This repository contains Distributed Acoustic Sensing (DAS) datasets for PhaseNet-DAS. The DAS event format is explained here: Seismic Event Format for DAS.

Datasets

Arcata

The "arcata" dataset is from the Arcata, California, Distributed Acoustic Sensing (DAS) experiment: 2022 M6.4 Ferndale Aftershock Sequence and the Spring 2022 Arcata to Eureka California, DAS experiment.

Period: December 22, 2022 – December 31, 2024
Equipment: Luna QuantX DAS interrogator installed at the Arcata Police Station, connected to fiber-optic telecommunications infrastructure owned by Vero Communications running between Arcata and Eureka, California
Institutions: U.S. Geological Survey (USGS), Cal Poly Humboldt University, Luna Inc.
Files: 2,470 event files, named by timestamp (YYYYMMDDTHHMMSSZ.h5)

This dataset was collected by Jeffrey J. McGuire, Andrew J. Barbour, Connie Stewart, Victor Yartsev, Martin Karrenbach, Mark Hemphill-Haley, Robert C. McPherson, Theresa Sawi, and Clara E. Yoon. Please inform the authors if you utilize this dataset in your research.

Related publication:

McGuire, J.J., Barbour, A.J., Stewart, C., Yartsev, V., Karrenbach, M., Hemphill-Haley, M., McPherson, R.C., Stockdale, K., Yoon, C.E., and Sawi, T., 2025, The GorDAS Distributed Acoustic Sensing Experiment Above the Cascadia Locked Zone and Subducted Gorda Slab. Seismological Research Letters, 96(4): 2489–2503. doi:10.1785/0220240415

Monterey Bay

The "monterey_bay" dataset uses data from the SeaFOAM experiment, a year-long DAS deployment on the Monterey Bay Accelerated Research System (MARS) 52 km offshore cable in Monterey Bay, California.

This dataset was collected by Barbara Romanowicz, Richard Allen, Knute Brekke, Li-Wei Chen, Yuancong Gou, Ivan Henson, Julien Marty, Doug Neuhauser, Brian Pardini, Taka'aki Taira, Stephen Thompson, Junli Zhang, and Stephane Zuzlewski. Please inform the authors if you utilize this dataset in your research.

Related publication:

Romanowicz, B., Allen, R., Brekke, K., Chen, L.-W., Gou, Y., Henson, I., Marty, J., Neuhauser, D., Pardini, B., Taira, T., Thompson, S., Zhang, J., and Zuzlewski, S., 2023, SeaFOAM: A Year‐Long DAS Deployment in Monterey Bay, California. Seismological Research Letters, 94(5): 2348–2359. doi:10.1785/0220230047

Ridgecrest

The "ridgecrest_north" dataset is extracted from The SCEDC Earthquake Data AWS Public Dataset.

Files: 751 event files, named by event ID (ci########.h5)

This dataset is collected by Prof. Zhongwen Zhan (zwzhan@caltech.edu). Please inform the authors if you utilize this dataset in your research.

Data Format

Each .h5 file follows the DAS Seismic Event Format and contains:

data: 2D float32 array with shape (nch, nt) — DAS waveform data in microstrain/s
Attributes on the data dataset:

Attribute	Type	Description
`event_id`	str	Earthquake identifier
`event_time`	str	Origin time
`begin_time`	str	Window start time
`end_time`	str	Window end time
`latitude`	float	Epicenter latitude
`longitude`	float	Epicenter longitude
`depth_km`	float	Depth below surface (km)
`magnitude`	float	Seismic magnitude
`magnitude_type`	str	Magnitude scale
`dt_s`	float	Temporal sampling interval (s)
`dx_m`	float	Spatial channel spacing (m)
`unit`	str	Data measurement unit

Note: Some files (especially in the arcata subset) may have missing event attributes (e.g., location, magnitude).

Usage

Requirements

h5py
numpy
huggingface_hub

Iterate over events

example.py provides DASDataset (a PyTorch Dataset) and helper functions. Files are downloaded on first access and cached locally:

from example import DASDataset

dataset = DASDataset("ridgecrest_north")
event = dataset[0]
print(event["data"].shape, event["event_id"], event["magnitude"])

Use with PyTorch DataLoader

from example import DASDataset
from torch.utils.data import DataLoader

dataset = DASDataset("arcata", max_events=100)
dataloader = DataLoader(dataset, batch_size=1, shuffle=True, num_workers=0)

for batch in dataloader:
    print(batch["data"].shape)
    break

Download a single file

import h5py
from huggingface_hub import hf_hub_download

filepath = hf_hub_download("AI4EPS/quakeflow_das", "arcata/data/20221223T043657Z.h5", repo_type="dataset")

with h5py.File(filepath, "r") as f:
    data = f["data"][:]  # shape: (nch, nt)
    print(f"Waveform shape: {data.shape}")
    for key, val in f["data"].attrs.items():
        print(f"{key}: {val}")

Citation

If you use this dataset, please cite:

@article{zhu2023phasenet_das,
  title={Seismic arrival-time picking on distributed acoustic sensing data using semi-supervised learning},
  author={Zhu, Weiqiang and Biondi, Ettore and Li, Jiaxuan and Yin, Jiuxun and Ross, Zachary E. and Zhan, Zhongwen},
  journal={Nature Communications},
  volume={14},
  pages={8192},
  year={2023},
  doi={10.1038/s41467-023-43355-3}
}

@misc{mcguire2024arcata_das,
  title={Arcata, California, Distributed Acoustic Sensing (DAS) experiment: 2022 M6.4 Ferndale Aftershock Sequence (ver. 3.0, February 2026)},
  author={McGuire, J.J. and Barbour, A.J. and Stewart, C. and Yartsev, V. and Karrenbach, M. and Hemphill-Haley, M. and McPherson, R.C. and Sawi, T. and Yoon, C.E.},
  year={2024},
  publisher={U.S. Geological Survey},
  doi={10.5066/P1V7CKGA}
}

@article{mcguire2025gordas,
  title={The GorDAS Distributed Acoustic Sensing Experiment Above the Cascadia Locked Zone and Subducted Gorda Slab},
  author={McGuire, J.J. and Barbour, A.J. and Stewart, C. and Yartsev, V. and Karrenbach, M. and Hemphill-Haley, M. and McPherson, R.C. and Stockdale, K. and Yoon, C.E. and Sawi, T.},
  journal={Seismological Research Letters},
  volume={96},
  number={4},
  pages={2489--2503},
  year={2025},
  doi={10.1785/0220240415}
}

@article{romanowicz2023seafoam,
  title={SeaFOAM: A Year-Long DAS Deployment in Monterey Bay, California},
  author={Romanowicz, Barbara and Allen, Richard and Brekke, Knute and Chen, Li-Wei and Gou, Yuancong and Henson, Ivan and Marty, Julien and Neuhauser, Doug and Pardini, Brian and Taira, Taka'aki and Thompson, Stephen and Zhang, Junli and Zuzlewski, Stephane},
  journal={Seismological Research Letters},
  volume={94},
  number={5},
  pages={2348--2359},
  year={2023},
  doi={10.1785/0220230047}
}

@article{gou2025leveraging,
  title={Leveraging submarine DAS arrays for offshore earthquake early warning: A case study in Monterey Bay, California},
  author={Gou, Yuancong and Allen, Richard M and Zhu, Weiqiang and Taira, Taka'aki and Chen, Li-Wei},
  journal={Bulletin of the Seismological Society of America},
  volume={115},
  number={2},
  pages={516--532},
  year={2025},
  publisher={Seismological Society of America}
}

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