DOI: 10.5061/dryad.brv15dvnz
title: Data from: Neural network-based methods for ocean surface wave measurement using submarine distributed acoustic sensing (DAS)

description: The data accompanying the article present two new neural network-based
 methods for estimating ocean surface waves from distributed acoustic
 sensing (DAS) submarine cable strain rate.  Models were trained
 using supervised machine learning on a 10-day dataset collected offshore
 of Oliktok Point, Alaska, in late summer.  The new models were
 trained on target data from seafloor pressure moorings at three sites
 spaced evenly along 27.1 km of cable and were benchmarked against an
 empirical transfer function method previously used to estimate waves from
 DAS. This dataset contains both hourly and half-hourly DAS and mooring
 data used to train the neural networks described in the article. 
 The data span 22 August 2023 to 22 September 2023, excluding the period in
 which DAS measurements were paused (30 August to 18 September) for repairs
 to the submarine fiber-optic cable. Trained model weights and
 normalizations are provided in PyTorch state dict format.
description: # Data from: Neural network-based methods for ocean surface wave
 measurement using submarine distributed acoustic sensing (DAS) Dataset
 DOI: [10.5061/dryad.brv15dvnz](https://doi.org/10.5061/dryad.brv15dvnz) ##
 Description of the data and file structure ### Files and variables DAS and
 mooring data are provided in netCDF (.nc) format and contain
 self-describing global attributes, variable descriptions, and units.
 **oliktok_das_mooring_hourly_dataset.nc** This dataset contains hourly DAS
 features (depth, cosine-square-wave direction, strain-rate spectra) and
 target mooring seafloor pressure spectra for training the spectral neural
 network model. Hourly strain rate and pressure spectral density estimates
 were computed from demeaned DAS and mooring records, respectively. Spectra
 were estimated using Welch's method with 512-point (256-s)
 Hann-windowed segments and 50% overlap. Frequencies above 0.5 Hz were
 trimmed to remove much of the noise floor, and every 4 adjacent
 frequencies were averaged to increase the spectral degrees of freedom
 (reduce noise). The resulting spectra span 0.0078 Hz to 0.49 Hz in 32
 frequency bins. All strain rate spectra within 1 km of each mooring site
 are included (61 channels per site). Depths from NOAA navigation maps
 described in Baker & Abbott (2022) were interpolated at each channel.
 Cosine-squared wave directions were estimated by fitting the ocean surface
 gravity wave dispersion relationship to strain rate frequency-wavenumber
 (f-k) spectra created from 1 km of channels centered on each site, and the
 results were broadcast to all 61 channels at each site.
 **oliktok_das_mooring_half-hourly_dataset.nc** This dataset contains
 half-hourly DAS strain rate f-k spectra and target mooring seafloor
 pressure spectra required to train the f-k convolutional neural network
 model. Strain rate f-k spectra were estimated using two-dimensional arrays
 of raw strain rate from 2 km of DAS channels collected over 30 min. Data
 were segmented along the time axis into 600-point (300-s) segments with
 50% overlap and were tapered with a 2D Hann window function prior to
 computing 2D Fourier transforms and averaging. Frequencies above 0.5 Hz
 were trimmed, and every four frequencies and three wavenumbers were
 merged. Final strain rate f-k spectra were interpolated onto a 32x32 grid
 of frequencies from 0.01 to 0.50 Hz and wavenumbers from +/- 0.00096 to
 0.03 m^(-1), where negative wavenumbers indicate offshore wave
 propagation. Mooring seafloor pressure spectra were estimated using the
 same methods as `oliktok_das_mooring_hourly_dataset.nc` but were
 interpolated onto the same frequency bins used in the f-k spectra.
 **oliktok_das_hourly_along_cable_dataset.nc** This dataset contains hourly
 DAS features (depth, cosine square wave direction, strain rate spectra) in
 2-km along-cable segments with 50% overlap (for a total of 25 segments, or
 artificial "sites"). Data were processed using the same methods
 as `oliktok_das_mooring_hourly_dataset.nc`, except DAS strain rate
 spectra, depth, and cosine squared wave direction were averaged over all
 123 channels within each segment.
