Sparse Kirchhoff Migration using a Quasi-Newton Optimization Algorithm

A.A. Aldawood¹ and I. Hoteit¹

¹King Abdullah University of Science and Technology

Abstract

First-arrival Kirchhoff migration consists of summing data over trajectories defined by the propagation rays. However, Kirchhoff imaging often results in a smeared version of the true reflectivity model caused by migration artifacts due to the limited recording aperture, coarse source-receiver sampling, and low subsurface illumination.

Least-square migration can be applied to mitigate the artifacts and enhance the spatial resolution of the image. Gradient-descent methods could be then used to iteratively solve the least-square migration problem in which data residuals are imaged to obtain reflectivity model updates at every iteration. Due to the nature of the L2-norm, Least-square minimization tends to provide a smooth solution that has a large number of non-zero elements for efficient fitting of the data in the L2 sense.