Implementation of conditional simulation by successive residuals

Abstract

Conditional simulation of ergodic and stationary Gaussian random fields using successive residuals is a new approach used to overcome the size limitations of the LU decomposition algorithm as well as provide fast updating of existing simulated realizations with new data. This paper discusses two different implementations of this approach. The implementations differ in the use of the new information available; in the first implementation new information is partially used to generate updated realizations; however, in the second implementation, the realizations are updated using all the new information available. The implementations are validated using the Walker Lake data set, and compared through a case study at a stockwork gold deposit.