Porosity of the porous carbonate rocks in the Jingfengqiao–Baidiao area based on finite automata

Abstract

This study is based on the processing of computed microtomography images of rock samples. In this study, a finite automation is constructed using the grey value, red-green-blue (RGB) value and Euler number of polarized images of carbonate rocks from the Jingfengqiao–Baidiao area. The finite automaton is used to perform black and white binary processing of the polarized images of the carbonate rocks. The porosity of the carbonate rock is calculated based on the black and white binarization processing results of the polarized images of the carbonate rocks. The obtained porosity is compared with the carbonate porosity obtained by use of the traditional carbonate research method. When the two porosities are close, the image processing threshold of the finite automata is considered to be credible. Based on the finite automata established using the image processing threshold, the black and white binary images of the polarized images of the carbonate rocks are used to establish a rock pore image using I mage J2X. The polarized images of the carbonate rocks are classified according to their RGB values using the finite automata for the porosity classification, and the obtained images are used as textures to paste onto a cube to construct a three-dimensional data model of the carbonate rocks. This study also uses 16S rDNA analysis to verify the formation mechanism of the carbonate pores in the Jingfengqiao–Baidiao area. The results of the 16S rDNA analysis show that the pores in the carbonate rocks in the Jingfengqiao–Baidiao area are closely related to microorganisms, represented by denitrifying bacteria.

Karst study of Jinfo Mountain based on image analysis

“…c (4) A binary map of the pores obtained using the gray-value, in which the white areas have been changed to blue. c (5) The carbonate pore binary map obtained using the Jinping finite automaton [ 40 ], in which the white areas have been changed to blue to distinguish them from the background color. c (6) A binary map of the carbonate rock pores obtained using the Jinping finite automaton [ 40 ], in which the white areas have been changed to blue to distinguish them from the background color.…”

“…Some studies have integrated image analysis technology with three-dimensional (3D) modeling technology to study carbonate rocks [ [33] , [34] , [35] , [36] , [37] ]. Other studies have shown that image binarization is promising in the study of carbonate rock images [ [38] , [39] , [40] , [41] , [42] ]. Additionally, research results have shown the importance of comparing the results of multiple research methods with those of TCRM [ [43] , [44] , [45] , [46] , [47] ].…”

Research on climate change based on carbonate porosity analysis in Jinping, China

“…Since a given polarized image of a carbonate rock is different from the other images, the standard gray-scale formula may not be suitable for use with polarized images of carbonate rocks. For this reason, finite automata were used in this study [ 26 ].…”