石油地球物理勘探2025，Vol.60Issue(3)：642-654,13.DOI:10.13810/j.cnki.issn.1000-7210.20240387
基于数据增广和伪标签生成的波阻抗反演
Wave impedance inversion based on data augmentation and pseudo-label generation
摘要
Abstract
Wave impedance inversion based on deep learning often requires a large number of label data to drive the model for network training,but in practice,the acquisition of label data(logging data)is difficult and costly,and usually only a small amount of label data is used for training.Therefore,a semi-supervised wave im-pedance inversion method based on data augmentation and pseudo-label generation is proposed.Firstly,the la-bel wave impedance data is interpolated by cubic spline interpolation method and then randomly resampled,and then the seismic records corresponding to the augmented wave impedance are calculated by forward modeling method.The amplified seismic record and wave impedance are used as the network training set to train the net-work and predict the wave impedance.High quality prediction data is selected as pseudo-labels,and the pseudo-labels are augmented to greatly expand the training data set.However,Temporal Convolutional Network(TCN)has advantages in time series data modeling,which can capture the long-term dependency of data and complete the wave impedance inversion task well.In this paper,the Marmousi model test results show that the proposed method is suitable for the wave impedance inversion of a small amount of label data,has good anti-noise performance,and still has good inversion accuracy for different label distributions.The inversion results of the actual exploration data show that this method can effectively solve the problem of seismic impedance in-version.关键词
波阻抗反演/时间卷积网络/数据增广/伪标签/半监督学习Key words
wave impedance inversion/temporal convolutional networks/data augmentation/pseudo-label/semi-supervised learning分类
地质学引用本文复制引用
程程,赵岩..基于数据增广和伪标签生成的波阻抗反演[J].石油地球物理勘探,2025,60(3):642-654,13.基金项目
本项研究受油气资源与勘探技术教育部重点实验室青年创新团队项目"智能地震数据处理与解释"(PI2023-01)资助. （PI2023-01）
