Microscopic image segmentation of sand grains can assist geological assessment， but it poses challenges to the accuracy of segmentation due to its variety and complex features. For such images， a segmentation method with enhanced tuna swarm optimization exponential entropy （ETSO-EXP） was proposed， which could effectively preserve the texture features of various sand grains. First of all， aiming at some deficiencies of the tuna swarm optimization （TSO） algorithm in global search and local development， a chaotic disturbance strategy， a dynamic weight strategy and a cosine disturbance strategy were proposed to enhance it. The benchmark function experiment showed that the ETSO greatly improved the convergence accuracy and slightly increased the convergence speed. Secondly， the ETSO algorithm was used to determine the segmentation threshold of the EXP， and the feasibility of the scheme was verified by taking the information content of the segmented image as the standard. Finally， a segmentation experiment was carried out on the Yarlung Zangbo River sand microscopic image dataset. Compared with the TSO-EXP， the image of the ETSO-EXP segmentation has a better peak signal-to-noise ratio， structural similarity， feature similarity and the optimization speed has been improved by 18.78%， 6.85%， 4.16% and 3.83%， respectively， and the performance is the best among the similar segmentation methods. The results show that the segmentation method with the ETSO-EXP has high segmentation accuracy and calculation speed for images with high contrast， rich texture or large differences in the size of sand debris.