Introduction of the Hessian in joint migration inversion and improved recovery of structural information using image-based regularization

Abstract

Joint migration inversion (JMI) is a method based on one-way wave equations that aims at fitting seismic reflection data to estimate an image and a background velocity. The depth-migrated image describes the high spatial-frequency content of the subsurface and, in principle, is true amplitude. The background velocity model accounts mainly for the large spatial-scale kinematic effects of the wave propagation. Looking for a deeper understanding of the method, we briefly review the continuous equations that compose the forward-modeling engine of JMI for acoustic media and angle-independent scattering. Then, we use these equations together with the first-order adjoint-state method to arrive at a new formulation of the model gradients. To estimate the image, we combine the second-order adjoint-state method with the truncated-Newton method to obtain the image updates. For the model (velocity) estimation, in comparison to the image update, we reduce the computational cost by adopting a diagonal preconditioner for the corresponding gradient in combination with an image-based regularizing function. Based on this formulation, we build our implementation of the JMI algorithm. Our image-based regularization of the model estimate allows us to carry over structural information from the estimated image to the jointly estimated background model. As demonstrated by our numerical experiments, this procedure can help to improve the resolution of the estimated model and make it more consistent with the image.