Subsalt Imaging Improvement Possibilities Through a Combination of FWI and Reflection FWI Chao Peng1, Minshen Wang1, Nicolas Chazalnoel1, and Adriano Gomes1
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Subsalt imaging improvement possibilities through a combination of FWI and reflection FWI Chao Peng1, Minshen Wang1, Nicolas Chazalnoel1, and Adriano Gomes1 Abstract approaches have yet to make the transition to consistent real-data Despite continuous improvements in seismic acquisition and applications. erefore, the most common method for updating processing technology, imaging under salt remains challenging, salt geometry remains to manually pick the dierent salt boundar- speci cally because of the diculty in updating complex salt ies through dierent migration steps. is method requires good geometries and subsalt velocity. Synthetic studies show that when geologic knowledge, can be quite time-intensive and challenging, certain conditions are met, full-waveform inversion (FWI) can and often fails to recover the full complexity of the salt geometry recover very complex velocity models, including the geometry of (Dellinger et al., 2017). For the subsalt, reverse time migration the salt and the subsalt velocity. Unfortunately, currently available (RTM) angle gathers (Li et al., 2011; Xu et al., 2011) or surface- seismic eld data fall short of meeting the requirements needed oset gathers (SOGs) (Yang et al., 2015) are used to update the to replicate what can be achieved on synthetic data. We rst use velocity tomographically. A drawback of these approaches is their a wide-azimuth data set from the Mexican side of the Gulf of reliance on the quality of the gathers under the salt, making it Mexico (GOM) to show how FWI can improve imaging in the dicult to estimate the dip and residual curvature. Another subsalt. In addition to utilizing the diving-wave energy to derive drawback is their dependence on ray-based tomography, which a reliable model in the shallow sediment overburden, we use is often unstable around salt, to update the velocity. reection FWI (RFWI) to update the velocity model in the deep To circumvent the diving-wave penetration limitation of FWI, area. RFWI utilizes the low-wavenumber components of the reection-based waveform inversions have been proposed in the FWI gradient associated with waves reected in the model, which past (Chavent et al., 1994; Xu et al., 2012) and have recently makes it possible to circumvent the well-known penetration-depth regained traction in the industry. As shown by Mora (1989), limitation of FWI and the shortcomings of traditional tomogra- reection data produce two dierent components in the FWI phy-based methods. is is achieved by alternately using the gradient: the high-wavenumber component, also known as the high-wavenumber and low-wavenumber components of the FWI migration term, and the low-wavenumber component, also known gradient to update density and velocity models, respectively. We as the tomographic term or “rabbit ears.” is tomographic term then use an ultralong-oset, full-azimuth data set from the U.S. is generated along the reection wavepath; therefore, it contains side of the GOM to show that, with more suitable data, FWI signi cant information about the kinematics of the velocity model, and RFWI can be combined to recover the velocity in and around including areas beyond the reach of diving waves. In an attempt complex salt bodies, providing signi cant uplift to subsalt images. to exploit this property of the tomographic term and update the deeper part of the velocity model, we follow the reection FWI Introduction (RFWI) method presented by Gomes and Chazalnoel (2017), Full-waveform inversion (FWI) (Tarantola, 1984) is now which alternately uses the high-wavenumber and low-wavenumber recognized as the method of choice to provide accurate high- components of the FWI gradient to update density and velocity resolution velocity for the shallower part of velocity models (Sirgue models, respectively. and Pratt, 2004). While impressive results have been achieved using FWI for updating the salt and subsalt of the velocity model What FWI can do on synthetic data on synthetic data sets, the same level of bene t has not yet been We rst use a synthetic example to show the capability of achieved on a eld data set. e gap between what FWI achieves FWI with the right data. In this example, we generated synthetic on synthetic data and what FWI achieves on eld data is likely data through acoustic modeling using the BP 2004 model (Billette due to imperfect seismic data. Speci cally, conventional FWI and Brandsberg-Dahl, 2005). Figure 1a shows a portion of the relies on diving waves, the penetration of which is limited to the velocity model that has very complex salt geometry. e initial shallow part of the model due to the limited osets of typical model provided to FWI is a heavily smoothed sediment model