A Physics-guided Generative AI Toolkit for Geophysical Monitoring

• URL: http://arxiv.org/abs/2401.03131v1
• Date: Sat, 6 Jan 2024 06:09:05 GMT
• Title: A Physics-guided Generative AI Toolkit for Geophysical Monitoring
• Authors: Junhuan Yang, Hanchen Wang, Yi Sheng, Youzuo Lin, Lei Yang
• Abstract summary: Full-waveform inversion (FWI) plays a vital role in geoscience to explore the subsurface. We introduce the EdGeo toolkit, which employs a diffusion-based model guided by physics principles to generate high-fidelity velocity maps.
• Score: 13.986582633154226
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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