Improving minibasin and subsalt imaging with reflection full-waveform inversion

Jonke, Katarina; Fu, Zhan; Wray, Brad; Shen, Hao

doi:10.1190/segam2017-17661070.1

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…(2017) and Jonke et al . (2017) suggested using the high‐wavenumber density model. However, these methods require explicit separation of the wavefields and, therefore, are still time‐consuming.…”

Section: Introductionmentioning

confidence: 99%

Efficient reflection waveform inversion using a locally normalized zero‐lag correlative objective function

Liu

2020

Geophysical Prospecting

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Full-waveform inversion is characterized by cycle-skipping when the starting background model differs significantly from the true model and low-frequency data are unavailable. To mitigate this problem, reflection waveform inversion is applied to provide a background velocity model for full-waveform inversion. This technique attempts to extract background velocity updates along the reflection wavepath by matching the reflection waveforms. However, two issues arise during the implementation of reflection waveform inversion: amplitude and efficiency. The amplitude is always underestimated due to the complex subsurface parameter (i.e. the source signature, density, attenuation etc.). This makes it unreasonable to match the reflection amplitude involved in waveforms, especially in the filed data cases. In addition, generating the background velocity gradient requires the simulation of the reflection wavefield. However, simulating the reflection wavefield is time-consuming. To address the former, we introduced a locally normalized objective function, while for the latter, we used an efficient strategy by avoiding the explicit generation of the reflection wavefield. Results show that applying the proposed method to both synthetic and field data can provide a good background velocity model for full-waveform inversion with high efficiency.

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Section: Introductionmentioning

confidence: 99%

Efficient reflection waveform inversion using a locally normalized zero‐lag correlative objective function

Liu

2020

Geophysical Prospecting

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“…Wavefield decomposition based on the vertical Hilbert transform is used to separate the migration term and the tomographic term (Liu et al, 2011;Tang et al, 2013). This workflow proves to be very effective in improving the velocity and the seismic image for deep targets (Jonke et al, 2017). However, strong migration leakage may be observed around high-dip events due to the limitation of the vertical Hilbert transform.…”

Section: Introductionmentioning

confidence: 99%

A demigration-based reflection full-waveform inversion workflow

Wang

Zhang

Wei

et al. 2018

SEG Technical Program Expanded Abstracts 2018

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Reflection FWI is effective at providing low-wavenumber velocity updates for deep areas beyond the penetration depth of diving waves and significantly improves seismic imaging. However, the tomographic term of the FWI gradient that is good for low-wavenumber velocity updates can be contaminated by the stronger high-wavenumber migration term. We present a reflection FWI workflow that is based on Born modeling to explicitly generate the tomographic term and thus is minimally impacted by migration contamination. In addition, we propose to use a set of partial stacks as reflectivity models for Born modeling to reduce the risks of cycle-skipping and incorrect update sign and to use a traveltime cost function to mitigate the negative impact from amplitude mismatch between input data and modeled synthetic data. Finally, we demonstrate the benefit and effectiveness of our approach using a field data set from the Gulf of Mexico.

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Reflection waveform inversion with variable density

Liu

et al. 2019

Journal of Applied Geophysics

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Improving minibasin and subsalt imaging with reflection full-waveform inversion

Cited by 7 publications

References 13 publications

Efficient reflection waveform inversion using a locally normalized zero‐lag correlative objective function

Efficient reflection waveform inversion using a locally normalized zero‐lag correlative objective function

A demigration-based reflection full-waveform inversion workflow

Reflection waveform inversion with variable density

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