Reflection-Waveform Inversion Regularized with Structure-Oriented Smoothing Shaping

Yao, Gang; Silva, Nuno V. da; Wu, Di

doi:10.1007/s00024-019-02265-6

Cited by 22 publications

(7 citation statements)

References 54 publications

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“…One manifestation of the ill-posedness is that an inverted background velocity model is full of artifacts but still fits the observed data, for example, Fig. 7a of Yao et al (2019c). In order to mitigate the ill-posedness, the inversion system has to incorporate extra information about the velocity model.…”

Section: Constraints In Reflection-waveform Inversionmentioning

confidence: 99%

“…So far, the research on adding extra information has been studied inadequately. Recently, Yao et al (2019c) introduced structural-oriented smoothing to constrain RWI. The constraint is a piece of geologic information: the rock properties vary slowly in one stratum but can be fast in different strata.…”

Section: Constraints In Reflection-waveform Inversionmentioning

confidence: 99%

“…The latter is called shaping, which directly manipulates the model to possess a particular feature (Fomel 2007). The shaping process can be added as the last step in a loop of RWI (Yao et al 2019c). Figure 14 shows an example using the constraint of structural-oriented smoothing.…”

Section: Constraints In Reflection-waveform Inversionmentioning

confidence: 99%

“…Mitigating this problem, except the advance of computer hardware, optimization on inversion algorithms, numerical wavefield modeling and computer codes are important research directions. Among them, (Yao et al 2019c) improving the inversion and modeling algorithms are the key directions because computer code optimization, for example, load balance between processes, CPU vectorization, cache reuse and GPU acceleration (e.g., Wang et al 2019), can only speed up the inversion in tens of times or less generally. Insufficient information restricted by the technology and space of acquisition, the acquired seismic data are incomplete in spatial coverage and frequencies.…”

Section: Conclusion and Future Perspectivementioning

confidence: 99%

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A review on reflection-waveform inversion

Yao

Wang

2020

Pet. Sci.

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Full-waveform inversion (FWI) utilizes optimization methods to recover an optimal Earth model to best fit the observed seismic record in a sense of a predefined norm. Since FWI combines mathematic inversion and full-wave equations, it has been recognized as one of the key methods for seismic data imaging and Earth model building in the fields of global/regional and exploration seismology. Unfortunately, conventional FWI fixes background velocity mainly relying on refraction and turning waves that are commonly rich in large offsets. By contrast, reflections in the short offsets mainly contribute to the reconstruction of the high-resolution interfaces. Restricted by acquisition geometries, refractions and turning waves in the record usually have limited penetration depth, which may not reach oil/gas reservoirs. Thus, reflections in the record are the only source that carries the information of these reservoirs. Consequently, it is meaningful to develop reflection-waveform inversion (RWI) that utilizes reflections to recover background velocity including the deep part of the model. This review paper includes: analyzing the weaknesses of FWI when inverting reflections; overviewing the principles of RWI, including separation of the tomography and migration components, the objective functions, constraints; summarizing the current status of the technique of RWI; outlooking the future of RWI.

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Section: Constraints In Reflection-waveform Inversionmentioning

confidence: 99%

Section: Constraints In Reflection-waveform Inversionmentioning

confidence: 99%

Section: Constraints In Reflection-waveform Inversionmentioning

confidence: 99%

Section: Conclusion and Future Perspectivementioning

confidence: 99%

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A review on reflection-waveform inversion

Yao

Wang

2020

Pet. Sci.

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“…Recently, reflection-waveform inversion (RWI) has become a popular method for background model retrieval, as it is capable of updating a smooth background model along reflection wave paths (Xu et al, 2012;Zhou et al, 2012). In RWI, Born modeling is used to separate the tomography and migration components in the velocity gradient (Wang et al, 2018;Li and Alkhalifah, 2020;Yao et al, 2020), and the tomography components are used to correct errors in the background velocity (Yao et al, 2019). Traveltime-based reflection RWI was proposed and demonstrated its effectiveness on marine seismic data (Wang et al, 2017;Sun et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

A reflection-based efficient wavefield inversion

Song

Alkhalifah

2021

GEOPHYSICS

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Full-waveform inversion (FWI) is popularly used to obtain a high-resolution subsurface velocity model. However, it requires either a good initial velocity model or low-frequency data to mitigate the cycle-skipping issue. Reflection-waveform inversion (RWI) uses a migration/demigration process to retrieve a background model that can be used as a good initial velocity in FWI. The drawback of the conventional RWI is that it requires the use of a least-squares migration, which is often computationally expensive, and is still prone to cycle skipping at far offsets. To improve the computational efficiency and overcome the cycle skipping in the original RWI, we incorporate it into a recently introduced method called efficient wavefield inversion (EWI) by inverting for the Born scattered wavefield instead of the wavefield itself. In this case, we use perturbation-related secondary sources in the modified source function. Unlike conventional RWI, the perturbations are calculated naturally as part of the calculation of the scattered wavefield in an efficient way. As the sources in the reflection-based EWI (REWI) are located in the subsurface, we are able to update the background model along the reflection wave path. In the background velocity inversion, we calculate the background perturbation by a deconvolution process at each frequency. After obtaining the REWI inverted velocity model, a sequential FWI or EWI is needed to obtain a high-resolution model. We demonstrate the validity of the proposed approach using synthetic data generated from a section of the Sigsbee2A model. To further demonstrate the effectiveness of the proposed approach, we test it on an ocean bottom cable (OBC) dataset from the North Sea. We find that the proposed methodology leads to improved velocity models as evidenced by flatter angle gathers.

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