What type of study is this?

September 10, 2025

Earthquake Forecasting Using Machine Learning

Puntos clave

Improved earthquake forecasting can enhance societal preparedness and reduce economic impacts, which are critical as seismic risks rise.
Using machine learning on large datasets shows potential in forecasting aftershock sequences with more precision and reliability.
Observational capacities and data limitations challenge accurate forecasting of seismic events across varying timescales, particularly for large-magnitude events.
Continued multidisciplinary research will advance the techniques for real-time monitoring and probabilistic modeling in earthquake forecasting.

Resumen

Earthquake forecasting aspires to estimate the likelihood of future seismic events — encompassing their probable location, timing, and magnitude — with the overarching aim of mitigating catastrophic societal and economic consequences. In contrast to precise, deterministic prediction, which remains an elusive goal due to the intricate and inherently non-linear behavior of fault systems, as well as the limitations of current observational capacities, contemporary efforts center on probabilistic assessments spanning a range of temporal scales. Long-term forecasts, extending over decades, draw upon historical seismic records, fault slip rates, and paleoseismic evidence to inform the development of building codes and the delineation of hazard maps. Forecasting at intermediate timescales and short-term horizons, particularly with respect to aftershock sequences — often modelled through frameworks such as ETAS — incorporates real-time seismic monitoring, geodetic strain measurements (via GPS and InSAR), and, with growing prominence, machine learning methodologies applied to vast and heterogeneous datasets. Principal challenges persist, notably the inherent stochasticity of earthquake rupture processes, the formidable difficulty of discerning credible precursory signals amid substantial background noise, the paucity of data concerning rare, large-magnitude events, and the stringent requirements for robust validation of forecasting models. Nevertheless, sustained, multidisciplinary research endeavors continue to advance the resolution, reliability, and practical applicability of earthquake forecasting. These efforts ultimately aspire to fortify societal preparedness, reduce seismic risk, and enhance the resilience of communities in the face of inevitable seismic hazards. Index Terms —Earthquake Forecasting, Machine Learning, Seismic Hazard Assessment, Probabilistic Modeling, Real-time Monitoring, Geodetic Data (GPS, InSAR),Aftershock Prediction, ETAS Model, Fault System Dynamics, Earthquake Precursors

Preguntar a la IA

Me gusta

Guardar