Gradient normalized least-squares reverse-time migration imaging technology - Sun, Yanfeng; Xu, Xiugang; Tang, Le (Frontiers in Earth Science, 2022)
Least-squares reverse-time migration (LSRTM) can overcome the problems of low resolution and unbalanced amplitude energy in deep formation imaging by reverse-time migration (RTM).It can obtain more accurate imaging profile. In the conventional conjugate gradient LSRTM, when there is no proper precondition operator, the gradient is obtained based on cross-correlation, then the source effect has a great influence on it, and its convergence rate become slow. Based on the conventional conjugate gradient LSRTM, we use the normalized cross-correlation of the source to effectively weaken the influence of the source effect and reduce the low-frequency noise. The idea of normalized cross-correlation of the source is adopted to improve the steepest descent gradient, further accelerate the iterative convergence speed and complete the final migration imaging. Model calculation and actual data processing verify that this method has obvious advantages over conventional methods in reducing source effect, improving convergence speed and enhancing underground deep illumination.