基于深度学习的页岩地质露头裂缝特征自动识别  

Automatic Recognition of Fracture Characteristics from Shale GeologicalOutcrop Based on Deep Learning

作　　者：刘恩良 袁俊亮 霍守东 舒梦珵 张峰[1] LIU En-liang;YUAN Jun-liang;HUO Shou-dong;SHU Meng-cheng;ZHANG Feng(College of Geophysics,China University of Petroleum,Beijing 102249,China;CNOOC Research Institute Co.,Ltd.,Beijing 100028,China;Institute of Geology and Geophysics,Chinese Academy of Sciences,Beijing 100029,China)

机构地区：[1]中国石油大学(北京)地球物理学院,北京102249 [2]中海油研究总院有限责任公司,北京100028 [3]中国科学院地质与地球物理研究所,北京100029

出　　处：《科学技术与工程》2024年第30期12853-12863,共11页Science Technology and Engineering

基　　金：国家自然科学基金(42122029);中石油地球物理勘探应用基础试验和先进理论方法研发项目(2022DQ0604-02);中国石油集团科学研究与技术开发项目(2021DJ3506)。

摘　　要：针对页岩层理构造发育、裂缝细小的特点,提出了一种基于UU-Net(multi-path U-shaped network)深度学习模型的页岩露头裂缝自动识别深度学习模型。首先,对裂缝图像数据集进行手工标注和二值化预处理;然后,将数据集分为训练集和测试集,通过参数优化迭代对模型进行训练和验证,得到精度符合要求的露头多尺度裂缝识别模型;最后,将该模型应用于四川盆地页岩露头图像和岩石薄片图像上分别进行不同尺度的裂缝提取,并定量统计裂缝的长度和倾角信息。实际应用结果表明:相比于手工标注,深度学习裂缝识别效率明显提高;大尺度裂缝识别准确率达到98%,中小尺度裂缝识别准确率约为96%,微裂缝识别准确率约为94%;训练的模型可以应用于其他区块页岩露头或岩石薄片图像的多尺度裂缝识别中,模型具有较好的泛化能力。Aiming at the characteristics of shale bedding structures and fine fracture developments,an automatic multi-scale fracture recognition method for shale outcrop images based on a UU-Net(multi-path U-shaped network)deep neural network was presented.The process begins with the manual labeling and binarization of fracture image datasets.The datasets were then divided into training and testing subsets.Through iterative parameter optimization,the model was trained and validated to achieve the desired accuracy,resulting in a multi-scale fracture recognition model for outcrop images.The model was subsequently applied to shale outcrop images and rock slice images from the Sichuan Basin to extract fractures at different scales.Quantitative statistics on fracture length and dip angle information were provided.The application results demonstrate as follows.Compared to manual labeling,the efficiency of crack recognition using deep learning is significantly improved.The accuracy of large-scale fracture identification is 98%,while the accuracy for medium-and small-scale fractures is approximately 96%,and for micro-fractures,it is about 94%.The trained model can be applied to multi-scale fracture identification of shale outcrops or rock slice images in other regions,indicating strong generalization capabilities.

关 键 词：野外岩石露头 页岩裂缝 深度学习 裂缝识别和提取 岩石薄片 

分 类 号：P624.4[天文地球—地质矿产勘探]