The main goal was the analysis of parameters describing the structure of the pore space of carbonate rocks, based on tomographic images. The results of CT images interpretation, made for 17 samples of Paleozoic carbonate rocks were shown. The qualitative and quantitative analysis of a pore system was performed. Objects were clustered according to the pore size. Within the clusters, the geometry parameters were analysed. The following dependences were obtained for carbonate rocks, also for individual clusters (due to the volume): (1) a linear relationship (on a bilogarithmic scale) between the specific surface and the Feret diameter and (2) a strong linear relationship between specific surface area and Feret diameter and average diameter of the objects calculated for the sphere. The results were then combined with available results from standard laboratory tests, including NMR and MICP.

The present paper discusses the essential tenets of the method whose purpose is to enable effective search of images of given rock in multimedia databases. The search is based exclusively on an image request, to which the system’s response is a set of images presenting visually similar rocks. The images that constitute the basis of the discussed research had been registered with an optical microscope. The collection of images that were used in the process of performing measurements encompassed 5700 digital images presenting 19 rock types. The proposed method is based on the application of image analysis and artificial intelligence concepts. The very process of inference, in turn, makes use of the methods of data classification and grouping. In the paper, the authors demonstrate that these may turn out to be effective mathematical methods, successfully applied to the problem of image search, performed with imagings presenting rock textures. The discussed system concept, based on a feature space defined by the authors, successfully matches up images with the reference standard. The effectiveness rate of that process is very high (very often, it is 100 %). Failed classifications concern only the images which differ visually—in a considerable way—from the rest of the images within a given group. The proposed system concept is to facilitate the decision-making process involved in determining the similarity of investigated objects. In the opinion of the authors, it meets the requirements—and, as such, can be applied to the problem of searching for images in databases, searching discs in order to find images of given rocks and automatic information gain on the basis of video sequences, e.g., in order to find frames presenting particular rock structures.