Summary of Physics-guided Full Waveform Inversion Using Encoder-solver Convolutional Neural Networks, by Matan Goren and Eran Treister
Physics-guided Full Waveform Inversion using Encoder-Solver Convolutional Neural Networks
by Matan Goren, Eran Treister
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Physics (physics.comp-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 The paper presents a method to accelerate Full Waveform Inversion (FWI) in the frequency domain by incorporating convolutional neural networks (CNNs) into an encoder-solver preconditioner. The goal is to reduce the computational cost of solving the Helmholtz equation, which is typically solved multiple times during the inversion process. By training the CNN on velocity medium parameters and re-training it between iterations, the approach achieves effective forward simulations throughout the optimization process. This is demonstrated using 2D geophysical models with high-frequency data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper finds a way to make computer simulations faster for a type of problem called Full Waveform Inversion (FWI). This is important because FWI helps us figure out what’s under the Earth’s surface by looking at how waves move through different materials. The researchers use special computers that can recognize patterns, called convolutional neural networks (CNNs), to help make these simulations faster and more accurate. |
Keywords
» Artificial intelligence » Cnn » Encoder » Optimization
