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--- |
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tags: |
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- time series |
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- time series classification |
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- monster |
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- other |
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- seismic |
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pretty_name: LenDB |
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size_categories: |
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- 1M<n<10M |
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license: cc-by-4.0 |
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--- |
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Part of MONSTER: <https://arxiv.org/abs/2502.15122>. |
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|LenDB|| |
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|Category|Seismic| |
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|Num. Examples|1,244,942| |
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|Num. Channels|3| |
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|Length|540| |
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|Sampling Freq.|20 Hz| |
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|Num. Classes|2| |
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|License|[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)| |
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|Citations|[1] [2]| |
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***LenDB*** consists of seismograms recorded from multiple different seismic detection networks from across the globe [1, 2]. The sampling rate is 20 Hz. The processed dataset consists of 1,244,942 multivariate time series, with 3 channels, each of length 540 (i.e., just under 30 seconds of data per time series at a sampling rate of 20 Hz), with two classes: earthquake and noise. This version of the dataset has been split into cross-validation folds based on seismic detection network (i.e., such that seismograms for a given network do not appear in both a training and validation fold). |
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[1] Fabrizio Magrini, Dario Jozinovic, Fabio Cammarano, Alberto Michelini, and Lapo Boschi. (2020). Local earthquakes detection: A benchmark dataset of 3-component seismograms built on a global scale. *Artificial Intelligence in Geosciences*, 1:1–10. |
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[2] Fabrizio Magrini, Dario Jozinovic, Fabio Cammarano, Alberto Michelini, and Lapo Boschi. (2020). LEN-DB Local earthquakes detection: A benchmark dataset of 3-component seismograms built on a global scale. <https://zenodo.org/doi/10.5281/zenodo.3648231>. CC BY 4.0. |
