Full waveform inversion of seismic data is often plagued by cycle skipping problems so that an iterative optimization method often gets stuck in a local minimum. To avoid this problem we simplify the objective function so that the iterative solution can quickly converge to a solution in the vicinity of the global minimum. The objective function is simplified by only using parsimonious and important portions of the data, which are defined as skeletonized data. We now present a mostly non-mathematical tutorial that explains the theory of skeletonized inversion. We also show its effectiveness with examples.
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST). We thank the financial support from the sponsors of the Consortium of Subsurface Imaging and Fluid Modeling (CSIM). For computer time, this research used the resources of the IT Research Computing Group and the Supercomputing Laboratory at KAUST. We thank them for providing the computational resources required for carrying out this work.