Predicting and Analyzing Rock Mechanical Properties Using Image Processing Techniques

Predicting the mechanical properties of rocks is a critical technical issue in the fields of geological engineering design, disaster prevention, and resource exploration. Traditional macroscopic physical experimental methods face many limitations in analyzing rock mechanical properties, struggling to meet the current demands for efficiency, low cost, and microscopic level analysis. This study is based on image processing technology, aiming to improve the accuracy and efficiency of predicting rock mechanical properties through the quantitative analysis of high-resolution microscopic images of rocks. Although the application of image processing technology in the field of rock mechanics has made some progress, existing methods still face challenges in accuracy and automation when segmenting microscopic images of rocks. Considering these shortcomings, this paper proposes a novel rock microscopic image segmentation strategy that combines the minimum threshold method, Laplacian histogram method, and maximum interclass variance method. Additionally, this study explores methods for extracting microscopic structural parameters of rocks and analyzes the relationship between these parameters and rock mechanical properties. The results indicate that the proposed methods effectively improve the accuracy of identifying microscopic structures of rocks, thereby enhancing the understanding of rock mechanical behavior, which has substantial significance for scientific decision-making in geological engineering.