摘要
综述了岩石薄片智能鉴定的研究现状,分析了薄片智能鉴定中存在的问题,提出了碳酸盐岩薄片人工智能鉴定命名规范,在此基础上提出碳酸盐岩薄片人工智能鉴定的研究流程与主要研究内容:①准备与处理薄片图像,建立碳酸盐岩图像数据库;②以先验知识指导建立薄片图像的结构组分、矿物组分、孔隙类型等分类标签,进行人工标注并建立标签数据库;③应用卷积神经网络、深度学习等技术,采用机器学习和人工修正相结合的方法,建立薄片图像标签的知识图谱;④进行结构组分、矿物组分及孔隙类型和含量的智能识别,完成自动岩性定名,输出鉴定报告,实现薄片智能鉴定。碳酸盐岩薄片人工智能鉴定目前仍然存在标签标注样本数量、语义分割不明确、成岩作用影响等问题,有待进一步研究。碳酸盐岩人工智能鉴定的发展方向包括从宏观(岩心、野外露头)到微观不同尺度的图像鉴定,CT、扫描电镜、阴极发光等不同类型图像的鉴定,以及测井、地震资料的智能解释等。
Thin section identification is the basis of various geological work such as research on sedimentation,diagenesis,and reservoir of carbonate rocks.Carbonate rocks have strong heterogeneity,various structural components and particle types.The artificial thin section identification is subjective,difficult,time-consuming and labor-intensive,and not easy to be widely popularized.In the big data and artificial intelligence(AI)background,it is promising to increase the efficiency by applying AI identification technology.This study summarized the research status and analyzed the existed problems in AI identification of carbonate thin sections.The main contents of AI identification of carbonate thin sections include:(1)Preparation of thin sections and image processing.Dyeing thin sections with no-cover glass are the basis of later recognition.The blue casting thin sections are significant for pore recognition.Photos should be captured under different optical property including PPL and XPL with different rotation degree.Image pre-processing and segmentation can help to increase the later identification.The establishment of carbonate thin section database is the basis of AI identification.(2)Based on the prior knowledge of carbonate professionals,the structural components,mineral components and pore types of the image are classified,label classification is established,and manual labeling is carried out by carbonate professionals.It is established that the classification chart of major component labels in carbonate thin sections.The establishment of label database can contribute to further machine learning.(3)The convolution neural network and deep learning are introduced into the labeled thin section images,which can learn and discriminate the morphology and internal structure of various components.The knowledge graph of the thin section image labels is established by combination of machine learning and manual correction,which can classify rock types,recognize sedimentary structures and grain types.(4)It is performed that i
作者
张杰
沈安江
胡安平
周进高
佘敏
韩明珊
ZHANG Jie;SHEN Anjiang;HU Anping;ZHOU Jingao;SHE Min;HAN Mingshan
出处
《海相油气地质》
CSCD
北大核心
2023年第4期337-348,共12页
Marine Origin Petroleum Geology
基金
中国石油天然气股份有限公司前瞻性基础性技术攻关项目“油气勘探开发人工智能关键技术研究”(编号:2023DJ84)
中国石油海外重点领域油气勘探关键技术研究项目(编号:2021DJ3104)资助。
关键词
碳酸盐岩薄片
人工智能识别
岩石结构组分
知识图谱
标签数据库
carbonate thin section
artificial intelligence identification
rock structural components
knowledge graph
la⁃bel database
