscale solver for flow in heterogeneous porous media (2014)
  18. Stright, Lisa; Bernhardt, Anne; Boucher, Alexandre: DFTopoSim: modeling topographically-controlled deposition of subseismic scale sandstone packages within a mass transport dominated deep-water channel belt (2013)
  19. Iglesias, Marco A.; McLaughlin, Dennis: Data inversion in coupled subsurface flow and geomechanics models (2012)
  20. Fernández-Martínez, J.L.; Mukerji, T.; García-Gonzalo, E.; Fernández-Muñiz, Z.: Uncertainty assessment for inverse problems in high dimensional spaces using particle swarm optimization and model reduction techniques (2011)

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