High-Order Spatial Simulation Using Legendre-Like Orthogonal Splines.

ABSTRACT: High-order sequential simulation techniques for complex non-Gaussian spatially distributed variables have been developed over the last few years. The high-order simulation approach does not require any transformation of initial data and makes no assumptions about any probability distribution function, while it introduces complex spatial relations to the simulated realizations via high-order spatial statistics. This paper presents a new extension where a conditional probability density function (cpdf) is approximated using Legendre-like orthogonal splines. The coefficients of spline approximation are estimated using high-order spatial statistics inferred from the available sample data, additionally complemented by a training image. The advantages of using orthogonal splines with respect to

SUBMITTER: Minniakhmetov I 

PROVIDER: S-EPMC6411132 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature