Deep-Learning-Based Migration Velocity Analysis With Multi-Type Input Data

In seismic data processing, velocity model building is always at its core and its accuracy directly affects the quality of subsequent imaging and reservoir characterization. Conventional ray- and wave-equation-based migration velocity analysis (MVA) methods that aim to flatten angle-domain common-image gathers (ADCIGs) require repeated application of prestack depth migration, resulting in a significant computational challenge. To mitigate this issue, we present a deep-learning-based MVA approach to efficiently generate accurate velocity models. Instead of relying solely on the ADCIGs as in conventional MVA, we use multi-type data, including the original ADCIGs, migration velocity model, the envelope functions of the Radon transform of ADCIGs and RGT models, as network inputs, which can provide diverse information to effectively enhance the accuracy of velocity inversion. In addition, to avoid unphysical lateral discontinuities, the multi-type data of several horizontally neighbouring locations rather than a single location are utilized during the network training and prediction. Numerical experiments show that the proposed deep-learning-based MVA method can not only recover the low- and middle-wavenumber velocity model as in conventional MVA approaches but also has the potential to generate high-wavenumber information at the strata interfaces. This improvement can be attributed to the strong nonlinear mapping capability of deep learning, which effectively relates the wide-spectrum velocity information in the label data to ADCIG-related multi-type information in the training data. The high-quality migration results calculated with the velocity models predicted by the network verify the feasibility of the proposed method.