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Part of MONSTER: https://arxiv.org/abs/2502.15122.
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).
[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.
[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.
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