Multiscale full-waveform inversion based on gradient bidimensional empirical mode decomposition

ABSTRACT In seismic exploration, establishing a reliable velocity model is crucial for seismic migration imaging and reservoir characterization. Full-waveform inversion (FWI) is used to recover subsurface physical parameters by iteratively minimizing the misfit between the observed and synthetic data, theoretically offering the highest modeling accuracy. FWI is a combination of migration imaging and tomography. However, for reflection data, the update of the tomographic component is notably weaker than the migration component. When the gradient is dominated by the migration component, FWI can easily fall into local minima, which leads to bad inversion results. Hence, it is necessary to extract the tomographic component from the original FWI gradient to build a robust background velocity model when the initial model is inaccurate. In this paper, we develop to use the bidimensional empirical mode decomposition (BEMD) method to separate the original FWI gradient into the tomography and migration component. The BEMD method first adaptively decomposes the gradient into different-scale subcomponents. Then, the small-scale bidimensional intrinsic mode functions are combined into the migration component, and the large-scale residual component is considered to be the tomographic component. At the beginning of FWI, the background velocity is updated using the tomographic component, and with iterations, small-scale components are gradually added to update the velocity model. Numerical experiments show that the BEMD method can effectively separate the two components. The BEMD-based FWI can reconstruct the background velocity model, improving the FWI accuracy.