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Starting FWI from high frequencies leads to cycle skipping because of wrong fitting (left), the issue could be solved by low frequencies (right).
Source publication
Full Waveform Inversion (FWI) of seismic data is a high resolution subsurface imaging tool and there is a lot of effort to fully industrialize it. The method, which uses a gradient based data fitting approach to minimize the misfit between observed and simulated waveforms, strongly requires either a good initial model or low frequency data for the...
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Thin sand layers are widely developed in the Cretaceous Baxigai Formation in the Yudong area, western Tabei Uplift, and series of lithologic traps controlled by the structures are formed. The thickness of the target layer is 3–5 m, which is beyond the resolution of conventional seismic data; this causes the prediction of the reservoir to be difficu...
Citations
... 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. ...
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 through a tomographic fashion. When these events are absent, a reliable initial velocity model is necessary to assure that the observed and simulated waveforms kinematically fit within an error 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 is 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.
... Because field data result from a combination of several unknowns, the inverse problem of recovering the unknowns from field data always suffers from non-uniqueness and uncertainty of solutions. To mitigate this issue, conventional tomography methods started with a good initial model (e.g., Landa et al. 1988;Prieux et al. 2013;Hondori et al. 2015) or introduced a supplementary method (Bunks et al. 1995;Yan and Lines 2001;Shin and Cha 2008;Oh and Min 2013;Wu, Luo and Wu 2014). When an initial model deviates from the true velocity model, many iterations are needed; nevertheless, distorted results can be obtained. ...
Reflection traveltime tomography (RTT) has been used to describe subsurface velocity structures in practice, which can be used as a background or initial model for prestack depth migration or full waveform inversion. Conventional RTT is performed by solving an optimization problem based on a ray‐tracing method. As a result, RTT requires heavy computational effort to carry out ray tracing and solve a large matrix equation. In addition, like most data‐domain tomography methods, RTT depends on initial guesses and suffers from non‐uniqueness and uncertainty of solutions. In this study, we propose a deterministic ray‐based RTT method by applying seismic interferometry (SI), and this method does not suffer from the non‐uniqueness problem and does not require a priori information on subsurface media. By adding a virtual layer (whose properties are known) on the top of the real surface and applying convolutional type interferometry, we approximately determine the stationary points (i.e., incident raypaths in the virtual layer). Then, we generate reflection points for a range of assumed velocities and estimate the velocity by considering the number of reflection points and the traveltime difference between the observed and calculated data. The reflection surface can then be recovered by using the estimated velocity. Once the first target layer is resolved, we can recover the whole media by recursively applying the same method to the lower layers. Numerical examples using surface seismic profile (SSP) data for homogeneous‐layer (with a low‐velocity layer) and inhomogeneous‐layer models and real field data experiments on the Congo data set demonstrate that our method can successfully recover the velocities and depths of subsurface media without initial guesses. However, our method has some limitations for multi‐layer models because the method does not have sufficient reflection points for the deeper layers. This article is protected by copyright. All rights reserved
... Low-frequency seismic data is necessary for overcoming the cycle-skipping problem (Plessix et al. 2010;Wu et al. 2014). However, in marine seismic oil exploration, the widely-used towed-streamer seismic data lacks lowfrequency components due to the recording limitations of hydrophones (Hondori et al. 2015). Thus, the FWI of towed-streamer seismic data always produces an unreliable velocity model if the starting model is very different from the true one. ...
In marine seismic oil exploration, full waveform inversion (FWI) of towed-streamer data is used to reconstruct velocity models. However, the FWI of towed-streamer data easily converges to a local minimum solution due to the lack of low-frequency content. In this paper, we propose a new FWI technique using towed-streamer data, its integrated data sets and limited OBS data. Both integrated towed-streamer seismic data and OBS data have low-frequency components. Therefore, at early iterations in the new FWI technique, the OBS data combined with the integrated towed-streamer data sets reconstruct an appropriate background model. And the towed-streamer seismic data play a major role in later iterations to improve the resolution of the model. The new FWI technique is tested on numerical examples. The results show that when starting models are not accurate enough, the models inverted using the new FWI technique are superior to those inverted using conventional FWI.
