Summary of A Framework For Real-time Volcano-seismic Event Recognition Based on Multi-station Seismograms and Semantic Segmentation Models, by Camilo Espinosa-curilem et al.
A Framework for Real-Time Volcano-Seismic Event Recognition Based on Multi-Station Seismograms and Semantic Segmentation Models
by Camilo Espinosa-Curilem, Millaray Curilem, Daniel Basualto
First submitted to arxiv on: 27 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Signal Processing (eess.SP)
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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 novel approach utilizes Semantic Segmentation models to automate seismic event recognition in volcano monitoring by transforming multi-channel 1D signals into 2D representations, enabling their use as images. The framework integrates multi-station seismic data with minimal preprocessing, performing both detection and classification simultaneously for five seismic event classes. Evaluation on approximately 25,000 seismic events recorded at four Chilean volcanoes demonstrates the UNet architecture’s effectiveness, achieving mean F1 and Intersection over Union (IoU) scores of up to 0.91 and 0.88, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps improve volcano monitoring by introducing a new way to recognize seismic events automatically. It uses special models that can transform signal data into images, making it easier to detect and classify different types of seismic events. The method is tested on real-world data from four volcanoes in Chile and shows promising results. |
Keywords
» Artificial intelligence » Classification » Semantic segmentation » Unet
