Frequency-domain seismic data transformation from point-source to line-source for 2D viscoelastic anisotropic media

Geophysics ◽  
2021 ◽  
pp. 1-45
Author(s):  
Qingjie Yang ◽  
Bing Zhou ◽  
Mohamed Kamel Riahi ◽  
Mohammad Al-Khaleel

We present a simple yet effective transform function to convert 3D point-source seismic data to equivalent 2D line-source data, which is required when applying efficient 2D migration and full-waveform inversion to field data collected along a line. By numerically comparing the 3D and corresponding 2D Green’s tensors in various media, the phase shift around 45° and the offset amplitude compensation factor, as well as small fluctuations of the amplitude ratios are observed in all nonzero components of the wave-equation solutions. Based on these observations, we derive a transform function comprised of (1) a simple filter for compensating amplitude and phase shift, and (2) stretching scalars for scaling amplitude differences for different components. We employ the 3D and 2D analytical wave solutions in various homogeneous media to demonstrate the accuracy of the proposed transform function, and then apply it to a heterogeneous, viscoelastic, anisotropic model and a modified Marmousi model. All of these results indicate that the proposed transform function is applicable for the conversion of point-source data to equivalent line-source data for imaging 2D subsurface structure.

Author(s):  
Ehsan Jamali Hondori ◽  
Chen Guo ◽  
Hitoshi Mikada ◽  
Jin-Oh Park

AbstractFull-waveform inversion (FWI) of limited-offset marine seismic data is a challenging task due to the lack of refracted energy and diving waves from the shallow sediments, which are fundamentally required to update the long-wavelength background velocity model in a tomographic fashion. When these events are absent, a reliable initial velocity model is necessary to ensure that the observed and simulated waveforms kinematically fit within an error of less than half a wavelength to protect the FWI iterative local optimization scheme from cycle skipping. We use a migration-based velocity analysis (MVA) method, including a combination of the layer-stripping approach and iterations of Kirchhoff prestack depth migration (KPSDM), to build an accurate initial velocity model for the FWI application on 2D seismic data with a maximum offset of 5.8 km. The data are acquired in the Japan Trench subduction zone, and we focus on the area where the shallow sediments overlying a highly reflective basement on top of the Cretaceous erosional unconformity are severely faulted and deformed. Despite the limited offsets available in the seismic data, our carefully designed workflow for data preconditioning, initial model building, and waveform inversion provides a velocity model that could improve the depth images down to almost 3.5 km. We present several quality control measures to assess the reliability of the resulting FWI model, including ray path illuminations, sensitivity kernels, reverse time migration (RTM) images, and KPSDM common image gathers. A direct comparison between the FWI and MVA velocity profiles reveals a sharp boundary at the Cretaceous basement interface, a feature that could not be observed in the MVA velocity model. The normal faults caused by the basal erosion of the upper plate in the study area reach the seafloor with evident subsidence of the shallow strata, implying that the faults are active.


2019 ◽  
Vol 16 (6) ◽  
pp. 1017-1031 ◽  
Author(s):  
Yong Hu ◽  
Liguo Han ◽  
Rushan Wu ◽  
Yongzhong Xu

Abstract Full Waveform Inversion (FWI) is based on the least squares algorithm to minimize the difference between the synthetic and observed data, which is a promising technique for high-resolution velocity inversion. However, the FWI method is characterized by strong model dependence, because the ultra-low-frequency components in the field seismic data are usually not available. In this work, to reduce the model dependence of the FWI method, we introduce a Weighted Local Correlation-phase based FWI method (WLCFWI), which emphasizes the correlation phase between the synthetic and observed data in the time-frequency domain. The local correlation-phase misfit function combines the advantages of phase and normalized correlation function, and has an enormous potential for reducing the model dependence and improving FWI results. Besides, in the correlation-phase misfit function, the amplitude information is treated as a weighting factor, which emphasizes the phase similarity between synthetic and observed data. Numerical examples and the analysis of the misfit function show that the WLCFWI method has a strong ability to reduce model dependence, even if the seismic data are devoid of low-frequency components and contain strong Gaussian noise.


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