The Influence of Secondary Data Integration on Multiple Point Geo-Statistical Simulation: A Case Study in the Upper Salzach Valley, Austria

Multiple-point geostatistical simulation has recently become popular in stochastic hydrogeology, primarily because of its capability to derive geologically reasonable patterns and multivariate distributions from a training image and conditioning training image data to multiple hard and soft data sources. This article resents, evaluates and contrasts the results of using multiplepoint geostatistical simulation for producing geologically realistic models of a Quaternary inner-alpine aquifer. Borehole data, expert-designed geological profiles and training image data were subject to conditional simulation with the Single Normal Equation Simulation (SNESIM) algorithm, one of the most widely used Matrix Product State (MPS) algorithms. The sensitivity of model predictions to the training image and hard as well as soft data input was evaluated. Modeling results indicate that soft conditioning in MPS is a convenient and efficient way for integrating secondary data such as geological expert drawings.