GC12-FibreOptic-81, updated on 14 May 2024
https://doi.org/10.5194/egusphere-gc12-fibreoptic-81
Galileo conference: Fibre Optic Sensing in Geosciences
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

A Deep Learning Approach for Denoising DAS data in urban volcanic areas

Martina Allegra1,2, Flavio Cannavò1, Miriana Corsaro1,2, Gilda Currenti1, Philippe Jousset3, Simone Palazzo2, Michele Prestifilippo1, and Concetto Spampinato2
Martina Allegra et al.
  • 1Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo, Piazza Roma 2, Catania, Italy
  • 2Department of Electrical, Electronic and Computer Engineering, University of Catania, Viale Andrea Doria 6, Catania, Italy
  • 3GFZ German Research Centre for Geosciences, Einsteinstrasse 42-46, Potsdam 14473, Germany

Among the strengths of Distributed Acoustic Sensing (DAS) applications, the sensing of existing fibre optic cable has certainly aided its extensive adoption in all fields, without excluding the geophysical domain. As a matter of fact, the high-quality of data recording, at high spatial-temporal resolution, has enabled the detection of a variety of seismic-volcanic events, especially in poorly or not at all instrumented environments.

In densely populated areas, the seismic exploration through the deployment of traditional seismic arrays would require high maintenance at prohibitive costs. In contrast, the interrogation of commercial fiber optic infrastructure through DAS technology results in minimal intrusion into urban life in a cost-effective but equally efficient manner.

However, data collection in urban contexts has to deal with unavoidable human interferences that frequently corrupt the seismic signal with anthropogenic noise. Indeed, ground vibrations induced by transportation, industrial and construction activities significantly reduce the signal-to-noise ratio by masking target events.

With the purpose of cleaning the DAS signal, anthropogenic noise removal has been approached with a deep learning technique. Both the neural network architecture and the ad-hoc training procedure have been developed with the aim of maintaining the meaningful features of the original recordings while removing man-induced noise.

Promising validation results on real low-frequency seismic events, detected during the 2021 Vulcano Island unrest, highlight the potential of the suggested method as a pre-processing step to help with the subsequent DAS signal analysis.

How to cite: Allegra, M., Cannavò, F., Corsaro, M., Currenti, G., Jousset, P., Palazzo, S., Prestifilippo, M., and Spampinato, C.: A Deep Learning Approach for Denoising DAS data in urban volcanic areas, Galileo conference: Fibre Optic Sensing in Geosciences, Catania, Italy, 16–20 Jun 2024, GC12-FibreOptic-81, https://doi.org/10.5194/egusphere-gc12-fibreoptic-81, 2024.