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Robust seismicity forecasting based on Bayesian parameter estimation for epidemiological spatio-temporal aftershock

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This study enhances earthquake aftershock forecasting using Epidemic Type Aftershock Sequence (ETAS) models. The new method improves seismic risk mitigation by incorporating uncertainties for more reliable short-term seismicity predictions.

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Area of Science:

  • Earthquake seismology
  • Geophysics
  • Computational statistics

Background:

  • Accurate short-term seismicity forecasts are vital for emergency response and risk mitigation after major earthquakes.
  • Epidemic Type Aftershock Sequence (ETAS) models are standard for predicting earthquake aftershock patterns.
  • Existing models often struggle to fully capture forecast uncertainties.

Purpose of the Study:

  • To develop a robust seismicity forecasting methodology for earthquake aftershock sequences.
  • To integrate Bayesian inference and Markov Chain Monte Carlo simulation for enhanced ETAS modeling.
  • To account for uncertainties in both model parameters and future event occurrences.

Main Methods:

  • Utilized Bayesian inference coupled with Markov Chain Monte Carlo simulation for ETAS model parameter estimation.
  • Incorporated uncertainties associated with model parameters and the stochastic nature of future seismic events.
  • Applied the methodology to the 2016 Amatrice seismic sequence in central Italy for retrospective forecasting.

Main Results:

  • Achieved robust spatio-temporal short-term seismicity forecasts for the Amatrice sequence.
  • Demonstrated reliable prediction of seismicity within two standard deviations of the mean estimate in the hours following main events.
  • Validated the forecasting accuracy across various time intervals in the initial days post-mainshock.

Conclusions:

  • The proposed Bayesian ETAS approach provides more reliable and robust seismicity forecasts.
  • This methodology enhances decision-making for earthquake risk mitigation and emergency management.
  • The approach effectively quantifies uncertainties, leading to improved short-term seismic hazard assessment.