Summary of Fully Data-driven Model For Increasing Sampling Rate Frequency Of Seismic Data Using Super-resolution Generative Adversarial Networks, by Navid Gholizadeh and Javad Katebi
Fully Data-Driven Model for Increasing Sampling Rate Frequency of Seismic Data using Super-Resolution Generative Adversarial Networks
by Navid Gholizadeh, Javad Katebi
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 research uses super-resolution generative adversarial networks (SRGANs) to improve the resolution of time-history data in Structural Health Monitoring (SHM) applications. The method transforms raw data into images and then upscales low-resolution images using SRGANs, potentially reducing data storage requirements and simplifying sensor networks. The performance of this approach is compared with traditional enhancement techniques using real seismic data. This study has promising implications for the safety and sustainability of infrastructures worldwide. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computer networks to make sensor readings better in buildings that need to be monitored. They take the old data and turn it into pictures, then use a new type of network to make those pictures clearer. This could help save money by using less storage space and being easier to install. The study tested this method with real earthquake data and found that it works well compared to older ways of doing things. |
Keywords
* Artificial intelligence * Super resolution
