TBSIM: a computer program for conditional simulation of three-dimensional Gaussian random fields via the turning bands method. The simulation of spatially correlated Gaussian random fields is widespread in geologic, hydrologic and environmental applications for characterizing the uncertainty about the unsampled values of regionalized attributes. In this respect, the turning bands method has received attention among practitioners, for it allows multidimensional simulations to be generated at the CPU cost of one-dimensional simulations. This work provides and documents a set of computer programs for (i) constructing three-dimensional realizations of stationary and intrinsic Gaussian random fields, (ii) conditioning these realizations to a set of data and (iii) back-transforming the Gaussian values to the original attribute units. Such programs can deal with simulations over large domains and handle anisotropic and nested covariance models. The quality of the proposed programs is examined through an example consisting of a non-conditional simulation of a spherical covariance model. The artifact banding in the simulated maps is shown to be negligible when thousands of lines are used. The main parameters of the univariate and bivariate distributions, as well as their expected ergodic fluctuations, also prove to be accurately reproduced.

References in zbMATH (referenced in 16 articles )

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  1. Alegría, Alfredo; Emery, Xavier; Lantuéjoul, Christian: The turning arcs: a computationally efficient algorithm to simulate isotropic vector-valued Gaussian random fields on the (d)-sphere (2020)
  2. Allard, Denis; Emery, Xavier; Lacaux, Céline; Lantuéjoul, Christian: Simulating space-time random fields with nonseparable Gneiting-type covariance functions (2020)
  3. Emery, Xavier; Furrer, Reinhard; Porcu, Emilio: A turning bands method for simulating isotropic Gaussian random fields on the sphere (2019)
  4. Lantuéjoul, Christian; Freulon, Xavier; Renard, Didier: Spectral simulation of isotropic Gaussian random fields on a sphere (2019)
  5. Liu, Yang; Li, Jingfa; Sun, Shuyu; Yu, Bo: Advances in Gaussian random field generation: a review (2019)
  6. 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)
  7. Le Blévec, Thomas; Dubrule, Olivier; John, Cédric M.; Hampson, Gary J.: Geostatistical modelling of cyclic and rhythmic facies architectures (2018)
  8. Wang, Fangfang; Leonenko, Nikolai; Ma, Chunsheng: Isotropic random fields with infinitely divisible marginal distributions (2018)
  9. Zamo, Michaël; Naveau, Philippe: Estimation of the continuous ranked probability score with limited information and applications to ensemble weather forecasts (2018)
  10. Zagayevskiy, Yevgeniy; Deutsch, Clayton V.: Multivariate geostatistical grid-free simulation of natural phenomena (2016)
  11. Chevalier, Clément; Emery, Xavier; Ginsbourger, David: Fast update of conditional simulation ensembles (2015)
  12. Emery, Xavier: Co-simulating total and soluble copper grades in an oxide ore deposit (2012)
  13. Emery, Xavier; Peláez, María: Assessing the accuracy of sequential Gaussian simulation and cosimulation (2011)
  14. Lagos, Guido; Espinoza, Daniel; Moreno, Eduardo; Amaya, Jorge: Robust planning for an open-pit mining problem under ore-grade uncertainty (2011)
  15. Emery, Xavier: Substitution random field with Gaussian and gamma distributions: theory and application to a pollution data set (2008)
  16. Emery, Xavier; Lantuéjoul, Christian: A spectral approach to simulating intrinsic random fields with power and spline generalized covariances (2008)