物探与化探2025，Vol.49Issue(6)：1319-1332,14.DOI:10.11720/wtyht.2025.0115
基于深度学习的零井源距VSP上、下行波分离方法
A deep learning-based method for separating up-and down-going waves in zero-offset vertical seismic profiles
摘要
Abstract
Wavefield separation serves as a key step in processing the data of vertical seismic profiles(VSPs).Its accuracy directly in-fluences seismic imaging,inversion of elastic parameters,lithology identification,and interpretation of hydrocarbon-bearing properties.Traditional methods face challenges in wavefield separation.For example,the median filtering requires manual intervention,often intro-ducing errors and thus compromising separation accuracy;the FK filtering yields high accuracy but low efficiency.In contrast,deep learning techniques offer high automation,enabling both high accuracy and efficiency in wavefield separation.Hence,this study proposed a deep learning-based method for separating up-and down-going waves in zero-offset VSPs.First,the up-and down-going waves were separated through FK transform,generating a dataset.Second,a deep learning-based model,Unet++,was constructed for separating these waves in VSPs.Third,the relative down-going wavefield(obtained by subtracting the predicted up-going wavefield from the full wavefield)was incorporated into the loss function to mitigate the impacts of amplitude differences between up-and down-going waves on network updates.Moreover,the structural similarity index measure(SSIM)was employed as a regularization constraint to assist the net-work in learning the structural characteristics of the wavefield.The test results of actual VSP data demonstrate that the trained network can effectively learn the characteristics of the up-and down-going waves,achieving high accuracy and efficiency in wavefield separation.关键词
深度学习/波场分离/垂直地震剖面(VSP)/Unet++Key words
deep learning/wavefield separation/vertical seismic profile(VSP)/Unet++分类
天文与地球科学引用本文复制引用
王腾宇,邓丁丁,郑多明,刘洋,张振,罗文君..基于深度学习的零井源距VSP上、下行波分离方法[J].物探与化探,2025,49(6):1319-1332,14.基金项目
中石油塔里木项目"超深层井中地震与重磁电研究"(YF202401.01.05) （YF202401.01.05）
