Sparsity-promoting multiparameter pseudoinverse Born inversion in acoustic media

Abstract

Least-squares reverse-time migration has become the method of choice for quantitative seismic imaging. The main drawback of such scheme is that it requires many migration/modeling cycles. The convergence of least-squares reverse-time migration can be accelerated by using a suitable preconditioner. In the context of extended domain in a variable density acoustic media, the pseudoinverse Born operator is the recommended preconditioner, providing quantitative results within a single iteration. This method consists of two steps: application of the pseudoinverse Born operator, and inversion of two parameters using an efficient weighted least-squares approach based on the Radon transform. As expected, cross-talk artifacts are generated in the second step due to limited acquisition. We present a variable density pseudoinverse Born operator constrained with the ℓ1-norm for each model parameter to suppress the artifacts. The fast iterative shrinkage-thresholding algorithm is used to carry out the optimization problem. In classical iterative least-squares migration, the ℓ1-norm constraints would affect the whole imaging process. As the imaging method is split into two steps, only the Radon transform part is modified, where no wave-based operators are involved. Through numerical experiments, we verify the robustness of the proposed method against different migration artifacts including the parameter cross-talk, interfaces with abrupt truncations, sparse shot acquisition geometry, noisy data and high contrast complex structures.

Target-oriented least-squares reverse-time migration using Marchenko double-focusing: reducing the artefacts caused by overburden multiples

“…However, LSRTM is a computationally expensive algorithm (Dai et al, 2012;Tang, 2009;Herrmann and Li, 2012;Farshad and Chauris, 2021). To reduce the computational cost of LSRTM, one can reduce the model's dimensions by focusing on a small area inside the big block of the subsurface model.…”

Target-oriented least-squares reverse-time migration using Marchenko double-focusing: reducing the artifacts caused by overburden multiples

“…This approach, known as Least-Squares reverse-time migration (LSRTM), is the most common algorithm for producing a high-resolution reflectivity model of the subsurface. Nevertheless, LSRTM is computationally expensive (Dai et al 2012) as it requires many iterations and storing large data volumes (Tang 2009;Herrmann & Li 2012;Farshad & Chauris 2021). Reducing the computational burden of LSRTM is possible by reducing the computational domain.…”