DeepQuake — An application of CNN for seismo-acoustic event classification in The Netherlands

Recent developments of infrastructures and methods are major driving forces in the advances of solid Earth sciences. The deployment of large and dense sensor networks enables data centres to acquire data of increased volume and quality. The analysis of such data provides scientists with a better und...

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Bibliographic Details
Published in:Earth and Space Science Open Archive ESSOAr 2020-12
Main Authors: Trani, Luca, Pagani, Giuliano Andrea, Pereira Zanetti, João Paulo, Chapeland, Camille, Evers, Läslo Gerardus
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural networks
Classification
Data centers
Earth sciences
Neural networks
Spectrograms
Waveforms
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Summary:Recent developments of infrastructures and methods are major driving forces in the advances of solid Earth sciences. The deployment of large and dense sensor networks enables data centres to acquire data of increased volume and quality. The analysis of such data provides scientists with a better understanding about natural phenomena in the subsurface. Nevertheless new challenges arise to exploit the growing information potential. Innovative methods based on Artificial Intelligence offer concrete opportunities to tackle those challenges. In this paper we present an investigation of Convolutional Neural Networks (CNN) for seismo-acoustic event classification in the Netherlands. We designed, trained and evaluated two CNN models. Our results suggest that as CNN inputs spectrograms are more suitable than continuous waveforms. We discuss our findings' potential and requirements for their operational adoption. We focus on explainability aspects and offer an approach to pave the way for a broader uptake of Artificial Intelligence based methods.
DOI:10.1002/essoar.10505253.2