Abstract
We have developed a migration scheme that can compensate absorption and dispersion with effective Q estimation and Fresnel zone identification based on deep learning. We use the U-Net neural network technology in deep learning to automatically identify Fresnel zones from compensated migrated dip-angle gathers and obtain the optimal aperture for migration, avoiding the tedious task of manually modifying the boundaries of Fresnel zones. Instead of the interval Q factor, we used an effective Q parameter to compensate absorption and dispersion. The effective Q is estimated using VSP well data and surface seismic velocity data. The proposed scheme can be incorporated into conventional seismic data processing workflow. A field data set was employed to validate the proposed scheme. Higher resolution imaging results with low noise levels are obtained.
Highlights
The dissipation of seismic energy is caused by the anelasticity of the subsurface medium, which will decrease the amplitude and modify the phase
Cheng et al (2020) used a modified VGGNet (A convolutional neural network was developed by the University of Oxford’s Visual Geometry Group and Google DeepMind in 2014) to extract Fresnel zones from migrated dip-angle gathers, which is a useful attempt at deep learning for Fresnel zone estimation
This article takes the estimations of the optimal aperture and effective Q model as the research focus in the compensated prestack time migration (PSTM), which is arranged as follows: first, we introduce a modified PSTM scheme with compensation based on the effective Q; second, we propose a Fresnel zone identification scheme based on compensated migrated dip-angle gathers using deep learning; third, we present an estimation approach of the effective Q model for the compensated PSTM
Summary
The dissipation of seismic energy is caused by the anelasticity of the subsurface medium, which will decrease the amplitude and modify the phase. Cheng et al (2020) used a modified VGGNet (A convolutional neural network was developed by the University of Oxford’s Visual Geometry Group and Google DeepMind in 2014) to extract Fresnel zones from migrated dip-angle gathers, which is a useful attempt at deep learning for Fresnel zone estimation. This article takes the estimations of the optimal aperture and effective Q model as the research focus in the compensated PSTM, which is arranged as follows: first, we introduce a modified PSTM scheme with compensation based on the effective Q; second, we propose a Fresnel zone identification scheme based on compensated migrated dip-angle gathers using deep learning; third, we present an estimation approach of the effective Q model for the compensated PSTM. We demonstrate our scheme with a field data set
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