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Earthquake Forecasting Based on Data Assimilation: Sequential Monte Carlo Methods for Renewal Point Processes : Volume 18, Issue 1 (03/02/2011)

By Werner, M. J.

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Book Id: WPLBN0003982125
Format Type: PDF Article :
File Size: Pages 22
Reproduction Date: 2015

Title: Earthquake Forecasting Based on Data Assimilation: Sequential Monte Carlo Methods for Renewal Point Processes : Volume 18, Issue 1 (03/02/2011)  
Author: Werner, M. J.
Volume: Vol. 18, Issue 1
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Werner, M. J., Ide, K., & Sornette, D. (2011). Earthquake Forecasting Based on Data Assimilation: Sequential Monte Carlo Methods for Renewal Point Processes : Volume 18, Issue 1 (03/02/2011). Retrieved from

Description: Swiss Seismological Service, Institute of Geophysics, ETH Zurich, Switzerland. Data assimilation is routinely employed in meteorology, engineering and computer sciences to optimally combine noisy observations with prior model information for obtaining better estimates of a state, and thus better forecasts, than achieved by ignoring data uncertainties. Earthquake forecasting, too, suffers from measurement errors and partial model information and may thus gain significantly from data assimilation. We present perhaps the first fully implementable data assimilation method for earthquake forecasts generated by a point-process model of seismicity. We test the method on a synthetic and pedagogical example of a renewal process observed in noise, which is relevant for the seismic gap hypothesis, models of characteristic earthquakes and recurrence statistics of large quakes inferred from paleoseismic data records. To address the non-Gaussian statistics of earthquakes, we use sequential Monte Carlo methods, a set of flexible simulation-based methods for recursively estimating arbitrary posterior distributions. We perform extensive numerical simulations to demonstrate the feasibility and benefits of forecasting earthquakes based on data assimilation.

Earthquake forecasting based on data assimilation: sequential Monte Carlo methods for renewal point processes

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