A Fast Global Optimization Algorithm for Regularized Migration Imaging

Abstract

AbstractAt the present, seismic migration usually only yields an image of the positions of geological structures, and can not provide accurate information for subsequent lithology analysis and attributes extraction. To get an image with true amplitude, we suggest that regularized migration imaging should be used. Since the amount of cost for solving the problem is huge and the kernel matrix is sparse, we propose a new hybrid algorithm which is called Memoryless Quasi‐Newton‐Simulated Annealing (MQN‐SA) method. The algorithm not only shows as good performance in searching a local optimized solution as memoryless quasi‐Newton method does, but also reaches the global optimized solution just as simulated annealing algorithm does. The global optimized solution obtained by this method not only gets the right positions, but also contains more reliable information for amplitude and other attributes. Theoretic simulations and field data applications are performed. It reveals that the proposed algorithm can attenuate the migration artifacts and provide a better frequency distribution of estimated reflectivity when a proper seismic modeling operator is constructed. Therefore, the proposed algorithm is very promising for seismic imaging.