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ASN OptoDAS (.mat) Svalbard 2020

Interrogator: Alcatel Submarine Networks (ASN) OptoDAS (processed)
Format: MATLAB (.mat)
Profile key: svalbard_v1
Dataset: Bouffaut et al. 2022 — Svalbard (2020) DAS4Whales

This profile reads pre-processed data from the Svalbard DAS4Whales dataset (Bouffaut et al., 2022, Frontiers in Marine Science). The data is distributed as MATLAB .mat files already converted to nanostrain, cropped to the channel range of interest, and annotated with fin whale and blue whale vocalisation events. This is not raw interrogator output — it is a processed scientific dataset.

Reader function

read_svalbard_v1 

read_svalbard_v1(
    path: str,
    stride: Optional[int] = None,
    read_dmin_m: Optional[float] = None,
    read_dmax_m: Optional[float] = None,
    **kwargs
) -> DASDataset

Read a DAS acquisition from the Svalbard DAS4Whale dataset (Bouffaut et al., 2022, Front. Mar. Sci.).

Data is stored as MATLAB HDF5 .mat files. Each file covers a subset of channels along the 120 km Svalbard fiber optic cable (Longyearbyen to open ocean through Isfjorden).

Units

Data is already in nanostrain — no scaling required.

Parameters

path : str Full path to the .mat file. stride : int, optional Channel subsampling factor (tr[::stride, :]).

Returns

DASDataset

Source code in dasexplorer\core\readers_lib\processed.py 
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def read_svalbard_v1(
    path: str,
    stride: Optional[int] = None,
    read_dmin_m: Optional[float] = None,
    read_dmax_m: Optional[float] = None,
    **kwargs,
) -> DASDataset:

    ######################################################################
    ### OPTODAS ASN/ ALCATEL SUBMARINE NETWORK - (.mat) SVALBARD-2020
    ### https://doi.org/10.5281/zenodo.5823343
    ######################################################################

    """
    Read a DAS acquisition from the Svalbard DAS4Whale dataset
    (Bouffaut et al., 2022, Front. Mar. Sci.).

    Data is stored as MATLAB HDF5 .mat files. Each file covers a
    subset of channels along the 120 km Svalbard fiber optic cable
    (Longyearbyen to open ocean through Isfjorden).

    Units
    -----
    Data is already in nanostrain — no scaling required.

    Parameters
    ----------
    path : str
        Full path to the .mat file.
    stride : int, optional
        Channel subsampling factor (tr[::stride, :]).

    Returns
    -------
    DASDataset
    """
    import scipy.io as sio

    mat = sio.loadmat(path)

    # Data: (n_channels, n_time), already in nanostrain
    tr = np.asarray(mat["data"]).astype(np.float32)

    # Sampling parameters
    fs_hz = float(np.asarray(mat["info_sampling_frequency_Hz"]).ravel()[0])
    dt_s  = float(np.asarray(mat["info_sample_interval_s"]).ravel()[0])
    dx_m  = float(np.asarray(mat["info_SSI_m"]).ravel()[0])
    gl_m  = float(np.asarray(mat["info_GL_m"]).ravel()[0])

    # Units
    #raw_units = np.asarray(mat["info_units"]).ravel()
    #units = str(raw_units[0]) if raw_units.size > 0 else "nanostrain"
    units = "nanostrain"

    # Channel distances from shore [m] — use x1_distance_from_shore_m
    dist_m = np.asarray(mat["x1_distance_from_shore_m"]).ravel().astype(np.float64)
    if dist_m.shape[0] != tr.shape[0]:
        dist_m = np.arange(tr.shape[0]) * dx_m

    # Time vector [s]
    time_s = np.asarray(mat["x2_time_s"]).ravel().astype(np.float64)
    if time_s.shape[0] != tr.shape[1]:
        time_s = np.arange(tr.shape[1]) * dt_s

    # UTC start time from info_timestamp (string: e.g. '20200627_052441')
    start_dt = None
    try:
        raw_ts = np.asarray(mat["info_timestamp"]).ravel()
        ts_str = str(raw_ts[0]).strip()
        start_dt = datetime.datetime.strptime(ts_str, "%Y%m%d_%H%M%S").replace(
            tzinfo=datetime.timezone.utc
        )
    except Exception:
        # Fallback: parse from filename YYYYMMDD_HHMMSS_ch...
        m = re.search(r"(\d{8})_(\d{6})", os.path.basename(path))
        if m:
            start_dt = datetime.datetime.strptime(
                m.group(1) + m.group(2), "%Y%m%d%H%M%S"
            ).replace(tzinfo=datetime.timezone.utc)

    downsample = None
    if stride is not None and stride > 1:
        tr     = tr[::stride, :]
        dist_m = dist_m[::stride]
        downsample = stride

    # Spatial crop applied after stride.
    # channel_offset = original (stride=1) cable index of the first kept channel.
    channel_offset = 0
    if read_dmin_m is not None or read_dmax_m is not None:
        dmin = read_dmin_m if read_dmin_m is not None else float(dist_m[0])
        dmax = read_dmax_m if read_dmax_m is not None else float(dist_m[-1])
        mask = (dist_m >= dmin) & (dist_m <= dmax)
        first_idx = int(np.argmax(mask))
        channel_offset = first_idx * int(downsample or 1)
        tr     = tr[mask, :]
        dist_m = dist_m[mask]

    return DASDataset(
        tr=tr,
        dist_m=dist_m,
        time_s=time_s,
        fs_hz=fs_hz,
        start_datetime_utc=start_dt,
        filename=os.path.basename(path),
        reader="svalbard_v1",
        downsample=downsample,
        channel_offset=channel_offset,
        metadata={
            "dx_m": dx_m,
            "gauge_length_m": gl_m,
        },
        units=units,
    )