A novel method for extracting information on pores from cast thin-section images

Abstract

Abstract In rock physics and petrological applications, pore identification from cast thin-section (CTS) images is a widely used means of estimating porosity and evaluating types of pores and pore structure parameters. Common problems encountered in current automatic methods of pore extraction from these images are accuracy and/or computational efficiency, especially as the image resolution increases. In high resolution, the transition boundaries between pores and matrices can easily be wrongly identified by automatic extraction methods, which would have significant impacts on the final pore estimation. To address this problem, we propose a revised multiple threshold method combined with error correction and image refining. The method, named ctsPore, is implemented in the hue-saturation-value colour space and comprises three steps: coarse extraction of pores from thin-section images; removal or reduction of incorrectly extracted pores on the surface of identified particle grains and within the transition boundaries between pores and rock matrices; and refinement of extracted pore images by removing unrealistically small areas within identified particle grains or pore regions. The first step applies the threshold method based on the hues of the pixels; the second step is based on the product of saturation and value; and the third step is based on the statistics of small areas. To demonstrate the application of the proposed method, a series of comparison studies were conducted using cyan-, blue- and magenta-impregnated cast thin-section images from the southern margin of the Junggar Basin, China. The results show that ctsPore is an accurate and efficient means of extracting the pore information from high-resolution CTS images impregnated with different colour agents.