Dual Reverse Time Migration: Efficient Least-squares Imaging with Multiple Scattering in Dual Space

Abstract

Least-squares reverse time migration (LSRTM) estimates a broadband reflectivity image by iteratively minimizing the data misfit. Despite its superior imaging performance over conventional reverse time migration (RTM), LSRTM is fundamentally limited by the first-order Born approximation and incurs a high computational cost due to the iterative solution of a source-dependent model-space Hessian. We propose dual RTM, a seismic imaging framework that reformulates the inverse problem in the dual space, where the optimization variable is the scattering information of all orders, rather than being limited to the first-order scattering of the Born approximation. Unlike LSRTM, which directly estimates the reflectivity model, dual RTM reconstructs the missing scattering data (dual variable) through a fixed-point iteration governed by the full wave equation, enabling the recovery of higher-order scattering and multiple reflections without repeated background velocity updates. Furthermore, dual RTM replaces the source-dependent model-space Hessian with a source-independent data-space Hessian. A hybrid time--frequency formulation enables this Hessian to be assembled and inverted once in the Fourier domain as an offline preprocessing step, reducing the online computational cost of each dual iteration to that of a single RTM application. Numerical experiments on challenging synthetic benchmarks demonstrate that dual RTM suppresses acquisition footprints, improves amplitude fidelity, and enhances deep imaging where primary reflections alone provide insufficient illumination. These results establish dual RTM as an efficient bridge between conventional RTM/LSRTM and nonlinear waveform inversion for high-resolution large-scale seismic imaging.