Many full-waveform inversion schemes are based on the iterative perturbation theory to fit the observed waveforms. When the observed waveforms lack low frequencies, those schemes may encounter convergence problems due to cycle skipping when the initial velocity model is far from the true model. To mitigate this difficulty, we have developed a new objective function that fits the seismic-waveform intensity, so the dependence of the starting model can be reduced. The waveform intensity is proportional to the square of its amplitude. Forming the intensity using the waveform is a nonlinear operation, which separates the original waveform spectrum into an ultra-low-frequency part and a higher frequency part, even for data that originally do not have low-frequency contents. Therefore, conducting multiscale inversions starting from ultra-low-frequency intensity data can largely avoid the cycle-skipping problem. We formulate the intensity objective function, the minimization process, and the gradient. Using numerical examples, we determine that the proposed method was very promising and could invert for the model using data lacking low-frequency information.
|Original language||English (US)|
|Number of pages||1|
|State||Published - Oct 23 2018|
Bibliographical noteKAUST Repository Item: Exported on 2021-02-19
Acknowledgements: We thank S. Xu, H. Zhou, Y. Luo, G. T. Schuster, and M. Lu for their helpful suggestions and insightful comments. The research was partially funded by Statoil Petroleum (grant no. 4503288025), the National Nature Science Foundation of China (grant nos. 41730425 and 41430321), and The National Oil and Gas Major Project of China (grant no. 2017ZX05008-007).