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Calculating an accurate seismic velocity model serves an important role in many seismic imaging techniques. The process of velocity model building is often time-consuming, specifically for anisotropic areas, where more than a single parameter is involved in the process. In the past few years, more time-efficient approaches have been considered to estimate seismic velocity as well as anellipticity parameters or heterogeneity factor using local event slopes. Nevertheless, some of these techniques are not practical due to curvature-dependency, or due to the lack of near-offset data. To address such limitations, we use a curvature-independent approach for normal-moveout correction as well as parameter estimation in vertical transverse isotropic media, which is based on local estimation of vertical traveltime using a shifted hyperbola approximation in the absence of near-offset data. The performance of the proposed approach is tested on synthetic and field common-midpoint gathers. It is also assessed in different signal-to-noise ratios and different missing-near-offset situations. Our findings are consistent with the results achieved by the previous methods that were not developed for sparse data.
Calculating an accurate seismic velocity model serves an important role in many seismic imaging techniques. The process of velocity model building is often time-consuming, specifically for anisotropic areas, where more than a single parameter is involved in the process. In the past few years, more time-efficient approaches have been considered to estimate seismic velocity as well as anellipticity parameters or heterogeneity factor using local event slopes. Nevertheless, some of these techniques are not practical due to curvature-dependency, or due to the lack of near-offset data. To address such limitations, we use a curvature-independent approach for normal-moveout correction as well as parameter estimation in vertical transverse isotropic media, which is based on local estimation of vertical traveltime using a shifted hyperbola approximation in the absence of near-offset data. The performance of the proposed approach is tested on synthetic and field common-midpoint gathers. It is also assessed in different signal-to-noise ratios and different missing-near-offset situations. Our findings are consistent with the results achieved by the previous methods that were not developed for sparse data.
Migration velocity analysis is a crucial seismic processing step that aims to translate residual moveout in common-image gathers (CIGs) into velocity updates. However, this is often an iterative process that requires migration and significant human effort in each iteration. To derive the residual moveout correction accurately and efficiently, we propose a new method that combines a newly designed residual moveout (RMO) normalization and RMO identification. To make training successful, the former is designed to normalize the residual moveout from reflectors with different slopes and different depths to a non-dipping case. To replace manually picking the velocity spectrum, the latter is arranged to recognize normalized frown and smile patterns in CIGs and translate them into velocity updates via convolutional neural networks. Two numerical and field data examples demonstrate that the proposed method can effectively and efficiently flatten CIGs. The proposed method improves the quality of the velocity where there exists manual-picking error in comparison with traditional methods.
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