砂样图像岩屑自动分割提取方法

通过将砂样图像进行单颗粒分割，识别砂样成分，可显著提高砂样岩性分析的准确性和效率。现有的砂样图像分割方法主要以传统分水岭算法和卷积神经网络为主，但由于对单颗粒岩屑轮廓细节提取不足，误分割率高。本文提出一种以图像融合算法为桥梁，将卷积神经网络和分水岭算法相结合的单颗粒图像分割提取方法。首先利用改进的Mask R-CNN网络快速分割砂样原图，获得其初分割图像；然后，将初分割图像与砂样原图进行融合，再使用改进的分水岭算法对融合结果进行分割；最后，利用砂样原图坐标点匹配方法，将分水岭分割得到的结果图像进行修正，完成单颗粒岩屑图像提取。实验结果表明，本文的单颗粒自动分割提取方法准确率高达96.77%,且模型更轻量和精准，为岩屑图像分割提供了一种可行且有效的方法，可满足有效测算油藏层构造变化、查找潜在沉积物源及储层动态变化的需求。

Automatic segmentation and extraction method for rock debris in sandstone sample images

By segmenting the sand sample image into single particles and identifying its components, the accuracy and efficiency of sand sample lithology analysis can be significantly improved. The existing methods for sand sample image segmentation mainly rely on traditional watershed algorithms and convolutional neural networks, but due to insufficient extraction of details from single particle rock debris contours, the mis-segmentation rate is high. Therefore, this paper proposes a single particle image segmentation and extraction method that combines convolutional neural networks and watershed algorithms, using image fusion algorithms as a bridge. Firstly, an improved Mask R-CNN network is used to quickly segment the original sand sample image and obtain its initial segmented image; Then, the initial segmented image is fused with the original sand sample image, and an improved watershed algorithm is used to segment the fusion results; Finally, using the coordinate point matching method of the original sand sample image, the resulting image obtained from watershed segmentation is corrected to complete the extraction of single particle rock debris images. The experimental results show that the accuracy of the single particle automatic segmentation and extraction method proposed in this paper is as high as 96.77%, and the model is lightweight and precise, providing a feasible and effective method for rock debris image segmentation, which can meet the needs of effectively calculating structural changes in oil reservoirs, searching for potential sediment sources, and dynamic changes of reservoirs.