Dynamic-warping full-waveform inversion to overcome cycle skipping

Wang, Min; Xie, Yi; Xu, W.; Loh, Fong Cheen; Xin, K.; Chuah, Boon Leng; Manning, Ted; Wolfarth, S.

doi:10.1190/segam2016-13855951.1

Cited by 19 publications

(11 citation statements)

References 8 publications

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“…Currently, the most widely used method for retraveling the travel-time information is dynamic time/ image warping-DTW/DIW from the field of image processing (Hale 2013). Besides, there are some other methods, for example, intermediate data by Wang et al (2016) and Yao et al (2019b).…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

A review on reflection-waveform inversion

Yao

Wang

2020

Pet. Sci.

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“…A reasonable signal-to-noise ratio can be seen for data from 5Hz, which was used as the starting frequency for FWI. To avoid cycle skipping, dynamic warping FWI (Wang et al, 2016) was used. We carried out the inversion up to 7.5 Hz with a frequency step of 0.5Hz.…”

Section: Building the High Resolution Velocitymentioning

confidence: 99%

Imaging challenges at Gulf of Papua (PNG) and their solutions through high-end imaging technology

Gong¹,

Chandra

Wu³

et al. 2019

ASEG Extended Abstracts

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“…In addition, due to the nonlinearity of the FWI problem and band-limited nature of the seismic data, having a good initial velocity model which allows kinematic matching of the observed traveltimes with an error of less than half a period is a critical prerequisite for the convergence of the classical FWI problem. Otherwise, the so-called cycle skipping issue will occur, which leads the optimization algorithm towards a local rather than global minimum of the misfit functional (Beydoun & Tarantola, 1988;Jamali Hondori et al, 2015;Wang et al, 2016;Wu & Alkhalifah, 2018;Yao et al, 2019). Recent reformulations of FWI (Sun & Alkhalifah, 2019a, b;Warner & Guasch, 2016) implement matching filters in the misfit functional to overcome cycle skipping when neither a good initial model nor low frequency data are available.…”

Section: Introductionmentioning

confidence: 99%

Full-Waveform Inversion for Imaging Faulted Structures: A Case Study from the Japan Trench Forearc Slope

Hondori

Guo

Mikada

et al. 2021

Pure Appl. Geophys.

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Full-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.

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Dynamic-warping full-waveform inversion to overcome cycle skipping

Cited by 19 publications

References 8 publications

A review on reflection-waveform inversion

A review on reflection-waveform inversion

Imaging challenges at Gulf of Papua (PNG) and their solutions through high-end imaging technology

Full-Waveform Inversion for Imaging Faulted Structures: A Case Study from the Japan Trench Forearc Slope

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