Section outline

  • Why a training session on SOP data analysis

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    SUBMERSE (SUBMarine cablEs for ReSearch and Exploration) is a EU-funded project that aims to turn existing submarine telecommunication cables into an international fiber-optic sensing network, providing near real-time data flows to monitor the Earth, with innovative applications in environmental sciences and security.

    One of the technical solutions designed by the SUBMERSE project partners is the CESNET’s Polbox, a device that captures the State of Polarisation (SoP). SoP analysis is the study of the values describing the polarisation state of coherent electromagnetic radiation (laser light). These values are subject to change over time, due to any modifications of the environment where the optical cable is situated. Therefore, tracking the SoP changes in response to disturbances along the cable can support detection and analysis of seismic activities, seabed interactions, and oceanographic phenomena, along with artificial events like optical cable manipulation. 

    The main advantage provided by the SUBMERSE approach to SoP is the ability to long-term capture and transmit acquired data at full sampling frequency to the computer for on-the-fly or further processing.

    What this training session is about
    A practical introduction to the analysis of State-of-Polarisation data, acquired by the equipment designed and developed by the SUBMERSE project partners.  Examples on how to display and analyse such data will be presented in interactive Jupyter Notebook. Anyone with basic Python background will be able to follow the trainers and explore simple tasks personally.

    • A practical introduction to State of Polarisation (SoP) data analysis URL
      Colab notebook: https://1url.cz/@IASPEI-SOP
      With your Google Account you can play the notebook live, make your own copy, download it as *.ipynb and work in Jupyter locally.
      For those without google accoun, a backup Jupyter Hub instance is avaiable at: https://1url.cz/@IASPEI-SOP-BACKUP, without local Jupyter, but a EDUGain identity is required.

      Table of content

      • Theoretical Background & SoP Intro: Introduces State of Polarisation (SoP) in optical fibers, Stokes parameters, and the Poincaré sphere for visualization. It explains how external events (like seismic waves) cause polarization transients.

      • Introduction to HDF5: Explains why the Hierarchical Data Format (HDF5) is used for large sensor datasets, its directory-like structure, and provides code examples for creating and reading these files.

      • Time-series & Feature Engineering: Covers fundamental techniques for processing sensor data, including lag features, moving window statistics, Fourier Transforms (for frequency domain analysis), and autocorrelation.

      • CESNET and SUBMERSE Data: This practical section involves downloading real-world HDF5 datasets containing SoP and DAS (Distributed Acoustic Sensing) records of various events like mechanical knocks, heartbeat simulations, and rack door movements.

      • Practical Tasks - Finding Event Properties: Demonstrates how to inspect specific signals, calculate event duration, and compare intensities across different optical channels.

      • Autocorrelation on Dataset 3: Shows how to use autocorrelation to identify periodic patterns (like a rack door opening and closing repeatedly) and estimate event frequency.

      • Aligning SOP and DAS Data: A complex exercise in synchronizing two different types of fiber sensing data to visualize a real earthquake event recorded on a submarine cable near Svalbard.

  • Trainer Panel

    • Rudolf Vohnout, Associate Researcher at CESNET
    • Martin Šlapák, Associate Researcher at CESNET