Application of time-adaptive FWI on onshore data in Bohai Bay Basin

One fundamental challenge of full-waveform inversion (FWI) is the local minimum issue caused by the cycle skipping between the predicted and observed data. Our proposed Time-Adaptive FWI (TA-FWI) 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 stability of FWI with inaccurate initial models. TA-FWI utilizes a dynamic time adjustment method to estimate time-shifts, which can handle low signal-to-noise ratio and low-resolution data, and it can adapt to large time-shift variations and achieve better inversion results. Combining this method with least square full-waveform inversion (LS-FWI) can further improve the accuracy and efficiency of velocity inversion. The paper presents a successful application of TA-FWI on onshore data example in Bohai Bay Basin. Results show that, by considering low-frequency signal denoising, surface consistency processing, wavelet estimation, and iterative FWI, a reliable and high-resolution velocity model can be recovered regardless of the initial velocity accuracy.