GSLIB

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.

Keywords for this software

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References in zbMATH (referenced in 216 articles )

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  1. Gómez-Hernández, J. Jaime; Srivastava, R. Mohan: One step at a time: the origins of sequential simulation and beyond (2021)
  2. Goovaerts, Pierre: From natural resources evaluation to spatial epidemiology: 25 years in the making (2021)
  3. Hansen, Thomas Mejer: Entropy and information content of geostatistical models (2021)
  4. Zhang, Chengkai; Song, Xianzhi; Azevedo, Leonardo: U-net generative adversarial network for subsurface facies modeling (2021)
  5. Calderon, Hernan; Santibañez, Felipe; Silva, Jorge F.; Ortiz, Julián M.; Egaña, Alvaro: Geological facies recovery based on weighted (\ell_1)-regularization (2020)
  6. Claudia Cappello, Sandra De Iaco, Donato Posa: covatest: An R Package for Selecting a Class of Space-Time Covariance Functions (2020) not zbMATH
  7. de Figueiredo, Leandro Passos; Grana, Dario; Le Ravalec, Mickaele: Revisited formulation and applications of FFT moving average (2020)
  8. McKenna, Sean A.; Akhriev, Albert; Echeverría Ciaurri, David; Zhuk, Sergiy: Efficient uncertainty quantification of reservoir properties for parameter estimation and production forecasting (2020)
  9. Pedretti, Daniele: Heterogeneity-controlled uncertain optimization of pump-and-treat systems explained through geological entropy (2020)
  10. Pereira, Pedro; Calçôa, Inês; Azevedo, Leonardo; Nunes, Rúben; Soares, Amílcar: Iterative geostatistical seismic inversion incorporating local anisotropies (2020)
  11. Pradhan, Anshuman; Mukerji, Tapan: Seismic Bayesian evidential learning: estimation and uncertainty quantification of sub-resolution reservoir properties (2020)
  12. Rabinovich, Avinoam; Cheng, Kan Bun: Equilibrium gravity segregation in porous media with capillary heterogeneity (2020)
  13. Luo, Xin; Tjelmeland, Håkon: Prior specification for binary Markov mesh models (2019)
  14. Luo, Xin; Tjelmeland, Håkon: A multiple-try Metropolis-Hastings algorithm with tailored proposals (2019)
  15. Ma, Wei; Jafarpour, Behnam: Assessing multiple-point statistical facies simulation behavior for effective conditioning on probabilistic data (2019)
  16. Ma, Wei; Jafarpour, Behnam: Integration of soft data into multiple-point statistical simulation: re-assessing the probability conditioning method for facies model calibration (2019)
  17. Nunes, Ruben; Azevedo, Leonardo; Soares, Amílcar: Fast geostatistical seismic inversion coupling machine learning and Fourier decomposition (2019)
  18. Silva, Diogo; Deutsch, Clayton: Multivariate categorical modeling with hierarchical truncated pluri-Gaussian simulation (2019)
  19. 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)
  20. Turco, Francesco; Azevedo, Leonardo; Herold, Dan: Geostatistical interpolation of non-stationary seismic data (2019)

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