TY  - JOUR
AU  - Lu, Yi-Lin
AU  - Huang, Jian-Ping
AU  - Chen, Liang
AU  - Li, Guo-Long
AU  - Wang, Qiu-Yang
PY  - 2026
TI  - Full waveform inversion via BEEMD-based gradient decomposition
JO  - Petroleum Science
SN  - 1672-5107
SP  - 3972
EP  - 3987
VL  - 23
IS  - 7
AB  - 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.
UR  - https://doi.org/10.1016/j.petsci.2026.02.002
DO  - 10.1016/j.petsci.2026.02.002