Case study Optical granulometric analysis of sedimentary deposits by color segmentation-based software: OPTGRAN-CS G. Moreno Chávez a,1 , D. Sarocchi b,n , E. Arce Santana c , L. Borselli b a Doctorado Institucional en Ingeniería y Ciencias de Materiales, c/o Instituto de Geología UASLP, Av. Dr. M. Nava No. 5, Zona Universitaria, 78290 San Luis Potosí, México b Instituto de Geología/Facultad de Ingeniería UASLP, Av. Dr. M. Nava No. 5, Zona Universitaria, 78290 San Luis Potosí, México c Facultad de Ciencias, UASLP, Diagonal Sur S/N, Zona Universitaria, 78290 San Luis Potosí, México article info Article history: Received 4 June 2015 Received in revised form 5 September 2015 Accepted 8 September 2015 Available online 12 September 2015 Keywords: Optical granulometry Image segmentation Stereology abstract 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 mod- eled. 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 fun- damental. 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. All rights reserved. 1. Introduction One of the most important textural characteristics of a rock consisting of clasts or sediments is the size of the clasts that compose it; that is, their granulometric distribution and the de- gree of uniformity of clast sizes. The importance that geologists has always given to this textural feature is amply demonstrated by the enormous amount of literature on the subject (Boudon et al., 1993; Allen, 1997). The size of the particles constituting a rock or a deposit is strongly related to their origin and affected by transport and de- position mechanisms. Detailed study of this property can therefore provide information of utmost importance for understanding the nature of the rock itself and the events that originated it (Folk, 1966; Glenn, 1969; Pettijohn, 1987; Allen, 1997; Nichols, 2009). However, to obtain the whole granulometric distribution of an outcrop is, in many cases, a complicated task. Sedimentary de- posits are often characterized by a wide range of grain sizes, ran- ging from a few microns to several meters (Gee and Bauder, 1986; Lirer and Vinci, 1991; Valsangkar, 1992). In many fields of sedimentology such as volcanic sedimentol- ogy, until the 1980s and also in recent times, most sedimentolo- gical studies were based only on granulometric data obtained by sieving (Walker, 1971; Yamazaki et al., 1973; Lajoie et al., 1989; Saucedo et al., 2002), with few exceptions (Freundt and Schmincke, 1986). In more recent times the extreme granulometric classes of distributions have also been begun to be considered, and rightly so. Thin tails were analyzed using sedimentographic scanning (Lewis and Mcconchie, 1994; Konert and Vandenberghe, 1997; Beuselinck et al., 1998) or stream scanning (Allen, 1997, Kaye et al., 1999) methods, and the coarser tails were analyzed using image Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/cageo Computers & Geosciences http://dx.doi.org/10.1016/j.cageo.2015.09.007 0098-3004/& 2015 Elsevier Ltd. All rights reserved. n Corresponding author. Fax: þ52 444 8111741. E-mail addresses: gamalielmch@gmail.com (G.M. Chávez), sarocchi@gmail.com (D. Sarocchi), arce@fciencias.uaslp.mx (E.A. Santana), lborselli@gmail.com (L. Borselli). 1 Fax: þ52 444 8111741. Computers & Geosciences 85 (2015) 248–257