  Full-waveform inversion (FWI) plays a vital role in geoscience to explore the
subsurface. It utilizes the seismic wave to image the subsurface velocity map.
As the machine learning (ML) technique evolves, the data-driven approaches
using ML for FWI tasks have emerged, offering enhanced accuracy and reduced
computational cost compared to traditional physics-based methods. However, a
common challenge in geoscience, the unprivileged data, severely limits ML
effectiveness. The issue becomes even worse during model pruning, a step
essential in geoscience due to environmental complexities. To tackle this, we
introduce the EdGeo toolkit, which employs a diffusion-based model guided by
physics principles to generate high-fidelity velocity maps. The toolkit uses
the acoustic wave equation to generate corresponding seismic waveform data,
facilitating the fine-tuning of pruned ML models. Our results demonstrate
significant improvements in SSIM scores and reduction in both MAE and MSE
across various pruning ratios. Notably, the ML model fine-tuned using data
generated by EdGeo yields superior quality of velocity maps, especially in
representing unprivileged features, outperforming other existing methods.
