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 211 articles )

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  1. Zhang, Chengkai; Song, Xianzhi; Azevedo, Leonardo: U-net generative adversarial network for subsurface facies modeling (2021)
  2. Calderon, Hernan; Santibañez, Felipe; Silva, Jorge F.; Ortiz, Julián M.; Egaña, Alvaro: Geological facies recovery based on weighted (\ell_1)-regularization (2020)
  3. Claudia Cappello, Sandra De Iaco, Donato Posa: covatest: An R Package for Selecting a Class of Space-Time Covariance Functions (2020) not zbMATH
  4. de Figueiredo, Leandro Passos; Grana, Dario; Le Ravalec, Mickaele: Revisited formulation and applications of FFT moving average (2020)
  5. McKenna, Sean A.; Akhriev, Albert; Echeverría Ciaurri, David; Zhuk, Sergiy: Efficient uncertainty quantification of reservoir properties for parameter estimation and production forecasting (2020)
  6. Pereira, Pedro; Calçôa, Inês; Azevedo, Leonardo; Nunes, Rúben; Soares, Amílcar: Iterative geostatistical seismic inversion incorporating local anisotropies (2020)
  7. Pradhan, Anshuman; Mukerji, Tapan: Seismic Bayesian evidential learning: estimation and uncertainty quantification of sub-resolution reservoir properties (2020)
  8. Rabinovich, Avinoam; Cheng, Kan Bun: Equilibrium gravity segregation in porous media with capillary heterogeneity (2020)
  9. Luo, Xin; Tjelmeland, Håkon: Prior specification for binary Markov mesh models (2019)
  10. Luo, Xin; Tjelmeland, Håkon: A multiple-try Metropolis-Hastings algorithm with tailored proposals (2019)
  11. Ma, Wei; Jafarpour, Behnam: Assessing multiple-point statistical facies simulation behavior for effective conditioning on probabilistic data (2019)
  12. Ma, Wei; Jafarpour, Behnam: Integration of soft data into multiple-point statistical simulation: re-assessing the probability conditioning method for facies model calibration (2019)
  13. Nunes, Ruben; Azevedo, Leonardo; Soares, Amílcar: Fast geostatistical seismic inversion coupling machine learning and Fourier decomposition (2019)
  14. Silva, Diogo; Deutsch, Clayton: Multivariate categorical modeling with hierarchical truncated pluri-Gaussian simulation (2019)
  15. Talebi, Hassan; Mueller, Ute; Tolosana-Delgado, Raimon; van den Boogaart, K. Gerald: Geostatistical simulation of geochemical compositions in the presence of multiple geological units: application to mineral resource evaluation (2019)
  16. Turco, Francesco; Azevedo, Leonardo; Herold, Dan: Geostatistical interpolation of non-stationary seismic data (2019)
  17. Yang, Xiu; Barajas-Solano, David; Tartakovsky, Guzel; Tartakovsky, Alexandre M.: Physics-informed cokriging: a Gaussian-process-regression-based multifidelity method for data-model convergence (2019)
  18. Emerick, Alexandre A.: Deterministic ensemble smoother with multiple data assimilation as an alternative for history-matching seismic data (2018)
  19. Gueting, Nils; Caers, Jef; Comunian, Alessandro; Vanderborght, Jan; Englert, Andreas: Reconstruction of three-dimensional aquifer heterogeneity from two-dimensional geophysical data (2018)
  20. Ibrahima, Fayadhoi; Tchelepi, Hamdi A.; Meyer, Daniel W.: An efficient distribution method for nonlinear two-phase flow in highly heterogeneous multidimensional stochastic porous media (2018)

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