acquisitions. Furthermore, good-quality low-frequency signal is without any salt, as shown in Figure 1b. Figure 1c shows the also required to drive the inversion in the right direction when inversion output from FWI, which is very close to the true model the initial model is not close enough to the true model, but good except for the sharp boundary at the sediment and salt interface. low-frequency signal is often lacking in seismic eld data. In this case, FWI does an almost perfect job recovering the Recently, new subsurface imaging developments have been complex model, including the salt geometry and subsalt velocity, proposed to close the gap between FWI requirements and the starting from a model that is far removed from the true model. data sets available, either by extending the minimum-usable low However, to achieve this, the synthetic data we generated for frequency in the seismic data (Dellinger et al., 2016) or by treating the inversion have 30 km ultralong osets and contain low-fre- salt bodies dierently in the FWI objective function (Esser et al., quency signal down to 0.5 Hz. In reality, these requirements are 2015; Datta et al., 2016; Kadu et al., 2016). However, these hard to meet, especially the low-frequency aspect. For eld data, 1CGG. https://doi.org/10.1190/tle37010052.1. 52 THE LEADING EDGE January 2018 Special Section: Advancements in 3D seismic processing the lowest usable frequency normally starts from 4 Hz for a marine FWI result with 4 Hz and 9 km oset data, which is typical for environment; though in some acquisitions the limit can be brought wide-azimuth (WAZ) acquisitions. In this case, FWI fails to down to 2 to 2.5 Hz (Mandroux et al., 2013; Dellinger, 2016). recover the true model, especially for the deeper part, due to the Figures 2b and 2c show the inversion results with data that have lack of diving-wave penetration. lowest usable signal from 2 and 4 Hz, respectively. Even with 30 km ultralong oset, the inversion fails to recover the true RFWI model. To a certain extent, the lack of low frequencies could be To reduce the need for long-oset acquisitions and reliance overcome if a better starting model was achieved with traditional on the refraction energy in the data, RFWI (Xu et al., 2012) methods so that FWI is not compromised by cycle skipping. uses the low-wavenumber component of the FWI gradient of Figure 3a shows another initial model with incorrect salt inter- reection data to update deeper parts of the model. As shown pretation at the rugose top of salt (TOS) and overhang areas. in Figure 4, the FWI gradient is formed by three main compo- Unlike the inversion from the sediment model, FWI successfully nents. While FWI normally relies on the transmitted wave term corrects these misinterpretations with 2 Hz and 30 km oset data, (Figure 4a), RFWI tries to make use of the reected wave terms as shown in Figure 3b. But the oset limitation, which dictates (Figures 4b and 4c), which are able to penetrate deeper into the the penetration of the diving waves, remains an obstacle for model. e high-wavenumber component (Figure 4b), or migra- conventional FWI (Sirgue and Pratt, 2004). Figure 3c shows the tion term, is generated by crosscorrelation of wave elds traveling Figure 1. (a) A portion of the BP 2004 velocity model. (b) Heavily smoothed sediment model. (c) FWI inverted model with frequency starting from 0.5 Hz. Figure 2. (a) FWI inversion starts from 0.5 Hz. (b) FWI inversion starts from 2 Hz. (c) FWI inversion starts from 4 Hz. Figure 3. (a) Perturbed BP 2004 model as initial model for FWI with misinterpretation at TOS and overhang area, indicated by the green arrows. (b) FWI inverted model with 2 Hz and 30 km offset data. The green arrows indicate the inversion is successful at resolving the two misinterpretations in the initial model. (c) FWI inverted model with 4 Hz and 9 km offset data. The blue arrows indicate that the inversion fails to resolve the two misinterpretations in the initial model. Special Section: Advancements in 3D seismic processing January 2018 THE LEADING EDGE 53 in opposite directions, and its wavenumber content normally RFWI implementation, the high-wavenumber and low-wave- has limited impact on the kinematics of the model. e low- number terms are used alternately to update density and velocity wavenumber component, or tomographic term, is generated models, respectively. e high-wavenumber density update along the reection wavepath by crosscorrelation of incident introduces the deep reectors that will generate the back-scattered and reected/scattered wave elds traveling in the same direction energy needed for the next iteration of low-wavenumber velocity (Mora, 1989); therefore, it contains signi cant information about updates. In this latter iteration, since the reector depths are the kinematics of the velocity model and is the base for the self-derived from the current velocity model, the timing of the RFWI velocity update.