Strumia, Claudio (2025) Modelling and Interpretation of DAS Amplitudes for Seismic Source Characterization. [Tesi di dottorato]
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| Tipologia del documento: | Tesi di dottorato |
|---|---|
| Lingua: | English |
| Titolo: | Modelling and Interpretation of DAS Amplitudes for Seismic Source Characterization |
| Autori: | Autore Email Strumia, Claudio claudio.strumia@unina.it |
| Data: | 7 Dicembre 2025 |
| Numero di pagine: | 185 |
| Istituzione: | Università degli Studi di Napoli Federico II |
| Dottorato: | Fisica |
| Ciclo di dottorato: | 38 |
| Coordinatore del Corso di dottorato: | nome email Canale, Vincenzo canale@na.infn.it |
| Tutor: | nome email Festa, Gaetano [non definito] |
| Data: | 7 Dicembre 2025 |
| Numero di pagine: | 185 |
| Parole chiave: | DAS; Seismology; Seismic Source |
| Settori scientifico-disciplinari del MIUR: | Area 02 - Scienze fisiche > FIS/06 - Fisica per il sistema terra e il mezzo circumterrestre |
| Informazioni aggiuntive: | Ciclo 38 |
| Depositato il: | 20 Gen 2026 10:16 |
| Ultima modifica: | 12 Ago 2026 05:38 |
| URI: | https://www.fedoa.unina.it/id/eprint/17042 |
Abstract
Distributed Acoustic Sensing (DAS) is a transformative technique that converts fibre optic cables into dense antennas of environmental sensors, enabling the recording of strain rate along the cable path. This rapidly evolving technology is seamlessly integrated into workflows that exploit arrival-time information, although a comprehensive understanding of information hidden in amplitude measurements has yet to be achieved. Here, I provide an overview of the current state of knowledge on DAS with a particular focus on amplitude recordings, reviewing the literature and introducing novel tools for characterizing seismic sources. I present operational workflows for robust conversion from strain rate to displacement, enabling local magnitude estimation and the integration of DAS data into seismic monitoring. I also propose a new mathematical formulation for modelling DAS spectral amplitudes in their native domain to support source parameter estimation. By analysing the first seconds of DAS earthquake recordings, I demonstrate the potential of this technique as a key asset for rapid and accurate real-time magnitude estimation in Earthquake Early Warning systems offshore, where I outline a framework for its integration into real-time monitoring systems, from earthquake location to magnitude estimation. Furthermore, I assess the performance of DAS in detection and characterization of earthquakes for continuous microseismic monitoring, when combined with dense seismic networks. The methods developed in this thesis further highlight the increasing potential of DAS systems for seismic source characterization, from offline processing to real-time monitoring, and support the integration of this technology into seismological practice.
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