[2603.00377] Improving Full Waveform Inversion in Large Model Era

Computer Science > Machine Learning arXiv:2603.00377 (cs) [Submitted on 27 Feb 2026] Title:Improving Full Waveform Inversion in Large Model Era Authors:Yinan Feng, Peng Jin, Yuzhe Guo, Yinpeng Chen, Youzuo Lin View a PDF of the paper titled Improving Full Waveform Inversion in Large Model Era, by Yinan Feng and 4 other authors View PDF HTML (experimental) Abstract:Full Waveform Inversion (FWI) is a highly nonlinear and ill-posed problem that aims to recover subsurface velocity maps from surface-recorded seismic waveforms data. Existing data-driven FWI typically uses small models, as available datasets have limited volume, geological diversity, and spatial extent, leading to substantial concerns about overfitting. Although they perform well on synthetic datasets, current methods fail to generalize to more realistic geological structures. In this work, we show that a model trained entirely on simulated and relatively simple data can generalize remarkably well to challenging and unseen geological benchmarks. We provide a working recipe that tames a billion-parameter model for FWI through coordinated scaling across three axes: model capacity, data diversity, and training strategy. Our model achieves state-of-the-art performance on OpenFWI and significantly narrows the generalization gap in data-driven FWI. Across six challenging geophysical benchmarks, including Marmousi, 2D SEG/EAGE Salt and Overthrust, 2004 BP, Sigsbee, and SEAM Phase I, it infers complex structures absent f...