石油地球物理勘探2025，Vol.60Issue(5)：1089-1098,10.DOI:10.13810/j.cnki.issn.1000-7210.20240439
基于超稀疏测井标注的半监督地震相自动识别
Semi-supervised automatic seismic facies identification based on ultra-sparse well logging labels
摘要
Abstract
Seismic facies identification is a crucial link in seismic data interpretation.Deep learning technology can enhance the efficiency and accuracy of automatic seismic facies identification.However,deep learning me-thods typically rely on large amounts of labeled data,and in practical applications,the labeling cost of seismic data is high,with great difficulty.Additionally,basic logging data cannot be directly utilized.To this end,this paper proposes a semi-supervised automatic seismic facies identification method based on ultra-sparse logging la-bels.First,based on the HRNet,a seismic facies identification model that uses one-dimensional logging labels is built for for supervision.Second,to preserve the vertical characteristics of seismic data,this paper develops a sparse label sampling module(SLSM)that conducts samples around the logging labels without slicing the seis-mic data vertically,thus retaining its vertical depth features and laying a solid foundation for subsequent semi-supervised learning tasks.Third,in terms of the lateral correlation of seismic data,the region growing training strategy(RGTS)is proposed,which expands the information from logging labels to the entire seismic volume through an iterative growing process.Experiments on real-world data show that the proposed model achieves a mean intersection over union(MIoU)of 79.64%by using only 32 one-dimensional logging labels,which ac-count for less than 0.5%of the total data volume.This approach provides references for conducting seismic fa-cies identification in areas with sparse and locally distributed logging data,demonstrating promising application potential.关键词
地震相识别/半监督学习/稀疏标注/深度学习Key words
seismic facies identification/semi-supervised learning/sparse labels/deep learning分类
天文与地球科学引用本文复制引用
李克文,董明辉,李文韬,武清汕..基于超稀疏测井标注的半监督地震相自动识别[J].石油地球物理勘探,2025,60(5):1089-1098,10.基金项目
本项研究受国家自然科学基金项目"储层天然气水合物相变和渗流多场时空演化规律"(51991365)和山东省自然科学基金项目"基于多源数据融合的浊积岩有效储层预测方法"(ZR2021MF082)联合资助. （51991365）
