Velocity-estimation improvements and migration/demigration using the common-reflection surface with continuing deconvolution in the time domain Martina Glöckner 1 , Sergius Dell 1 , Benjamin Schwarz 2 , Claudia Vanelle 1 , and Dirk Gajewski 1 ABSTRACT To obtain an image of the earth’s subsurface, time-imaging methods can be applied because they are reasonably fast, are less sensitive to velocity model errors than depth-imaging methods, and are usually easy to parallelize. A powerful tool for time imaging consists of a series of prestack time migra- tions and demigrations. We have applied multiparameter stacking techniques to obtain an initial time-migration veloc- ity model. The velocity model building proposed here is based on the kinematic wavefield attributes of the common-reflec- tion surface (CRS) method. A subsequent refinement of the velocities uses a coherence filter that is based on a predeter- mined threshold, followed by an interpolation and smoothing. Then, we perform a migration deconvolution to obtain the final time-migrated image. The migration deconvolution consists of one iteration of least-squares migration with an estimated Hessian. We estimate the Hessian by nonstationary matching filters, i.e., in a data-driven fashion. The model building uses the framework of the CRS, and the migration deconvolution is fully automated. Therefore, minimal user in- teraction is required to carry out the velocity model refinement and the image update. We apply the velocity refinement and migration deconvolution approaches to complex synthetic and field data. INTRODUCTION Time migration is an attractive tool for producing subsurface images because it is reasonably fast, less sensitive to the model errors than depth migration, and is usually a massively parallelized technique. A highly focused time image is, however, achievable only with suffi- ciently well-determined migration velocities. Thus, a refinement of the initial time-migration velocities is often applied to obtain an improved final image. In addition, time migration is derived by considering many assumptions, among others, a straight ray propagation, regularly sampled seismic data, and an infinite migration aperture. However, these assumptions are violated when sufficiently complex subsurface structures and field data are considered. Thus, time-migrated images usually suffer from imperfections of the operator, exhibiting artifacts (Hertweck et al., 2003) such as the commonly observed migration swings. Conventionally, a residual moveout (RMO) analysis is used to reduce the impact of the model errors on the image (Yilmaz, 2001). The RMO analysis is an iterative approach to update velocities based on the analysis of the flattening of events in the common-image gathers (CIGs) after time migration. Another approach to perform the velocity update after prestack time migration is common-offset mi- gration followed by the application of the inverse normal moveout (NMO) and subsequent velocity analysis on the newly generated gath- ers. The obtained gathers contain time-migrated reflections with ap- proximately hyperbolic moveout, and they are therefore suitable for classic 1D or multidimensional velocity analysis (Dell et al., 2012). To reduce migration artifacts, several image-enhancement techniques, e.g., dip- or structure-oriented filters, are usually applied after time migration. These methods, however, can introduce a certain smoothing into the migrated images, which may increase uncertainties in fault interpretation. A comprehensive imaging theory based on migration and demi- gration in the depth domain is presented by Hubral et al. (1996) and Tygel et al. (1996). In their work, Huygens’ surfaces and isochrons form the central ingredients, and they are combined with proper am- plitude weighting to preserve the amplitudes. Similar to the works mentioned above, Iversen et al. (2012) present a time-based ap- proach. They use reflection times, slopes, and curvatures as param- Manuscript received by the Editor 1 March 2018; revised manuscript received 16 January 2019; published ahead of production 15 March 2019; published online 3 May 2019. 1 University of Hamburg, Institute of Geophysics, Bundesstrasse 55, Hamburg 20146, Germany. E-mail: martina.gloeckner@uni-hamburg.de; sergiusdell75@ gmail.com; claudia.vanelle@uni-hamburg.de; dirk.gajewski@uni-hamburg.de. 2 University of Oxford, Department of Earth Sciences, South Parks Road, Oxford OX1 3AN, UK. E-mail: benjamin.schwarz@earth.ox.ac.uk. © 2019 Society of Exploration Geophysicists. All rights reserved. S229 GEOPHYSICS, VOL. 84, NO. 4 (JULY-AUGUST 2019); P. S229–S238, 15 FIGS. 10.1190/GEO2018-0173.1 Downloaded 03/25/21 to 139.17.28.154. Redistribution subject to SEG license or copyright; see Terms of Use at https://library.seg.org/page/policies/terms DOI:10.1190/geo2018-0173.1