First break picking is crucial for estimating and simulating surface and shallow medium velocities, particularly in complex mountainous regions. Here, the significant variations in near-surface elevation, the thickness of low-velocity zones, and lateral changes in near-surface velocity result in noticeable differences in trace-to-trace arrival times. The manual picking method is inefficient and unrealistic on massive seismic data. Accordingly, various automatic picking methods have been developed over time, including approaches utilizing seismic record attributes and deep learning techniques, among others. In this paper, we proposed a feature template based nonlinear trace time difference (FT-NltD) correction method to correct nonlinear time difference, turning the nonlinear time difference to linear. Then, we proposed geometric structure constraint multi-attribute Markov decision process (GCMDP) for robust and high-precision automatic first break picking. In the GCMDP, we use linear geometry structure as constraint, dynamically optimize in the picking process, and apply multi-step prediction to the first break position and multi-attribute constraint at the same time, so as to realize the first break picking stably. Finally, we use field data demonstrate the effectiveness of the proposed first break picking method.

In seismic exploration, the technique of multiple coverage significantly enhances the quality of migration imaging. Migration imaging results are obtained by stacking the common-image gathers (CIGs) from different shot-receiver pairs. However, due to the irregular observation system and the propagation of seismic waves through complex subsurface media, the illumination intensity of CIGs from different shot-receiver pairs is uneven. Conventional stacking methods often overlook the impact of uneven illumination, resulting in inaccurate amplitudes in the imaging results and poorer imaging quality. In this article, we propose an amplitude-preserving stacking imaging method based on illumination weighting. First, we distinguish the illumination areas by constructing a self-organizing mapping network based on feature attributes. Only regions with high and consistently similar imaging wavelets are considered effective illumination and are included in the stacking imaging process. Then, we implement optimal stacking with an illumination-weighted operator designed based on the energy variation relationship in the effective areas. The proposed method can obtain amplitude-preserving and high-resolution stacking results and enhance imaging quality. Finally, the effectiveness of the proposed method is tested using synthetic and field data.

Water-layer-related multiple (WLRM) is a critical type of surface-related multiple. The model-based multiple prediction method has proven effective for predicting WLRMs while requiring less data acquisition compared to data-driven method. The model-based WLRMs prediction process is described as the convolution of recorded data and the seabed primary response. According to the ray theory, the primary response is typically approximated using the first-arrival traveltimes of seabed primary reflection. However, in rugged seabed case, this ray-theory-based method may reduce the accuracy of WLRMs prediction. This paper proposes a WLRMs prediction method based on Born simulation. The proposed method can characterize the multiple diffractions and multi-arrival reflections generated by rugged seabed, resulting in a more accurate WLRMs prediction result. The prediction process consists of two main steps, namely downward extrapolating the sea surface recorded data to the seabed, and upward extrapolating the multiple scattered wavefield generated by the seabed back to the surface. We efficiently implement the wavefield extrapolation in the water-layer using phase shift operators. The synthetic data validate the accuracy of the proposed method in predicting WLRMs from rugged seabed. Then we successfully applied this method to attenuate WLRMs in 3D field data. The demultiple results demonstrate that the WLRMs in the synthetic and field data are effectively suppressed, thus verifying the effectiveness of the proposed method.

The improvement of parameter estimation accuracy depends on the increase of information. The purpose of structure-constrained scattered data fitting is to build a more accurate field by incorporating structure into a few scattered data. This is important for exploration geophysics because only a few well data can be obtained, while a wide range of subsurface parameters need to be estimated. This article introduces a structure-constrained method named direction-orthogonalizing constraint (DOC), which makes directions parallel to the structure orthogonal to parameter gradients. The structure tensor is utilized to extract directions parallel to the structure from seismic images. Then, a DOC radial basis function (RBF) fitting method is developed. Scattered data are approximated by anisotropic RBFs, and DOC is used to bring in structure. Applications on Marmousi density model, 3-D overthrust model, and 3-D field data demonstrate the efficiency and effectiveness of DOC-RBF fitting.

The curvature of the residual moveout (RMO) on common-image gathers (CIGs), which indicates the error in a migration velocity model, is often used as input data in reflection tomography to produce reliable velocity updates. The conventional semblance-based method is widely used for automatic RMO picking. However, moveout picks are often inaccurate and unreliable in areas where the quality of CIGs is poor and the type of RMO in different traces is complex and variable. In this article, we propose an effective and robust scheme for RMO picking with higher accuracy by utilizing a Markov decision process (MDP). RMO picking on a single CIG is modeled as an MDP in high-dimensional attribute space. The desired RMO can be obtained by maximizing the value function with an optimal policy. Then, an effective implementation scheme of RMO picking on multiple CIGs is designed. First, we extract the reflection horizons on the seismic image stacked by CIGs as seed points. Then, the energy spectrum and similarity coefficient are used to determine the size of the state space and update the position of the initial state. Median filtering and smoothing along the horizons are used to remove abnormal parameters and outliers. Finally, the MDP model is implemented and the optimal decision is realized. Geologically plausible moveouts with high accuracy and robustness are extracted. Numerical examples of synthetic and field data demonstrate the effectiveness and validity of the proposed scheme. Furthermore, this scheme can guarantee accurate and robust input moveout data for subsequent tomography.