Sequential Updating Simulation

Abstract

This paper presents a new implementation of the sequential simulation principle, within a multi-Gaussian framework. In this approach, the local conditional distribution functions, from which simulated values are drawn by Monte-Carlo, are updated iteratively rather than re-estimated at each step. This new implementation offers several significant advantages: the local distribution functions, from which simulated values are drawn, are conditional to all hard and previously simulated data, rather than to data within a search neighbourhood only; there is no need to assign existing hard data to the nearest grid nodes; the local means and variances are estimated from the available data at their exact locations; and the updating process does not involve any longer the solving of a linear system of equations. This, in turns, relaxes the constrains on the spatial correlation models which can be used. This new approach is illustrated by a case study in soil contamination.KeywordsPolycyclic Aromatic HydrocarbonGrid NodeVariogram ModelPolycyclic Aromatic Hydrocarbon ConcentrationSequential SimulationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.