基于多特征融合自编码器的无监督地震相分类研究
Unsupervised seismic facies classification based on multi-feature fusion autoencoder
王倩楠 1王治国 1杨阳 2朱剑兵 3高静怀2
作者信息
- 1. 西安交通大学数学与统计学院,西安 710049;陕西国家应用数学中心,西安 710049
- 2. 陕西国家应用数学中心,西安 710049;西安交通大学信息与通信工程学院,西安 710049
- 3. 中国石油化工股份有限公司胜利油田分公司物探研究院,东营 257022
- 折叠
摘要
地震相分类是地震数据解释中的一个重要步骤,是地震数据与沉积相的连接工具.为了提高地震相分类精度和减少对有限人工标签的依赖,本文提出了一种基于多特征融合自编码器的无监督地震相分类方法.首先,提出了一种混合卷积和变分编码的多特征融合自编码器,实现了地震数据中表征地震相的大量隐含特征提取.其次基于非负矩阵分解和K均值聚类实现了主特征分量分解和地震相聚类.实际地震数据应用结果和指标分析表明,本文方法提取的隐含特征趋于正态分布,且主特征分量中蕴含了不同地震相类别的响应,从而可以获得更准确的地震相分类结果.在渤海湾盆地东营凹陷古近系沙河街组湖相沉积中,清晰划分出了六类沉积微相的边界,有利于揭示三角洲沉积环境演变.
Abstract
Seismic facies classification plays an important role in seismic data interpretation,which is a bridge between seismic data and sedimentary facies.To improve the accuracy of seismic facies classification and reduce the number of manual labels,we propose an unsupervised seismic facies classification method based on the Multi-Feature Fusion Autoencoder(MFAE).At first,the proposed MFAE is generated by the hybride convolution network and variational autoencoder,which extract a large number of latent variables from seismic data.Then,the nonnegative matrix factorization is utilized to implement the principal eigencomponent decomposition and the K-means clustering is also introduced to obtain the results of seismic clustering.Finally,the proposed method is applied to the real data to test the performance.The results reveal that the extracted features of the proposed methods approximately satisfy the Gaussian distribution and the main features after principal eigencomponent decomposition contains the responses of different seismic facies classes.Therefore,the accuracy of the seismic facies classification can be improved.For the Paleogene Shahejie formation in Dongying Sag,the proposed method can predict more clear boundaries of the six seismic facies classes,which is beneficial to demonstrating the evolution of the deltaic sedimentary.
关键词
地震相分类/多特征融合自编码器/卷积自编码器/变分自编码器/非负矩阵分解Key words
Seismic facies classification/Multi-Feature Fusion Autoencoder(MFAE)/Convolutional autoencoder/Variational autoencoder/Non-negative matrix factorization引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据