In this study, we introduce a novel approach for early earthquake detection in urban environments with high ambient noise. By using machine learning techniques to analyze the polarization alterations of light traveling along an existing traffic-carrying optical network, we demonstrate a cost-effective, secure, and efficient solution for detecting primary earthquake wave in noisy conditions. Detecting the primary wave preceding a destructive surface earthquake wave enables the rapid initiation of emergency plans, ensuring timely implementation of earth- quake countermeasures. Our methodology involves collecting large dataset of polarization angular speed evolution along a fiber cable to conduct a Monte Carlo analysis, after integrating the strains induced by car passages over those induced by real earth- quake ground displacement values. This dataset trains a machine learning model that leverages a deep learning architecture based on Long Short - Term Memory layers and attention mechanism. The model’s training and validation show high accuracy rates, implying that additional training is unlikely to yield significant improvements, resulting in a 99% correct detection rate for multi-class classification of all events. The model demonstrates high accuracy in distinguishing between various environmental events, providing accurate early warning signals upon primary wave detection.

Artificial Intelligence Driven Earthquakes Early Detection in Noisy Urban Areas / Awad, Hasan; Usmani, Fehmida; Virgillito, Emanuele; Bratovich, Rudi; Straullu, Stefano; Aquilino, Francesco; Proietti, Roberto; Pastorelli, Rosanna; Curri, Vittorio. - (2024), pp. 65-70. (Intervento presentato al convegno 2024 IEEE Middle East Conference on Communications and Networking (MECOM) tenutosi a Abu Dhabi (UAE) nel 17 - 20 November 2024) [10.1109/mecom61498.2024.10881035].

Artificial Intelligence Driven Earthquakes Early Detection in Noisy Urban Areas

Awad, Hasan;Usmani, Fehmida;Virgillito, Emanuele;Straullu, Stefano;Proietti, Roberto;Pastorelli, Rosanna;Curri, Vittorio
2024

Abstract

In this study, we introduce a novel approach for early earthquake detection in urban environments with high ambient noise. By using machine learning techniques to analyze the polarization alterations of light traveling along an existing traffic-carrying optical network, we demonstrate a cost-effective, secure, and efficient solution for detecting primary earthquake wave in noisy conditions. Detecting the primary wave preceding a destructive surface earthquake wave enables the rapid initiation of emergency plans, ensuring timely implementation of earth- quake countermeasures. Our methodology involves collecting large dataset of polarization angular speed evolution along a fiber cable to conduct a Monte Carlo analysis, after integrating the strains induced by car passages over those induced by real earth- quake ground displacement values. This dataset trains a machine learning model that leverages a deep learning architecture based on Long Short - Term Memory layers and attention mechanism. The model’s training and validation show high accuracy rates, implying that additional training is unlikely to yield significant improvements, resulting in a 99% correct detection rate for multi-class classification of all events. The model demonstrates high accuracy in distinguishing between various environmental events, providing accurate early warning signals upon primary wave detection.
2024
979-8-3503-7672-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2997940