An Effective Scheme for Residual Moveout Picking Using a Markov Decision Process

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.