Summary of Enhancing Deep Learning Based Rmt Data Inversion Using Gaussian Random Field, by Koustav Ghosal et al.
Enhancing Deep Learning based RMT Data Inversion using Gaussian Random Field
by Koustav Ghosal, Arun Singh, Samir Malakar, Shalivahan Srivastava, Deepak Gupta
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); 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 The proposed deep learning (DL) method, built on Gaussian Random Fields (GRF), successfully inverts Radio Magnetotelluric data and identifies subsurface resistivity models. The network is trained using an out-of-distribution (OOD) dataset, comprising a homogeneous background and rectangular-shaped anomalous bodies, to enhance generalization ability. Synthetic experiments demonstrate the effectiveness of GRF-based training, which outperforms traditional gradient-based methods in recovering structures in a checkerboard resistivity model. The method’s robustness to noise is also highlighted. Finally, the scheme is tested using field data from a waste site near Roorkee, India. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a deep learning model that can take geophysical data and turn it into useful information about what’s beneath the surface of the Earth. Right now, these models are only good at understanding patterns in the training data they’re shown. To fix this, the researchers used something called Gaussian Random Fields to train their model. This helped it generalize better to new situations, kind of like how you can learn to recognize a face even if you’ve never seen that person before. They tested their method with fake data and real data from an old waste site in India, and it did really well. |
Keywords
» Artificial intelligence » Deep learning » Generalization
