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---
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tags:
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- other
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- seismic
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---
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Part of MONSTER: <https://arxiv.org/abs/2502.15122>.
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***LenDB*** consists of seismograms recorded from multiple different seismic detection networks from across the globe [1, 2]. The processed dataset consists of 1,244,942 multivariate time series, with 3 channels, each of length 540, 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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---
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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 processed dataset consists of 1,244,942 multivariate time series, with 3 channels, each of length 540, 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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