海洋地质前沿2025，Vol.41Issue(8)：40-54,15.DOI:10.16028/j.1009-2722.2024.215
联合RGB属性融合与FCM聚类算法的浅海浊积砂体精细表征
Detailed characterization of shallow marine turbidite sand bodies using RGB attribute fusion and FCM clustering algorithm:a case study of X Gas Field in the Yinggehai Basin
摘要
Abstract
The deposition characteristics of turbidite sand bodies in the X gas field in the Yinggehai Basin,South Chin Sea,controlled by shallow marine turbidity current sedimentary systems,are complex and spatially distrib-uted unclearly,which restricts the development of oil and gas resources.Based on well logging,core,and 3D seis-mic data,we combined frequency decomposition RGB attribute fusion technology with the FCM clustering al-gorithm and achieved a fine characterization of turbidite sand bodies.Results show that ① The turbidite sand bod-ies have strong amplitude and high continuity in seismic reflection.The RGB fusion of frequency-decomposed seismic attributes at 15,35,and 55 Hz best responded to the spatial distribution of turbidite sand bodies,and the predicted sand body thickness well matched the actual thickness seen in drill hole,showing a high correlation coefficient(R2=0.94).② The FCM algorithm effectively clustered the selected seismic attributes,and divided the turbidite sand bodies into three main categories based on the planar features of five clustering groups.③ In the muddy sedimentary background,six sedimentary units could be recognized,i.e.,banded lateral accumulation bod-ies,annular lateral accumulation bodies,channel banks,channel-branch channels,proximal lobe bodies,and distal lobe bodies.The annular and banded lateral accumulation bodies,as well as the distal lobe bodies,are good tar-gets for sand mining.关键词
浅海浊流/浊积砂体/RGB属性融合/FCM算法/聚类分析Key words
shallow marine turbidity current/turbidite sand body/RGB attribute fusion/fuzzy C-Means al-gorithm/cluster analysis分类
海洋科学引用本文复制引用
赵兴,李磊,薛国庆,张忠坡,袁晓婷,柴亚伟,杨潘,徐勇..联合RGB属性融合与FCM聚类算法的浅海浊积砂体精细表征[J].海洋地质前沿,2025,41(8):40-54,15.基金项目
西安石油大学研究生创新与实践能力培养计划(YCS23213064) （YCS23213064）
