基于深度学习的零井源距VSP上、下行波分离方法

doi:10.11720/wtyht.2025.0115

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Abstract

Wavefield separation serves as a key step in processing the data of vertical seismic profiles (VSPs). Its accuracy directly influences 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 introducing 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 network 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.

Key words： deep learning wavefield separation vertical seismic profile (VSP) Unet++

Received: 01 April 2025 Published: 30 December 2025

ZTFLH:

P631.4

Corresponding Authors: DENG Ding-Ding E-mail: wtyu-tlm@petrochina.com.cn;dantecup@163.com

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	Articles by authors
	Teng-Yu WANG
	Ding-Ding DENG
	Duo-Ming ZHENG
	Yang LIU
	Zhen ZHANG
	Wen-Jun LUO

Cite this article:

Teng-Yu WANG,Ding-Ding DENG,Duo-Ming ZHENG, et al. A deep learning-based method for separating up- and down-going waves in zero-offset vertical seismic profiles[J]. Geophysical and Geochemical Exploration, 2025, 49(6): 1319-1332.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2025.0115 OR https://www.wutanyuhuatan.com/EN/Y2025/V49/I6/1319

Marmousi model

Data driven deep learning based up-/ downgoing wave separation process for VSP

Block of VSP data for part of survey A (the same color indicates the same amplitude value)

Dimensional overview before and after block division of synthetic dataset and zero-offset VSP data in area A

Schematic diagram of Unet network structure

Schematic of Unet++ network architecture(a)and detailed analysis of the first skip connection pathway(b)

Model learning curve

M1 zero-offset synthetic VSP data and up- /down going wave labels

Comparison of Unet wavefield separation results for M1 zero-offset synthetic VSP data with conventional loss and adding relative downgoing wavefield loss

Comparison of residuals between Unet wavefield separation results and labels with conventional loss and adding relative downgoing wavefield loss

Comparison of Unet++ wavefield separation results for M1 zero-offset synthetic VSP data with conventional loss and adding relative downgoing wavefield loss

Comparison of residuals between Unet++ wavefield separation results and labels with conventional loss and adding relative downgoing wavefield loss

Comparison of wavefield separation results for zero-offset VSP data in A-w1 using different deep learning models

FK spectra of wave field separation results of different deep learning models

Residual comparison between wavefield separation results and labels across different deep learning models

Computational cost of wave-field separation for different methods in area A

Comparison of wavefield separation results for zero-offset VSP data in A-w4 using different deep learning models

FK spectra of wave field separation results of different deep learning models

Multi-trace waveform comparison of zero-offset VSP data from well A-w4 using different processing methods

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