Conference Paper

Distributed Acoustic Sensing (DAS) is an advanced event detection technology that has matured over decades across industries. Its potential for real-time monitoring of natural hazards along submarine fiber is now widely recognized. Multiple studies aim to establish reliable correlations between detected signals and geophysical parameters. While data-driven approaches have demonstrated meaningful correlations, high-reliability real-time seismic or tsunami detection also requires advanced modelling. This study examines parameter correlations between submarine DAS data and modelled signal propagation in optical fibres. Using DAS datasets from the MODAS project in the Azores, we implemented a Python-based preprocessing pipeline to convert received DAS data into a standard seismological format. Data analysis techniques were applied to extract key parameters. The DAS instrument measures optical phase changes via Rayleigh backscatter (phase-sensitive OTDR), detecting strain variations up to 50Hz, in every 10m fibre element, along 50 km. We study optical fibre propagation models, focusing on phase evolution, and employ numerical models to refine parameter correlations. The correlation of parameters from submarine DAS data analysis with those from modelled optical signal propagation will be discussed. Results can offer insights into data analysis procedures and calibration techniques. This work is supported by the Portuguese Fundação para a Ciência e Tecnologia, FCT, I.P./MCTES, by the MODAS project 2022.02359.PTDC.