Prestack correlative elastic least-squares reverse time migration based on wavefield decomposition

Abstract Standard elastic least-squares reverse time migration (ELSRTM) can iteratively improve the quality of stacked reflection images of P- and S-wave impedance by minimizing the L2 norm of waveform residual. However, the resulting images are sensitive to the velocity errors and amplitude errors in the observed data. Moreover, the crosstalk artifacts created by the different wave modes may degrade the imaging quality. To mitigate these problems, a correlative ELSRTM approach based on wavefield decomposition and prestack implementation is developed. This approach includes two key points. The first is that the proposed approach aims at finding the optimal reflection images for each shot recording via minimizing the normalized zero-lag cross-correlation objective function. The final reflection images can be produced by summing the optimal reflection images for all shot recordings. The second is that we develop a wavefield decomposition scheme, which is compatible with the correlative ELSRTM approach, as a gradient precondition to reduce crosstalk artifacts and accelerate the convergence. Numerical examples using several 2D experiments have determined that the proposed ELSRTM approach can provide higher quality images with fewer crosstalk artifacts, compared to the correlative ELSRTM approach without wavefield decomposition. Furthermore, it is less sensitive to velocity errors than the poststack correlative ELSRTM approach.