@article{Lu2026, 
author = {Yi-Lin Lu and Jian-Ping Huang and Liang Chen and Guo-Long Li and Qiu-Yang Wang},
title = {Full waveform inversion via BEEMD-based gradient decomposition},
year = {2026},
journal = {Petroleum Science},
volume = {23},
number = {7},
pages = {3972-3987},
keywords = {Full waveform inversion, Gradient decomposition, BEEMD, Migration component, Tomography component},
url = {https://www.sciopen.com/article/10.1016/j.petsci.2026.02.002},
doi = {10.1016/j.petsci.2026.02.002},
abstract = {Full waveform inversion (FWI) gradient inherently contains both tomographic and migration components, which are responsible for updating large-scale background velocity and small-scale structural details, respectively. A key challenge in FWI is to decouple these components to ensure a robust, multi-scale inversion strategy. To overcome this, we propose a novel gradient decomposition method based on bidimensional ensemble empirical mode decomposition (BEEMD). In contrast to conventional bidimensional empirical mode decomposition (BEMD), the proposed method employs a noise-assisted ensemble averaging scheme to effectively mitigate mode-mixing artifacts. In this framework, the migration and tomographic components are respectively derived from fine-scale bidimensional intrinsic mode functions (BIMFs) and the large-scale residual. This decoupling allows the tomographic component mitigates cycle-skipping in the presence of inaccurate initial models, whereas the migration component accelerates convergence to a high-resolution solution once the background is well-defined. Sensitivity kernel analyses and numerical experiments on both synthetic and the Marmousi models demonstrate that the proposed method possesses superior stability and robustness compared to conventional approaches.}
}