“There has been an important breakthrough in the field of geological assessment. Researchers have proposed an enhanced tuna swarm optimization index entropy segmentation method (ETSO-EXP) to address the challenge of segmenting microscopic images of sand particles. This method can effectively preserve the texture features of various sand particles, providing a more accurate image segmentation method for geological assessment. The researchers first addressed the shortcomings of the Tuna Swarm Optimization (TSO) algorithm and proposed chaotic perturbation strategy, dynamic weight strategy, and cosine interference strategy for enhancement. Experiments have shown that these improvements have significantly improved the convergence accuracy and speed of ETSO. Subsequently, the researchers applied ETSO to determine the segmentation threshold of EXP and verified the feasibility of the scheme through information quality standards. The experimental results on the the Yarlung Zangbo River sand microscopic image dataset show that, compared with TSO-EXP, ETSO-EXP has achieved significant improvements in peak signal to noise ratio, structural similarity, feature similarity and optimization speed. This research achievement not only demonstrates the superior performance of ETSO-EXP in sand micro image segmentation, but also provides a new solution for the field of geological assessment. This method has high segmentation accuracy and computational speed for processing images with high contrast, rich texture, or significant differences in sand particle size, providing strong technical support for geological assessment.”