Conditional Spectral Simulation with Phase Identification

@inproceedings{YaoRM1998a,
 abstract = { Spectral simulation is widely used in electrical engineering to generate random fields with a specified spectrum or covariance model. The spectral representation theorem allows generalization to 3D simulation. The algorithm is particularly fast when based on Fast Fourier Transform (FFT). However, because oflack of phase identification spectral simulation could not be conditioned to local data. Conditioning is typically done by adding a simulated kriging error which calls for one kriging system per node and de tracts from the strict elegance of the spectral approach. A new algorithm is proposed whereby the simulated field is perturbed to identify the data values at their locations, phases are recalculated and associated with the original amplitudes which carry the covariance information. A new simulated field is then generated and the process is iterated until both phases and amplitudes match jointly the constraints, that is a covariance model and local data values. The algorithm need not caU for traditional analytical covariance models. The amplitudes can be read directly from a licit spectral table, itself modeled by FFT of the original experimental covariance table. A case study is presented to illustrate the method. },
 author = { Yao, Tingting AND JOURNEL, André },
 booktitle = { 17th gOcad Meeting },
 month = { "june" },
 publisher = { ASGA },
 title = { Conditional Spectral Simulation with Phase Identification },
 year = { 1998 }
}