IEEE Geoscience and Remote Sensing Letters · 2022 · 20 citations · 18 references
Natural SciencesEarth SciencesGeophysicsMachine LearningSeismologyTime Series AnalysisEarthquake Early WarningEarthquake MagnitudeEngineering SeismologyGround Motion CharacterizationSeismic Data ProcessingEarly Warning PredictionEarthquake Detection AlgorithmsEarthquake Early Warning SystemsEarthquake Magnitude EstimationEarthquakesLong Short-term Memory NetworksSeismic Event Detection
In this paper, a method based on Long Short-Term Memory 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 a 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 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 specially TW.
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Sepp Hochreiter, Jürgen Schmidhuber · Neural Computation · 1997 · 98.1K citations
STanford EArthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI
S. Mostafa Mousavi, Yixiao Sheng, Weiqiang Zhu et al. · IEEE Access · 2019 · 434 citations · Full text