Highly-sensitive measurements with chirped- pulse phasesensitive otdr
- Autores: Regina Manuela Teixeira Magalhaes
- Directores de la Tesis: Hugo Fidalgo Martins (dir. tes.), Sonia Martín López (codir. tes.)
- Lectura: En la Universidad de Alcalá ( España ) en 2021
- Idioma: inglés
- Tribunal Calificador de la Tesis: Juan Diego Ania Castañón (presid.), Óscar Esteban Martínez (secret.), Luca Schenato (voc.)
- Programa de doctorado: Programa de Doctorado en Electrónica: Sistemas Electrónicos Avanzados. Sistemas Inteligentes por la Universidad de Alcalá
- Materias:
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- Resumen
Distributed optical fiber sensing is currently a very predominant research field, which perceives optical fibers as the potential nervous system of the Earth. Optical fibers are understood as continuous densely-packed sensing arrays, able of retrieving physical quantities from the environment of the fiber.
Some of the most prominent distributed sensing implementations nowadays rely on performing interferometric measurements using the Rayleigh backscattered light, resorting to a technique called Phase-sensitive Optical Time-Domain Reflectometry (CP-¿OTDR). A variant to this technique has been recently proposed in 2016, known as Chirped-Pulse Phase-Sensitive OTDR, which allowed to overcome most of the limitations of traditional ¿OTDR implementations while retaining a simple setup, yielding remarkably high sensitivities.
In this thesis, we aim to optimize the stability and performance of chirped-pulse ¿OTDR systems over long-term measurements, and develop novel paradigm changing applications benefiting from the high sensitivity provided by the technique. We reach a mK-scale long-term stability in ¿OTDR systems, and perform highly sensitive strain, temperature, and refractive index measurements, demonstrating new photonic applications such as distributed bolometry, electro-optical reflectometry, or distributed underwater seismology. We discuss how these applications might be able of increasing the efficiency in the energy field, paving the way towards the development of self-diagnosable grids (smart-grids), and also of revolutionizing next-generation seismological networks, allowing to overcome some of the greatest limitations faced in modern seismology today.
