This hypothesis could be tested in the future with more detailed models and actual seismic data. 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 forecasting strategy outperforms others that only take into account the earthquake series. We use these partially synchronized models to successfully forecast most of the largest earthquakes generated by the first 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. Our purpose here is to forecast synthetic earthquakes in a simple but stochastic (random) fault model. Here we explore how it can be used to at least synchronize fault models between themselves and forecast synthetic earthquakes. The rupture area is one of the measurable parameters of earthquakes. 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. However, lithospheric dynamics is largely unobservable: important parameters cannot (or can rarely) be measured in Nature. 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). Their final goal would be to forecast future large earthquakes. Numerical models are starting to be used for determining the future behaviour of seismic faults and fault networks. This simple model is capable of reproducing earthquake dynamics, including the effects due to transient loads such as those imposed by elastic waves, with an efficiency superior to that of the most complicated automata and with less stringent assumptions.

The automaton also considers local dissipation of energy and time-dependent strain applications. Our automaton is based on a homogeneous grid of cells and its rupturing is controlled by a generalized local threshold.

We set the clock back and attempt to derive a dynamically evolving automaton that is as simple as possible and that incorporates all the basic ingredients and includes strain diffusion, a process often disregarded in simple models in spite of its crucial importance. A variety of models have been presented but there seems to be little that can be done to ascertain the merits and defects of each. For this reason a different approach, aimed at reproducing the statistical mechanical properties of earthquakes, has attracted progressively increasing interest. The physics of fractures, which forms the basis of seismic faulting, is not very amenable to simple deterministic differential equations.

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