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Wavelet-like denoising of GNSS data through machine learning. Application to the time series of the Campi Flegrei volcanic area (Southern Italy) / Rolando Carbonari in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)  

Public [article]  inGeomatics, Natural Hazards and Risk > vol 14 n° 1 (2023) . - n° 2187271 Titre : Wavelet-like denoising of GNSS data through machine learning. Application to the time series of the Campi Flegrei volcanic area (Southern Italy) Type de document : Article/Communication Auteurs : Rolando Carbonari, Auteur ; Umberto Riccardi, Auteur ; Prospero De Martino, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 2187271 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] caldeira [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] déformation de la croute terrestre [Termes IGN] données GNSS [Termes IGN] filtrage du bruit [Termes IGN] Naples [Termes IGN] relief volcanique [Termes IGN] risque naturel [Termes IGN] série temporelle [Termes IGN] surveillance géologique [Termes IGN] transformation en ondelettes Résumé : (auteur) The great potential of the Global Navigation Satellite System (GNSS) in monitoring ground deformation is widely recognized. As with other geophysical data, GNSS time series can be significantly noisy, hiding elusive ground deformation signals. Several denoising techniques have been proposed to improve the signal-to-noise ratio over the years. One of the most effective denoising techniques has been proved to be multi-resolution decomposition through the discrete wavelet transform. However, wavelet analysis requires long data sets to be effective, as well as long computation times, that hinder its use as a real or near real-time monitoring tool. We propose training by a Convolutional Neural Network (CNN) to perform the equivalent of wavelet analysis to overcome these limitations. Once trained, the CNN model provides answers within seconds, making it feasible as a real-time data analysis tool. Our Machine Learning algorithm is tested on daily GNSS time series collected in the Campi Flegrei caldera (Southern Italy), which is a highly volcanic risk area. Without significant gaps, the retrieved RMSE and R2 values vary in the ranges 0.65–0.98 and 0.06–0.52 cm, respectively. These results are encouraging, as they hint at the possibility of applying this methodology in more effective real-time monitoring solutions for active volcanoes. Numéro de notice : A2023-180 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1080/19475705.2023.2187271 Date de publication en ligne : 10/03/2023 En ligne : https://doi.org/10.1080/19475705.2023.2187271 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102949 [article]