Summary

One fundamental challenge of full waveform inversion(FWI) is the local minimum issue caused by the cycle skipping between the predicted and acquired data. To overcome this challenge and achieve a successful inversion, a good initial model becomes a necessary ingredient of FWI. In others words, the background model must be accurate enough to start FWI. Our proposed adjustive FWI (AdFWI) is designed to build the relation between travel time shift and model error in a different and novel way, so that FWI can be used to correct the erroneous background model, and therefore mitigate cycle-skipping issues and improve the robustness of FWI with inaccurate initial models.

Introduction

FWI has recently emerged as a promising method for refining detailed seismic velocity fields, which will then benefit migration techniques to achieve enhanced subsurface images. The algorithm iteratively updates the subsurface earth models to reduce the misfit functional measuring the difference between the recorded seismic data and the simulated waveforms. The success of FWI relies essentially on wide-bandwidth, wideazimuth, and wide-aperture seismic data. One fundamental challenge of FWI is the local minimum issue caused by the cycle skipping between the predicted and acquired data. The major cause of this issue is that the acquired data lack lowfrequency information because of the limitation of the physical instruments during the acquisition and the presence of unavoidable noises. To overcome this challenge and achieve a successful inversion, a good initial model is an essential ingredient of FWI. Therefore, the background model must be accurate enough so that FWI may begin.

Some researchers, such as Luo and Schuster (1991), Chavent et al. (1994), and Ma et al. (2013), tried to recover the background model using travel time shift as the misfit function in FWI. This is because travel time shift is more sensitive and more linearly related to the errors in the background model. Our proposed AdFWI is designed to build the relation between travel time shift and model error in a different and novel way, so that FWI can be used to correct the erroneous background model, and therefore mitigate cycle-skipping issues and improve the robustness of FWI with inaccurate initial models.

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