Unsupervised model generation for geological events

Abstract So far, large stochastic models require considerable amounts of time to be created. In fact, to simulate systems or events, there is a constant need to perform an analysis of the system and its variables. In this paper we propose a method to automatically generate Stochastic Automata Networks (SAN) models for geological events. Based on user-defined input data, the method creates a model in SAN formalism for the prediction of geological stratal stacking patterns through time. Although models automatically generated tend to be less accurate, we believe that the time saved compensates for the precision lost.