In this letter, a method based on long short-term memory (LSTM) is presented to address the problem of earthquake magnitude estimation for earthquake early warning (EEW) and tsunami early warning (TW) purposes using a seismic station. An end-to-end-based scheme is adopted, and particular attention is paid to compute the magnitude of seismic events larger than M6 and reduce the effective time for TW. More so, these earthquakes are the ones that cause more fear or uncertainty in the population with the provision of the most significant destructive potential. However, the occurrence of large earthquakes is low, but to counteract the drawback of limited training data, engineered features were also proposed. The earthquake magnitude relative error estimation reported here in experiments with Chilean seismic data was 4.01% and 8.04% with earthquakes M4.0 or larger (up to M8.1) and M4.0 or smaller, respectively, by employing seismic traces in the nearest station to the corresponding seismic event. The average earthquake-nearest station distance was 196 km, and in 26% of the data, this distance was greater than 200 km. These results are competitive with those published elsewhere and suggest the possibility to reduce the time required for EEW and especially TW.

In this paper, we present an automatic method, without human supervision, for the detection and classification of blue whale vocalizations from passive acoustic monitoring (PAM) data using Hidden Markov Model technology implemented with a state-of-the-art machine learning platform, the Kaldi speech processing toolkit. 157.5 hours of PAM data were annotated for model training and testing, selected from a dataset collected from the Corcovado Gulf, Chilean Patagonia in 2016. The system obtained produced 85.3% accuracy for detection and classification of a range of different blue whale vocalizations. This system was then validated by comparing its unsupervised detection and classification results with the published results of southeast Pacific blue whale song phrase (‘SEP2’) via spectrogram cross-correlation, involving a dataset collected with a different hydrophone instrument. The proposed system led to a reduction in the root mean square error relative to published results as high as 80% when compared with comparable methods employed elsewhere. This is a significant step in advancing the monitoring of endangered whale populations in this region, which remains poorly covered in terms of PAM and general ocean observation. With further training, testing and validation, this system can be applied to other target signals and regions of the world ocean.

Exoplanets, short for extra solar planets, are planets outside our solar system. They are objects with masses fewer than around 15 Jupiter-masses that orbit stars other than the sun. They are small enough so they cannot burn deuterium in their cores, yet large enough that they are not so-called dwarf planets like Pluto.