【目的】油页岩中有机质的密度远低于其他岩石基质,因此,在CT图像中有机质的灰度值往往接近于孔隙裂隙的灰度值,从而在图像中表现为灰度值差异不明显,有机质和岩石的边界模糊等问题。【方法】为了精准识别分割出油页岩CT图像中的有机质,对深度学习领域的图像分割方法进行研究,并自主搭建了描述有机质分割的OM-Unet语义分割网络架构。通过在传统Unet模型中引入混合空洞卷积模块、由粗到精的部署策略和轻量化自适应特征融合模块,利用卷积神经网络识别分割油页岩CT图像中的有机质,并结合MIoU等评价指标对其分割效果进行评估。【结果】OM-Unet模型的MIoU为80.66%,相较于三相分割方法、Unet、CBAM-Unet、Deep-LabV3、HDC-Unet和LAFF-Unet模型分别增加了8.01%、17.68%、9.5%、2.54%、2.83%和9.13%.OM-Unet模型的MPA为89.16%,相较于三相分割方法、Unet、CBAM-Unet、Deep-LabV3、HDC-Unet和LAFF-Unet模型分别增加了12.85%、20.62%、15.82%、8.81%、9.55%和15.34%.【结论】该结果证明OM-Unet模型可有效提高油页岩有机质分割的准确性,更加精确地确定有机质体积百分比、有机质团数量随温度或者热解条件的变化规律,为油页岩原位开发提供基础理论数据。

【Purposes】 The density of organic matter in oil shale is much lower than that of other rock matrix, so the gray value of organic matter in CT images is often close that of pore fractures, which results in problems such as inconspicuous difference in gray value and blurred boundary between organic matter and rock in the images. In order to accurately identify the or-ganic matter in the segmented oil shale CT images, the image segmentation methods in the field of deep learning are studied, and the OM-Unet semantic segmentation network architectures de-scribing the organic matter segmentation is built independently. 【Methods】 By introducing a hy-brid null convolution module, a coarse-to-fine deployment strategy, and a lightweight adaptive feature fusion module into the traditional Unet model, the convolutional neural network is used to identify and segment organic matter in oil shale CT images, and its segmentation effect is eval-uated by combining MIoU and other evaluation indexes. 【Findings】 The MIoU of the OM-Unet model is 80.66%, which is higher than that of the three-phase segmentation methods, Unet, CBAM-Unet, DeepLabV3, HDC-Unet, and LAFF-Unet models by 8.01%, 17.68%, 9.5%, 2.54%, 2.83%, and 9.13%, respectively. The MPA of OM-Unet model is 89.16%, which is higher than that of the three-phase segmentation method, Unet, CBAM-Unet, DeepLabV3, HDC-Unet, and LAFF-Unet models by 12.85%, 20.62%, 15.82%, 8.81%, 9.55%, and 15.34%, respectively. 【Conclusions】 The results demonstrate that the OM-Unet model can effec-tively improve the accuracy of oil shale organic matter partitioning, more accurately determine the variation patterns of organic matter volume percentage and organic matter cluster number with temperature or pyrolysis conditions, and provide basic theoretical data for in situ oil shale development.