Advances in finite-frequency imaging and inversion — Introduction

Abstract

While real-life seismic data acquired at finite-frequency bands are inherently highly nonlinear in terms of earth properties, seismic imaging and inversion have historically relied on limiting assumptions, such as linearity, with respect to the subsurface structures and infinite bandwidth data. Over the last two decades, however, seismic exploration has seen unprecedented improvements in acquisition systems, forward-modeling algorithms, and computational power that together finally enable geoscientists to begin tackling the nonlinear, finite-frequency aspects of seismic imaging and inversion in realistic subsurface environments. Presently, these problems are under intense investigation by many scientists in both the exploration and global geophysical communities, labeled under the various methods that fall broadly under seismic imaging, inversion, and tomography. Perhaps the most established of such methods, proposed more than 30 years ago by pioneers such as Tarantola, Lailly, and Bamberger, became known as seismic full-waveform inversion (FWI), where the subsurface parameters are estimated through a data-fitting formalism. In parallel to the classical data-fitting approach used in FWI, other alternative approaches have been proposed to address the finite-frequency and nonlinear nature of seismic data, while aiming at overcoming some of the known limitations of traditional FWI. One such example are the so-called image-domain objective functions, which attempt to retrieve an earth model based on the behavior of the subsurface images: differential semblance optimization, wave-equation migration velocity analysis, and subsurface-angle finite-frequency tomography all fall under this …