GSLIB is an acronym for Geostatistical Software LIBrary. This name was originally used for a collection of geostatistical programs developed at Stanford University over the last 15 years. The original GSLIB inspired the writing of GSLIB: Geostatistical Software Library and User’s Guide by Clayton Deutsch and André Journel, 1992, 340 pp. during 1990 - 1992. The second edition was completed in 1997. Both editions were published by Oxford University Press.

References in zbMATH (referenced in 139 articles )

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  1. Deng, Q.; Ginting, V.; McCaskill, B.; Torsu, P.: A locally conservative stabilized continuous Galerkin finite element method for two-phase flow in poroelastic subsurfaces (2017)
  2. Emerick, Alexandre A.: Investigation on principal component analysis parameterizations for history matching channelized facies models with ensemble-based data assimilation (2017)
  3. Fossum, Kristian; Mannseth, Trond: Coarse-scale data assimilation as a generic alternative to localization (2017)
  4. Galerne, Bruno; Leclaire, Arthur: Texture inpainting using efficient Gaussian conditional simulation (2017)
  5. Ibrahima, Fayadhoi; Tchelepi, Hamdi A.: Multipoint distribution of saturation for stochastic nonlinear two-phase transport (2017)
  6. Sandra de Iaco: The cgeostat Software for Analyzing Complex-Valued Random Fields (2017)
  7. van den Boogaart, K.Gerald; Mueller, Ute; Tolosana-Delgado, Raimon: An affine equivariant multivariate normal score transform for compositional data (2017)
  8. Vigsnes, Maria; Kolbjørnsen, Odd; Hauge, Vera Louise; Dahle, Pål; Abrahamsen, Petter: Fast and accurate approximation to Kriging using common data neighborhoods (2017)
  9. Wambeke, T.; Benndorf, J.: A simulation-based geostatistical approach to real-time reconciliation of the grade control model (2017)
  10. Chatterjee, Snehamoy; Sethi, Manas Ranjan; Asad, Mohammad Waqar Ali: Production phase and ultimate pit limit design under commodity price uncertainty (2016)
  11. Goovaerts, Pierre; Albuquerque, M.T.D.; Antunes, I.M.H.R.: A multivariate geostatistical methodology to delineate areas of potential interest for future sedimentary gold exploration (2016)
  12. Perozzi, Lorenzo; Gloaguen, Erwan; Giroux, Bernard; Holliger, Klaus: A stochastic inversion workflow for monitoring the distribution of $\textCO_2$ injected into deep saline aquifers (2016)
  13. Tarrahi, Mohammadali; Elahi, Siavash Hakim; Jafarpour, Behnam: Fast linearized forecasts for subsurface flow data assimilation with ensemble Kalman filter (2016)
  14. Thenon, Arthur; Gervais, Véronique; Le Ravalec, Mickaële: Multi-fidelity meta-modeling for reservoir engineering - application to history matching (2016)
  15. Zagayevskiy, Yevgeniy; Deutsch, Clayton V.: Multivariate geostatistical grid-free simulation of natural phenomena (2016)
  16. Barnett, Ryan M.; Deutsch, Clayton V.: Multivariate imputation of unequally sampled geological variables (2015)
  17. Kim, Hyoung-Moon; Yoon, Young Joo; Kim, Hea-Jung: Optimal classifier for multivariate rectangle-screened normal data classification (2015)
  18. Li, Weidong; Zhang, Chuanrong; Willig, Michael R.; Dey, Dipak K.; Wang, Guiling; You, Liangzhi: Bayesian Markov chain random field cosimulation for improving land cover classification accuracy (2015)
  19. Nejadi, Siavash; Leung, Juliana; Trivedi, Japan: Characterization of non-Gaussian geologic facies distribution using ensemble Kalman filter with probability weighted re-sampling (2015)
  20. Skou Cordua, Knud; Mejer Hansen, Thomas; Mosegaard, Klaus: Improving the pattern reproducibility of multiple-point-based prior models using frequency matching (2015)

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