Summary of Accelerating Full Waveform Inversion by Transfer Learning, By Divya Shyam Singh et al.
Accelerating Full Waveform Inversion By Transfer Learning
by Divya Shyam Singh, Leon Herrmann, Qing Sun, Tim Bürchner, Felix Dietrich, Stefan Kollmannsberger
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper presents Neural Network (NN) based Full Waveform Inversion (FWI), a method that reconstructs material fields using sparse data from wave propagation. By discretizing the material field with an NN, the optimization problem becomes more robust and accurate. The NN weights are iteratively updated to fit simulated wave signals to measured data. Gradient-based optimization requires a suitable initial guess for fast and robust convergence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper talks about how to use computers to figure out what’s inside things, like rocks or buildings, using special waves that go through them. They’re trying to make this process better by using something called Neural Networks (like the ones that help self-driving cars learn). This new method is called NN-based Full Waveform Inversion. It starts with a guess and then adjusts itself until it gets close enough to the real answer. |
Keywords
» Artificial intelligence » Neural network » Optimization
