Simulating Geological Structures Based on Training Images and Pattern Classifications

P. Switzer, T. Zhang, A. Journel
Department of Geological and Environmental Sciences
Stanford University
CA, 94305, U.S.A
Ph. +01 650 7232879; Fax +01 650 7250979
E-mail: switzer@stanford.edu

Abstract
Local two-dimensional spatial structure, as represented by a training image, can be summarized by a system of filter scores. Local patterns are then classified according to these scores. Sequen- tial point-support simulation proceeds by selecting the score class most resembling the local data and then patching a pattern from this class at the simulation location. This procedure can handle both categorical and continuous variable training images. In addition, because the score space has low dimension, computation is efficient. Examples show spatial simulations derived from training images of sand channels and lithofacies.

Keywords: training images, filters, filter scores, simulations, spatial uncertainty

In: McRoberts, R. et al. (eds).  Proceedings of the joint meeting of The 6th International Symposium On Spatial Accuracy Assessment In Natural Resources and Environmental Sciences and The 15th Annual Conference of The International Environmetrics Society, June 28 – July 1 2004, Portland, Maine, USA.

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