 **oliktok_das_half-hourly_along_cable_dataset.nc** This dataset contains
 half-hourly DAS strain rate f-k spectra in 2-km along-cable segments with
 50% overlap (for a total of 25 segments, or artificial "sites").
 Data were processed using the same methods as
 `oliktok_das_mooring_half-hourly_dataset.nc`. **model_states.zip** This
 file contains the trained spectral neural network and f-k convolutional
 neural network model states and fitted min-max normalizations required to
 apply the models. States are provided in PyTorch state dict format (.pth).
 Example code for loading the states and reconstructing the model and
 normalizations are provided in a separate repository hosted on Zenodo. The
 .zip folder includes the following files: *
 `fk_convolutional_neural_network_hyperparameters.pth`: Hyperparameters
 required to reconstruct the f-k CNN model. *
 `fk_convolutional_neural_network_model.pth`: Trained f-k CNN model state.
 * `fk_convolutional_neural_network_spectral_feature_norm.pth`: Fitted f-k
 CNN feature (f-k spectra) min-max normalization. *
 `fk_convolutional_neural_network_target_norm.pt`: Fitted f-k CNN target
 (seafloor pressure spectra) min-max normalization. *
 `spectral_neural_network_hyperparameters.pth`: Hyperparameters required to
 reconstruct the spectral NN model. *
 `spectral_neural_network_model.pth`: Trained spectral NN model state. *
 `spectral_neural_network_scalar_feature_norm.pth`: Fitted spectral NN
 scalar feature (depth, cosine squared wave direction) min-max
 normalization. *
 `spectral_neural_network_spectral_feature_norm.pth`: Fitted spectral NN
 spectral feature (strain rate spectra) min-max normalization. *
 `spectral_neural_network_target_norm.pth`: Fitted spectral NN target
 (seafloor pressure spectra) min-max normalization. **References** Baker,
 M. G., & Abbott, R. E. (2022). Rapid Refreezing of a Marginal Ice Zone
 Across a Seafloor Distributed Acoustic Sensor. Geophysical Research
 Letters, 49(24).
 [https://doi.org/10.1029/2022GL099880](https://doi.org/10.1029/2022GL099880) ## Code/software Example code for training and applying the models is organized into Python notebooks and can be accessed via GitHub at [https://github.com/jacobrdavis/neural_network-based_methods_for_das_ocean_surface_wave_measurement](https://github.com/jacobrdavis/neural_network-based_methods_for_das_ocean_surface_wave_measurement) or via the Zenodo archive at [https://doi.org/10.5281/zenodo.17381091](https://doi.org/10.5281/zenodo.17381091). NetCDF files can be opened using any software that supports the netCDF file format (e.g., the Xarray and netCDF4 Python packages or MATLAB's ncread function).  See [https://www.unidata.ucar.edu/software/netcdf/](https://www.unidata.ucar.edu/software/netcdf/) for more information. Model state files (.pth) can be loaded using PyTorch. PyTorch v2.9.0 state dict (.pth) files are serialized Python OrderedDicts and can be opened using `torch.load`. Example notebooks and code to reconstruct the models are provided in a separate repository hosted on Zenodo. ## Access information Other publicly accessible locations of the data: * The complete April to September 2023 mooring dataset is archived on the Arctic Data center at [https://doi.org/10.18739/A24J0B01J](https://doi.org/10.18739/A24J0B01J) (Thomson and Smith, 2024) #### References: Thomson, J., & Smith, M. (2024). Moorings along the seafloor cable route extending offshore from Oliktok Point, Alaska, from April to September of 2023 [Dataset]. Arctic Data Center. [https://doi.org/doi:10.18739/A24J0B01J](https://doi.org/doi:10.18739/A24J0B01J)