The sequential spectral turning band simulator as an alternative to Gibbs sampler in large truncated- or pluri- Gaussian simulations

The Sequential Spectral Turning Bands Method (S-STBM) builds Gaussian random fields (GRF) calibrated to desired response functions. An interesting application of S-STBM concerns the simulation of GRF subject to inequality constraints. S-STBM works by choosing the phase of each cosine function of the STBM algorithm instead of perturbating nodes of the GRF many thousand times using conditional distributions as in Gibbs sampler. Each chosen phase increasingly constrains the nodes to the desired inequalities. A method based on the sequential Gaussian simulation is introduced to accelerate convergence at the end of the process. Examples shown compare S-STBM approach to Gibbs sampler. Orders of magnitude reduction in computation time is achieved with our spectral method. Furthermore, examples show that the phase selection has no significant influence on the spatial correlation. Our approach is easily generalized to pluriGaussian simulations. Compared to Gibbs sampler, S-STBM is not limited to small systems (no memory limitation) and its complexity of O(n) makes it an efficient tool to simulate large GRF subject to inequality constraints.

[1]  D. Allard,et al.  Non-parametric diagrams for plurigaussian simulations of lithologies , 2012 .

[2]  Jens Christian Refsgaard,et al.  Review of strategies for handling geological uncertainty in groundwater flow and transport modeling , 2012 .

[3]  Xavier Emery,et al.  Assessing the accuracy of sequential Gaussian simulation and cosimulation , 2011 .

[4]  Denis Marcotte,et al.  Half-tapering strategy for conditional simulation with large datasets , 2016, Stochastic Environmental Research and Risk Assessment.

[5]  Y. Liu,et al.  Updating multipoint simulations using the ensemble Kalman filter , 2013, Comput. Geosci..

[6]  Gregoire Mariethoz,et al.  Which Path to Choose in Sequential Gaussian Simulation , 2017, Mathematical Geosciences.

[7]  Denis Marcotte,et al.  Spatial turning bands simulation of anisotropic non-linear models of coregionalization with symmetric cross-covariances , 2016, Comput. Geosci..

[8]  Margaret Armstrong,et al.  Plurigaussian Simulations in Geosciences , 2014 .

[9]  Nicolas Desassis,et al.  PluriGaussian Simulations with the Stochastic Partial Differential Equation (SPDE) Approach , 2019 .

[10]  H. Rue,et al.  An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach , 2011 .

[11]  Phillipp Kaestner,et al.  Linear And Nonlinear Programming , 2016 .

[12]  D. Allard Simulating a Geological Lithofacies with Respect to Connectivity Information Using the Truncated Gaussian Model , 1994 .

[13]  Xavier Emery,et al.  Testing the correctness of the sequential algorithm for simulating Gaussian random fields , 2004 .

[14]  Diogo S. F. Silva,et al.  Multiple imputation framework for data assignment in truncated pluri-Gaussian simulation , 2017, Stochastic Environmental Research and Risk Assessment.

[15]  Omid Asghari,et al.  Assessing the accuracy of sequential gaussian simulation through statistical testing , 2017, Stochastic Environmental Research and Risk Assessment.

[16]  Mickaele Le Ravalec-Dupin,et al.  GRADUAL DEFORMATION OF BOOLEAN SIMULATIONS , 2005 .

[17]  Thomas Tran,et al.  Improving variogram reproduction on dense simulation grids , 1994 .

[18]  X. Emery,et al.  Spectral simulation of vector random fields with stationary Gaussian increments in d-dimensional Euclidean spaces , 2017, Stochastic Environmental Research and Risk Assessment.

[19]  Clayton V. Deutsch,et al.  A multidimensional scaling approach to enforce reproduction of transition probabilities in truncated plurigaussian simulation , 2013, Stochastic Environmental Research and Risk Assessment.

[20]  Omid Asghari,et al.  Application of plurigaussian simulation to delineate the layout of alteration domains in Sungun copper deposit , 2013 .

[21]  A. Galli,et al.  An Application of the Truncated Pluri-gaussian Method for Modeling Geology , 2006 .

[22]  Masanobu Shinozuka,et al.  Simulation of Multivariate and Multidimensional Random Processes , 1971 .

[23]  C. Fouquet,et al.  Conditioning a Gaussian model with inequalities , 1993 .

[24]  J. Chilès,et al.  Geostatistics: Modeling Spatial Uncertainty , 1999 .

[25]  X. Emery,et al.  An improved spectral turning-bands algorithm for simulating stationary vector Gaussian random fields , 2016, Stochastic Environmental Research and Risk Assessment.

[26]  Christian Lantuéjoul,et al.  TBSIM: A computer program for conditional simulation of three-dimensional Gaussian random fields via the turning bands method , 2006, Comput. Geosci..

[27]  H. Rue,et al.  Fitting Gaussian Markov Random Fields to Gaussian Fields , 2002 .

[28]  Denis Marcotte,et al.  TASC3D: A program to test the admissibility in 3D of non-linear models of coregionalization , 2015, Comput. Geosci..

[29]  Xavier Emery,et al.  Simulating Large Gaussian Random Vectors Subject to Inequality Constraints by Gibbs Sampling , 2014, Mathematical Geosciences.

[30]  Christian Lantuéjoul,et al.  Simulation of a Gaussian random vector: A propagative version of the Gibbs sampler , 2012 .

[31]  Denis Marcotte,et al.  Calibration of random fields by a sequential spectral turning bands method , 2020, Comput. Geosci..

[32]  D. Marcotte,et al.  Calibration of categorical simulations by evolutionary gradual deformation method , 2018, Computational Geosciences.

[33]  Alain Galli,et al.  The Pros and Cons of the Truncated Gaussian Method , 1994 .

[34]  Z. Botev The normal law under linear restrictions: simulation and estimation via minimax tilting , 2016, 1603.04166.

[35]  Francesca Greco,et al.  Integrated Groundwater Management , 2015 .

[36]  O. Dubrule,et al.  Geostatistical Modelling of Cyclic and Rhythmic Facies Architectures , 2018, Mathematical Geosciences.

[37]  H. Beucher,et al.  Truncated Gaussian and derived methods , 2016 .

[38]  Yanhui Zhang Ensemble Methods of Data Assimilation in Porous Media Flow for Non-Gaussian Prior Probability Density , 2015 .

[39]  X. Emery,et al.  Simulation of geo-domains accounting for chronology and contact relationships: application to the Río Blanco copper deposit , 2015, Stochastic Environmental Research and Risk Assessment.

[40]  Denis Marcotte,et al.  Calibration of random fields by FFTMA-SA , 2019, Comput. Geosci..

[41]  Denis Marcotte,et al.  Gibbs sampling on large lattice with GMRF , 2018, Comput. Geosci..