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The study of grain size distribution is fundamental for understanding sedimentological environments. Through these analyses, clast erosion, transport and deposition processes can be interpreted and modeled. However, grain size distribution analysis can be difficult in some outcrops due to the number and complexity of the arrangement of clasts and matrix and their physical size. Despite various technological advances, it is almost impossible to get the full grain size distribution (blocks to sand grain size) with a single method or instrument of analysis. For this reason development in this area continues to be fundamental. In recent years, various methods of particle size analysis by automatic image processing have been developed, due to their potential advantages with respect to classical ones; speed and final detailed content of information (virtually for each analyzed particle). In this framework, we have developed a novel algorithm and software for grain size distribution analysis, based on color image segmentation using an entropy-controlled quadratic Markov measure field algorithm and the Rosiwal method for counting intersections between clast and linear transects in the images. We test the novel algorithm in different sedimentary deposit types from 14 varieties of sedimentological environments. The results of the new algorithm were compared with grain counts performed manually by the same Rosiwal methods applied by experts. The new algorithm has the same accuracy as a classical manual count process, but the application of this innovative methodology is much easier and dramatically less time-consuming. The final productivity of the new software for analysis of clasts deposits after recording field outcrop images can be increased significantly. © 2015 Elsevier Ltd
Image segmentation; Optical granulometry; Stereology Deposits; Grain size and shape; Image analysis; Image processing; Image segmentation; Optical data processing; Particle size; Sedimentology; Size distribution; Automatic image processing; Color image segmentation; Grain size distribution; Granulometric analysis; Granulometries; Innovative methodologies; Stereology; Technological advances; Particle size analysis; accuracy assessment; algorithm; clast; complexity; deposition; erosion rate; grain size; granulometry; segmentation; size distribution; software
Optical granulometric analysis of sedimentary deposits by color segmentation-based software: OPTGRAN-CS
Generically Dependent Continuant
Consejo Nacional de Ciencia y Tecnología, CONACYT: 184060
RDF description of Optical granulometric analysis of sedimentary deposits by color segmentation-based software: OPTGRAN-CS - http://orbis.uaslp.mx/vivo/individual/scopus4338
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