Data inversion immune to cycle-skipping using AWI

Abstract:

Over the last decade, 3D Full Waveform Inversion (FWI) has become a standard model-building tool in exploration seismology, especially in oil and gas applications -thanks to the high quality (spatial density of sources and receivers) datasets acquired by the industry. FWI provides superior quantitative images than its travel-time counterparts (travel-time based inversion methods) because it aims to match all the information in the observations instead of a severely restricted subset of them, namely picked arrivals.

The downside is that the solution space explored by FWI has a high number of local minima, and since the solution is restricted to local optimization methods (due to the objective function evaluation cost), the success of the inversion is subject to starting within the basin of attraction of the global minimum.

Local minima can exist for a wide variety of reasons, and it seems unlikely that a formulation of the problem that can eliminate all of them -by defining the optimization problem in a form that results in a monotonic objective function- exist. However, a significant amount of local minima are created by the definition of data misfit. In its standard formulation FWI compares observed data (field data) with predicted data (generated with a synthetic model) by subtracting one from the other, and the objective function is defined as some norm of this difference. The combination of this criteria and the fact that seismic data is oscillatory produces the well-known phenomenon of cycle-skipping, where model updates try to match nearest cycles from one dataset to the other.

In order to avoid cycle-skipping we propose a different comparison between observed and predicted data, based on Wiener filters, which exploits the fact that the “identity” Wiener filter is a spike at zero lag. This gives rise to a new objective function without cycle-skipped related local minima, and therefore suppress the need of accurate starting models or low frequencies in the data. This new technique, called Adaptive Waveform Inversion (AWI) appears always superior to conventional FWI.