Summary of Path Signatures and Graph Neural Networks For Slow Earthquake Analysis: Better Together?, by Hans Riess et al.
Path Signatures and Graph Neural Networks for Slow Earthquake Analysis: Better Together?
by Hans Riess, Manolis Veveakis, Michael M. Zavlanos
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 paper presents a novel approach, Path Signature Graph Convolutional Neural Networks (PS-GCNN), by integrating path signatures into graph convolutional neural networks (GCNN) to leverage their strengths. Path signatures excel at feature extraction, while GCNNs handle spatial interactions effectively. The proposed method is applied to analyze slow earthquake sequences using GPS timeseries data from a sensor network on New Zealand’s north island. Benchmarking is also performed on simulated stochastic differential equations modeling similar reaction-diffusion phenomena. This methodology shows promise for advancing earthquake prediction and sensor network analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper combines two powerful tools in machine learning to analyze complex data. It brings together “path signatures” that help extract useful features, and “graph neural networks” that can handle data with spatial relationships. The new method is tested on GPS data from a sensor network in New Zealand that tracks slow-moving earthquakes. The results show that this approach can be useful for predicting earthquakes and analyzing data from sensors. |
Keywords
* Artificial intelligence * Diffusion * Feature extraction * Machine learning
