石油物探2026，Vol.65Issue(3)：442-458,17.DOI:10.12431/issn.1000-1441.2025.0200
基于深度学习的二维VSP数据纵横波分离研究
P-and S-wavefield separation for 2D VSP data based on deep learning
摘要
Abstract
P-and S-wavefield separation is a key step in multicomponent VSP data processing.Conventionally,this is achieved through signal analysis or polarization projection to decompose different wave types.However,factors such as strong vertical velocity variations and borehole deviation significantly increase the complexity of wavefield separation.To address this issue,a deep neural network-based method is proposed.This method integrates elastic wave theory with Helmholtz decomposition to construct P-and S-wave labels for multicomponent VSP data under complex acquisition conditions.A U-Net architecture is then leveraged to extract P-and S-wavefield features in 2D VSP data and consequently achieve model-independent wavefield separation.Experimental results from synthetic and field data show that the deep learning method effectively separates P-from S-waves in 2D VSP data when using training samples constructed with target features.Furthermore,by analyzing the impact of factors such as offset and deviation on network performance,targeted strategies are proposed to improve the network's generalization ability.These findings provide effective support for P-and S-wave separation in multicomponent VSP data.关键词
VSP/P/S波分离/深度学习/数据标签构建/卷积神经网络Key words
VSP/P-and S-wavefield separation/deep learning/data label construction/convolutional neural network分类
能源科技引用本文复制引用
黄河,孟涛,王腾飞,程玖兵,徐蔚亚,朱成宏..基于深度学习的二维VSP数据纵横波分离研究[J].石油物探,2026,65(3):442-458,17.基金项目
国家自然科学基金项目(42574147,42204110,42474140)和中国石化油气藏地球物理重点实验室开放基金项目(33550000-22-ZC0613-0289)共同资助. This research is financially supported by the National Natural Science Foundation of China(Grant Nos.42574147,42204110,42474140)and Open Fund of Sinopec Key Laboratory of Oil and Gas Reservoir Geophysics(Grant No.33550000-22-ZC0613-0289). （42574147,42204110,42474140）
