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Computer stratigraphic simulation models provide a quantitative means to evaluate and understand complex interactions of sedimentary depositional systems. People in the geosciences are quickly advancing in their ability to acquire and interpret large data sets, resulting in major advances in understanding earth systems. Simulation is a natural outcome of these advances, as is the need to integrate and process this information. This volume provides a collection of 26 papers that describe and illustrate the application of some of the latest approaches to stratigraphic-sedimentologic modeling. This paper serves as an overview of these papers, classifies modeling, reviews current issues of modeling, and evaluates possible future modeling directions and opportunities.

We have recognized several different approaches to modeling and present a rational classification for these model types, illustrated here and in the volume by diverse examples. Despite varying philosophies and methodologies of their creators, most models consist of three essential components: (1) input, (2) engine, and (3) output. Our results suggest that models have a sound observational basis (input) and logical foundation (engine), both of which use ever-improving quantitative knowledge of geologic systems. Roles of modeling include: (1) encouraging accuracy and precision in data collection and process interpretation (Slingerland et al., 1994); (2) providing a means to quantitatively test interpretations of the roles of various driving mechanisms to produce sedimentary packages; (3) predicting or extrapolating results into areas of limited control; (4) affording mechanisms for enhanced multidisciplinary integration and communication; (5) gaining new insights to offer nonintuitive results regarding the interaction of parameters; and (6) helping focus future studies to resolve specific problems. The future of modeling is dependent upon fully using improved computational methods and machines, refining quantitative geologic observations and interpretations, and developing rigorous, quantitative approaches to testing, calibrating, verifying, and comparing models.

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