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  5. Dataset: Land P and S arrival waveform dataset from the Alaska Amphibious Community Seismic Experiment, 2018-2019

Dataset: Land P and S arrival waveform dataset from the Alaska Amphibious Community Seismic Experiment, 2018-2019

File(s)
Barcheck_AACSE_land_waveform_dataset_README.txt (10.69 KB)
archive_AACSE_metadata_land.tar.gz (3.82 MB)
archive_AACSE_waveforms_land.tar.gz (6.67 GB)
Permanent Link(s)
https://doi.org/10.7298/q2fq-9688
https://hdl.handle.net/1813/119695
Collections
Geophysics and Seismology
Author
Barcheck, Grace
Abstract

This archive contains an earthquake waveform dataset and corresponding metadata generated from onshore seismic data collected in 2018-19 as part of the Alaska Amphibious Community Seismic Experiment (AACSE) (Ruppert et al., 2022, SRL; Barcheck et al., 2020, SRL; Abers et al., 2019, EOS). AACSE was deployed May 2018 through August 2019, and the experiment collected seismic data both on- and off-shore along a stretch of the Alaska-Aleutian subduction zone near the Alaska Peninsula. The Alaska Earthquake Center created the authoritative, analyst-checked earthquake catalog for the experiment (Ruppert et al., 2022). Waveforms are cut out relative to the analyst-checked P and S picks, for all events within 350 km epicentral distance. Data included here are from land seismometers only; no ocean-bottom data are included. Datasets are intended to be used for machine learning training with seismic data.

Description
Please cite as: Grace Barcheck (2026) Dataset: Land P and S arrival waveform dataset from the Alaska Amphibious Community Seismic Experiment, 2018-2019. [dataset] Cornell University Library eCommons Repository. https://doi.org/10.7298/q2fq-9688
Sponsorship
U.S. Geological Survey, Grant No. G22AP00040
Date Issued
2026
Keywords
earthquake
•
seismic waveform
•
Alaska Peninsula
•
machine learning
Related DOI
https://doi.org/10.7298/01da-ka24
https://doi.org/10.1785/0220220226
https://doi.org/10.1785/0220200189
https://doi.org/10.1785/0220210324
https://doi.org/10.1029/2021JB023499
https://doi.org/10.1029/2023EA003332
Related To
Barcheck, Grace (2023) Dataset: Ocean-bottom P and S arrival waveform dataset from the Alaska Amphibious Community Seismic Experiment, 2018-19. [dataset] Cornell University Library eCommons Repository. https://doi.org/10.7298/01da-ka24
Natalia A. Ruppert, Grace Barcheck, Geoffrey A. Abers; Enhanced Regional Earthquake Catalog with Alaska Amphibious Community Seismic Experiment Data. Seismological Research Letters 2022;; 94 (1): 522–530. https://doi.org/10.1785/0220220226
Grace Barcheck, Geoffrey A. Abers, Aubreya N. Adams, Anne Bécel, John Collins, James B. Gaherty, Peter J. Haeussler, Zongshan Li, Ginevra Moore, Evans Onyango, Emily Roland, Daniel E. Sampson, Susan Y. Schwartz, Anne F. Sheehan, Donna J. Shillington, Patrick J. Shore, Spahr Webb, Douglas A. Wiens, Lindsay L. Worthington; The Alaska Amphibious Community Seismic Experiment. Seismological Research Letters 2020;; 91 (6): 3054–3063. https://doi.org/10.1785/0220200189
G. A. Abers, A. N. Adams, P. J. Haeussler, Emily Roland, P. J. Shore, D. A. Wiens, S. Y. Schwartz, A. F. Sheehan, Donna Shillington, S. Webb and Lindsay Lowe Worthington. https://doi.org/10.1029/2019EO117621
Jack Woollam, Jannes Münchmeyer, Frederik Tilmann, Andreas Rietbrock, Dietrich Lange, Thomas Bornstein, Tobias Diehl, Carlo Giunchi, Florian Haslinger, Dario Jozinović, Alberto Michelini, Joachim Saul, Hugo Soto; SeisBench—A Toolbox for Machine Learning in Seismology. Seismological Research Letters 2022;; 93 (3): 1695–1709. https://doi.org/10.1785/0220210324
Münchmeyer, J., Woollam, J., Rietbrock, A., Tilmann, F., Lange, D., Bornstein, T., et al. (2022). Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers. Journal of Geophysical Research: Solid Earth, 127, e2021JB023499. https://doi.org/10.1029/2021JB023499
Bornstein, T., Lange, D., Münchmeyer, J., Woollam, J., Rietbrock, A., Barcheck, G., et al. (2024). PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning. Earth and Space Science, 11, e2023EA003332. https://doi.org/10.1029/2023EA003332
Rights
CC0 1.0 Universal
Rights URI
https://creativecommons.org/publicdomain/zero/1.0/
Type
dataset

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