Summary of A Physics-guided Generative Ai Toolkit For Geophysical Monitoring, by Junhuan Yang et al.
A Physics-guided Generative AI Toolkit for Geophysical Monitoring
by Junhuan Yang, Hanchen Wang, Yi Sheng, Youzuo Lin, Lei Yang
First submitted to arxiv on: 6 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Signal Processing (eess.SP); Geophysics (physics.geo-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel machine learning (ML) approach called EdGeo toolkit is introduced to improve full-waveform inversion (FWI) tasks in geoscience. The toolkit employs a diffusion-based model guided by physics principles to generate high-fidelity velocity maps, which are then used to fine-tune pruned ML models. This is achieved by utilizing the acoustic wave equation to generate corresponding seismic waveform data. The results show significant improvements in SSIM scores and reduction in both MAE and MSE across various pruning ratios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, scientists have developed a new tool called EdGeo that helps create more accurate maps of what’s underground by combining machine learning with physics-based methods. This is important because it can help us better understand the Earth’s subsurface and make better predictions about natural phenomena like earthquakes. |
Keywords
* Artificial intelligence * Diffusion * Machine learning * Mae * Mse * Pruning
