tags:
- time series
- time series classification
- monster
- other
- seismic
pretty_name: LenDB
size_categories:
- 1M<n<10M
license: cc-by-4.0
Part of MONSTER: https://arxiv.org/abs/2502.15122.
| LenDB | |
|---|---|
| Category | Seismic |
| Num. Examples | 1,244,942 |
| Num. Channels | 3 |
| Length | 540 |
| Sampling Freq. | 20 Hz |
| Num. Classes | 2 |
| License | CC BY 4.0 |
| Citations | [1] [2] |
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).
[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.