Recent Advances in Early Earthquake Magnitude Estimation by Using Machine Learning Algorithms: A Systematic Review DOI Creative Commons

Andrés Navarro-Rodríguez,

Oscar Castro Artola, Enrique Efrén García-Guerrero

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(7), P. 3492 - 3492

Published: March 22, 2025

Earthquakes are among the most destructive natural phenomena, leading to significant loss of human life and substantial economic damage that severely impacts affected communities. Rapid detection characterization seismic parameters, including location magnitude, crucial for real-time seismological applications, Earthquake Early Warning (EEW) systems. Machine learning (ML) has emerged as a powerful tool enhance accuracy these enabling more efficient responses events different magnitudes. This systematic review aims provide researchers professionals with summary current state ML applications in seismology, particularly on early earthquake magnitude estimations related topics such phase identification. A search was conducted Scopus, ScienceDirect, IEEE Xplore, Web Science databases, covering period from 2014 7 March 2025. The terms included following: (“earthquake magnitude” OR “earthquake warning”) AND (prediction forecasting estimation forecast classification) (“machine learning” “deep “artificial intelligence”). Out 472 articles initially identified, 28 were selected based pre-defined inclusion criteria. described methods algorithms illustrate strong performance despite limited implementation highlights need develop standardized benchmark datasets promote future progress this field.

Language: Английский

Recent Advances in Early Earthquake Magnitude Estimation by Using Machine Learning Algorithms: A Systematic Review DOI Creative Commons

Andrés Navarro-Rodríguez,

Oscar Castro Artola, Enrique Efrén García-Guerrero

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(7), P. 3492 - 3492

Published: March 22, 2025

Earthquakes are among the most destructive natural phenomena, leading to significant loss of human life and substantial economic damage that severely impacts affected communities. Rapid detection characterization seismic parameters, including location magnitude, crucial for real-time seismological applications, Earthquake Early Warning (EEW) systems. Machine learning (ML) has emerged as a powerful tool enhance accuracy these enabling more efficient responses events different magnitudes. This systematic review aims provide researchers professionals with summary current state ML applications in seismology, particularly on early earthquake magnitude estimations related topics such phase identification. A search was conducted Scopus, ScienceDirect, IEEE Xplore, Web Science databases, covering period from 2014 7 March 2025. The terms included following: (“earthquake magnitude” OR “earthquake warning”) AND (prediction forecasting estimation forecast classification) (“machine learning” “deep “artificial intelligence”). Out 472 articles initially identified, 28 were selected based pre-defined inclusion criteria. described methods algorithms illustrate strong performance despite limited implementation highlights need develop standardized benchmark datasets promote future progress this field.

Language: Английский

Citations

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