Efficient training image selection for multiple-point geostatistics via analysis of contours.

Multiple-point statistics (MPS) methods have emerged as efficient tools for environmental modelling, however their efficiency highly depends on the availability of appropriate training images (TIs). We introduce an efficient method for selecting one compatible TI among a proposed set, based on a measure of compatibility with available conditioning data. While existing approaches to do this consider all available data-events in the simulation grid, we concentrate on a limited number of data-events around the contours and edges of the image. The proposed method is evaluated with different sampling rates, based on hundreds of sample sets extracted from binary, categorical and continuous images, and compared with exhaustive data-event extraction. Our experiments show that the proposed method improves the required CPU-time by up to two orders of magnitude and at the same time leads to a slight improvement in the recognition accuracy. • A method for evaluating compatibility of a sample set with given images is proposed. • For efficient judgment the method mainly concentrates on the image contours. • Ignoring textureless areas reduces computational time and improves accuracy. [ABSTRACT FROM AUTHOR]