Summary of Denoising Of Geodetic Time Series Using Spatiotemporal Graph Neural Networks: Application to Slow Slip Event Extraction, by Giuseppe Costantino et al.
Denoising of Geodetic Time Series Using Spatiotemporal Graph Neural Networks: Application to Slow Slip Event Extraction
by Giuseppe Costantino, Sophie Giffard-Roisin, Mauro Dalla Mura, Anne Socquet
First submitted to arxiv on: 6 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This study addresses the challenging task of denoising multivariate time series acquired by irregularly distributed networks of sensors, specifically focusing on geodetic position time series used to monitor ground displacement worldwide. The method, called SSEdenoiser, is designed to handle the spatiotemporal correlation of noise and signal in GNSS data, with an emphasis on revealing slow slip events (SSEs) that are weakly emerging compared to other signals. By combining graph recurrent networks and spatiotemporal Transformers, SSEdenoiser learns latent characteristics of GNSS noise to achieve sub-millimeter precision in detecting SSE-related displacement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about making better use of data from sensors on the ground that helps us understand how the Earth is moving. The problem is that this data can be noisy and hard to understand, so scientists are working on a new way to clean it up. They’re using special computers to look at patterns in the noise and signal together, which helps them find tiny movements in the ground that might indicate big changes happening deep beneath us. |
Keywords
* Artificial intelligence * Precision * Spatiotemporal * Time series
