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This study enhances the epidemic-type aftershock sequence (ETAS) model for improved earthquake aftershock forecasting. The refined Bayesian procedure offers more accurate predictions by incorporating background seismicity and advanced simulation techniques.

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Area of Science:

  • Earth Sciences
  • Seismology
  • Geophysics

Background:

  • The epidemic-type aftershock sequence (ETAS) model is crucial for short-term aftershock prediction.
  • Previous Bayesian inference methods adapted forecasts using seismic sequence data.
  • Ongoing seismic activity necessitates adaptive and accurate forecasting models.

Purpose of the Study:

  • To improve a previously proposed Bayesian procedure for spatio-temporal aftershock forecasting.
  • To enhance the accuracy and reliability of earthquake aftershock predictions.
  • To validate the improved framework using real seismic data.

Main Methods:

  • Modified the ETAS model's likelihood function to account for piecewise stationary seismicity rates.
  • Analytically calculated the spatial integral of seismicity rates.
  • Explicitly incorporated background seismicity into the forecasting procedure.
  • Employed an adaptive Markov Chain Monte Carlo simulation.
  • Utilized N-test and S-test for forecast verification.

Main Results:

  • The enhanced Bayesian procedure demonstrated improved spatio-temporal aftershock forecasting capabilities.
  • Retrospective analysis of the 2017-2019 Kermanshah seismic sequence validated the framework's effectiveness.
  • The model successfully predicted seismicity in distinct phases following major earthquakes.

Conclusions:

  • The improved Bayesian ETAS model provides a robust framework for aftershock forecasting.
  • The methodology offers enhanced accuracy by integrating advanced statistical techniques and background seismicity.
  • This approach is valuable for seismic hazard assessment and risk management.