Inversion of seismic reflection data in the acoustic approximation

TLDR

The authors solve the nonlinear seismic reflection inverse problem in the acoustic approximation using a generalized least‑squares approach that iteratively updates bulk modulus, density, and source time function through forward propagation of sources and data residuals, accounting for multiple reflections, refractions, and surface waves. The method achieves a general solution comparable in performance to commercial unstacked‑data migration techniques.

Abstract

The nonlinear inverse problem for seismic reflection data is solved in the acoustic approximation. The method is based on the generalized least‐squares criterion, and it can handle errors in the data set and a priori information on the model. Multiply reflected energy is naturally taken into account, as well as refracted energy or surface waves. The inverse problem can be solved using an iterative algorithm which gives, at each iteration, updated values of bulk modulus, density, and time source function. Each step of the iterative algorithm essentially consists of a forward propagation of the actual sources in the current model and a forward propagation (backward in time) of the data residuals. The correlation at each point of the space of the two fields thus obtained yields the corrections of the bulk modulus and density models. This shows, in particular, that the general solution of the inverse problem can be attained by methods strongly related to the methods of migration of unstacked data, and commercially competitive with them.