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Fresnel zones are helpful for obtaining a high signal-to-noise ratio (S/N)-migrated result. A migrated dip-angle gather provides a simple domain for estimating 2D Fresnel zones for 3D migration. We develop a deep-learning-based technology to automatically estimate Fresnel zones from migrated dip-angle gathers, thus avoiding the cumbersome task of manually checking and modifying the boundaries of the Fresnel zones. A pair of 1D Fresnel zones are incorporated to represent a 2D Fresnel zone in terms of the inline and crossline dip angles because it is difficult to directly extract 2D Fresnel zones from a 2D dip-angle gather. The proposed convolutional neural network (CNN) is established by modifying VGGNet. As picking boundaries of the Fresnel zones is a regression problem, we remove the last soft-max layer from the VGGNet. The last three convolution layers and a pooling layer are also removed because the feature maps are small enough. To improve the contrast and definition, we enhance the features of the reflected events in the dip-angle gather. Data normalisation is carried out to accelerate the training process using a simple-rescaling method before training the modified VGGNet. Field data examples demonstrate the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Conventional residual static corrections determine the residual statics (time shifts) using common mid-point (CMP) gathers to estimate and correct anomalies induced by the near-surface. These time shifts disregard the phase errors existing in the data, which will reduce the resolution of the stacked image. Further errors result when reflection events inCMP gathers do not exhibit hyperbolic moveout. In order to solve these problems, we propose a robust surface-consistent residual phase correction method that simultaneously resolves both time shifts and constant phase rotations based on migrated gathers. The surface-consistent residual statics and phase are obtained from the migrated gathers expressed in terms of shot and receiver locations.Wemodified the standard technique of estimating the time shift corrections to include a surfaceconsistent constant phase rotation term. The proposed dual parameter algorithm (time shift and constant phase rotation) proved on a synthetic example that it was superior to conventional residual statics for improving the coherence of trace gathers. The computational effort can be reduced by generating migrated gathers and estimating the dual parameters in a spatially varying time window. We applied the proposed method to both synthetic and real data, and improved results were obtained with both. [ABSTRACT FROM AUTHOR]