Detecting grain boundaries in deformed rocks using a cellular automata approach

• Published in: Computers & geosciences 2012-05, Vol.42, p.136-142
• Main Authors: Gorsevski, Pece V., Onasch, Charles M., Farver, John R., Ye, Xinyue
• Format: Article
• Language: English
• Subjects: Boundaries; Cellular automata; Computer simulation; computers; Deformation; digital images; Digitization; dynamic models; Edge detection; Extraction; geographic information systems; GIS; Grain boundaries; Grain boundary; Grains; image analysis; information sources; quartz; rocks; Thin section
• ISSN: 0098-3004; 1873-7803
• DOI: 10.1016/j.cageo.2011.09.008

Cellular automata (CA) are widely used in geospatial dynamic modeling and image processing. Here, we explore the application of two-dimensional cellular automata to the problem of grain boundary detection and extraction in digital images of thin sections from deformed rocks. The automated extraction of boundaries, which contain rich sources of information such as shape, orientation, and spatial distribution of grains, involves a CA Moore's neighborhood-based rules approach. The Moore's neighborhood is a 3×3 matrix that is used for changing states by comparing differences between a central pixel and its neighbors. In this dynamic approach, the future state of a pixel depends upon its current state and that of its neighbors. The rules that are defined determine the future state of each cell (i.e., on or off) while the number of iterations to simulate boundaries detection are specified by the user. Each iteration outputs different detection scenarios of grain boundaries that can be evaluated and assessed for accuracy. For a deformed quartz arenite, an r2 of 0.724 was obtained by comparing manually digitized grains to model derived grains. The value of this proposed method is compared against a traditional manual digitization approach and a recent GIS-based method developed for this purpose by Li et al. (2007). ► Grain boundary detection in deformed rocks used GIS-based cellular automata. ► Automated extraction used Moore's neighborhood-based rules for edge detection. ► Different iteration outputs were tested by changing transition rules. ► Model was tested with deformed quartz arenite and quartz wacke. ► Quantitative evaluation compared modeled and manually-digitized polygons.