2005/07/31 by Alvaro Gonzalez, Álvaro González, Miguel Vazquez-Prada +5
Earth and Planetary Sciences · Physics and Astronomy · #Earthquake Detection and Analysis #Earthquake prediction #Earthquake simulation #Econometrics #Fault (geology) #Geological and Geochemical Analysis #Geology #Lithosphere #Remotely triggered earthquakes #Seismic gap #Seismology #Statistics #Stochastic modelling #Tectonics #Unobservable #earthquake and tectonic studies #physics.data-an #physics.geo-ph
paper · pdf · doi:10.1016/j.tecto.2006.03.039
Revised version. Recommended for publication in Tectonophysics
arxiv created 2006/02/13 · openalex publication_date 2006/06/24 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Numerical models are starting to be used for determining the future behaviour of seismic faults and fault networks. Their final goal would be to forecast future large earthquakes. In order to use them for this task, it is necessary to synchronize each model with the current status of the actual fault or fault network it simulates (just as, for example, meteorologists synchronize their models with the atmosphere by incorporating current atmospheric data in them). However, lithospheric dynamics is largely unobservable: important parameters cannot (or can rarely) be measured in Nature. Earthquakes, though, provide indirect but measurable clues of the stress and strain status in the lithosphere, which should be helpful for the synchronization of the models. The rupture area is one of the measurable parameters of earthquakes. Here we explore how it can be used to at least synchronize fault models between themselves and forecast synthetic earthquakes. Our purpose here is to forecast synthetic earthquakes in a simple but stochastic (random) fault model. By imposing the rupture area of the synthetic earthquakes of this model on other models, the latter become partially synchronized with the first one. We use these partially synchronized models to successfully forecast most of the largest earthquakes generated by the first model. This forecasting strategy outperforms others that only take into account the earthquake series. Our results suggest that probably a good way to synchronize more detailed models with real faults is to force them to reproduce the sequence of previous earthquake ruptures on the faults. This hypothesis could be tested in the future with more detailed models and actual seismic data.