No abstract

The development of an accurate velocity model is the significant target in the Full Waveform Inversion (FWI) process where the data fitting process is carried out based on an ill-posed technique. In the FWI technique optimization process plays a crucial role through which objective function minimizes, which is related to the misfit function between observed and modelled data. However, the influence of external factors such as data fitting errors (local minima) and the presence of noise in data are involved in the success of this processing technique. The artefacts that arise during gradient computation also affect this processing technique. This study presents a strategy to mitigate the influence of these local minima and other artefacts based on the velocity variation coefficient where an angle-dependent gradient conditioning approach has been proposed. It is an auto-controlled process in which the primary mechanism updates the velocity model from a large angle scale to a smaller angle scale when iteration begins. At each iteration, it preserves the previous result whereby it does not scatter or overlap with the previous one. It covers all the angles smoothly which helps in minimizing the data misfit and providing a high-resolution velocity model. The proposed conditioning approach is demonstrated by implementing the Marmousi model, and the result proves that the method provides a much-improved velocity inversion result which is attained with reasonable iterations. This study represents of a suitable procedure for the FWI processing technique where less sensitive artefacts are identified with negligible time consumption. Furthermore, it also helps to reduce the cycle skips and improve convergence in any complex scenario.

This a preprint and has not been peer reviewed. Data may be preliminary.

No abstract