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

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.