Section outline

  • Why a training session on DAS data analysis

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    SUBMERSE (SUBMarine cablEs for ReSearch and Exploration) is an innovative 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 and its systems.

    Within this scope, several fibers have been instrumented with ɸ-OTDR interrogators, which are capable of deriving apparent strain measures from phase changes of Rayleigh back-scattering. The interpretation of DAS data can be challenging, as Rayleigh-based systems are inherently sensitive not only to longitudinal strain, but also to temperature and pressure. Furthermore, the finite size of the sensing elements can introduce physical filtering on specific wavelengths, and varying sensitivity that depends on the azimuth from which energy is reaching the fiber. The acquisition parameters (sampling rate, gauge length, overlap) must be balanced for every experiment, considering not only the scientific.

    What this training session is about
    An introduction to Rayleigh-based Distributed Acoustic Sensing (DAS) to take a researcher from knowing nothing about DAS to accessing high-volume data in the cloud. This training session covers the physics of the sensors, the logic of the metadata, and the modern cloud-computing tools required to work with the data without downloading it all.

  • DAS fundamentals

    DAS uses standard fiber optic cables as thousands of virtual sensors by measuring the backscatter of laser pulses to detect vibrations and environmental changes. It is particularly effective for monitoring seismic activity, marine life, and oceanographic changes. 

    In a real subsea environment, the data recorded (apparent strain) is a combined signal of temperature fluctuations, pressure from tides/waves, and actual physical strain. Despite the complexity of the signals, and the gap between theory and reality (the so called "spherical cow" problem), researchers can use the different temporal frequencies of these fields, such as pressure and temperature, to separate and study individual phenomena like internal tides or seismic events.

    • DAS fundamentals -slides File

      In this presentation you will find:

      1. The GeoLab Fibre (slides 1–2): Introduces the GeoLab fibre as a 57 km "dark fibre" used for research in seismology, oceanography, and biology. It details the installation of the OptoDAS interrogator and the facility's role in the Geo-INQUIRE testbed for collaborative research.

      2. Introduction to DAS technology (slides 3–7): Defines DAS as the sampling of vibrations using a linear sensing element (fibre). This section explains the physical principles of Rayleigh backscattering, how phase shifts detect strain, and the use of "gauge lengths" to improve the signal-to-noise ratio.

      3. Cross-sensitivity challenges (slides 8–9): Discusses the "Warning" that apparent strain is not just true strain. It introduces the mathematical complexity of cross-sensitivity, where the fibre's optical properties are simultaneously affected by acoustic, pressure, and temperature fields.

      4. Fibre response to different fields (slides 10–24): Analyzes how the fibre reacts to specific stimuli:

        • Strain: Influenced by ground coupling, fibre age, azimuth, and gauge length.

        • Temperature: Depends on thermal response parameters and variation.

        • Pressure: Affected by seafloor compliance and longitudinal deformation via the Poisson effect.

      5. Signal separation and data extraction (slides 25–29): Explains how to isolate specific signals based on their timescales. For example, band-pass filtering can enhance temperature imprints by removing short-period seismic and long-period pressure contributions. It showcases results such as tidal cycles detected via temperature signals in the deep basin.

      6. Conclusions (slide 30): Summarizes that while correct amplitudes are difficult to obtain due to cross-sensitivity, simple filtering techniques allow for meaningful interpretation even without full calibration.

  • DAS data management: theory

    • DAS Data Management: Metadata and Data Access File PDF
      In this presentation: 

      1. Traditional data workflow in seismology
      2. The DAS data volume challenge
      3. Towards a DAS (meta)data standardisation
      4. Implementation and interim strategy
      5. DAS data formats
      6. File vs Object Storage Systems
      7. Real-time streaming and AAI for data access
      8. Outlook and summary
      9. Available DAS data software tools

  • DAS data management: exercises

    • Access to S3 URL

      This notebook provides a comprehensive tutorial on using the boto3 library in Python to access and download data from S3-compatible object storage.

      Steps covered:

      1. Installation & Imports: Setting up boto3 and configuring it for UNSIGNED access, which allows you to retrieve public data without needing AWS credentials.
      2. Resource vs. Client: Demonstrating the two ways to interact with S3:
        • Resource: A higher-level, object-oriented interface (e.g., using s3r.Bucket).
        • Client: A lower-level interface that maps closely to the actual service API (e.g., using s3c.list_objects).
      3. Data Exploration: How to list buckets, iterate through objects, and inspect metadata like file sizes and keys.
      4. File Operations: Practical examples of downloading files (like README.txt and 3u2023.json) directly to the local environment and reading their contents.

      The examples specifically use the GFZ Potsdam S3 endpoint to explore DAS datasets.

    • How to validate DAS metadata in JSON URL

      This notebook demonstrates a workflow for accessing and validating Distributed Acoustic Sensing (DAS) metadata. 

      Steps covered:

      1. Environment Setup: Installation of boto3 (for S3 access) and jsonschema (for data validation);
      2. S3 Data Retrieval: Configures a boto3 client to access a public S3 endpoint (GFZ Potsdam) using unsigned requests. It downloads a metadata file named 3u2023.json
      3. Schema Fetching: Uses the requests library to fetch the official FDSN DAS-Metadata JSON schema (v2.0) from GitHub;
      4. Validation:
        • Initial validation of the downloaded metadata against the schema to ensure compliance.
        • A demonstration of how schema validation works by intentionally introducing a type error (changing an array to a string) and showing the resulting ValidationError

    • From DAS to Zarr URL

      This notebook demonstrates how to load data from an HDF5 file, convert it to a Zarr array, and then visualise it.  

      Steps covered:

      1. Setting up: Installing the zarr library and importing necessary packages;
      2. Data Loading: Downloading an HDF5 data file from a URL and loading it into an in-memory buffer;
      3. HDF5 Inspection: Opening the HDF5 file and inspecting its 'data' group and 'header' metadata to extract important parameters like time, channels, and units;
      4. Zarr Conversion: Creating a zarr.storage.MemoryStore and converting the HDF5 data into an Xarray Dataset, which is then saved to the Zarr store with specified chunking;
      5. Zarr Access and Visualization: Demonstrating how to open and access the Zarr array directly or as an Xarray Dataset. Finally, it visualises slices of the data using matplotlib.pyplot.imshow, showing the strain rate over time and channel.

  • Trainer panel

    • Javier Quinteros, Researcher and GEOFON Data Centre Manager at GFZ
    • Afonso Loureiro, Researcher at ARDITI / Instituto Dom Luiz

    • In collaboration with Geo-INQUIRE

      Geo-INQUIRE project logo