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On the earthquake predictability of fault interaction models.

Marzocchi W, Melini D - Geophys Res Lett (2014)

Bottom Line: Notwithstanding the popularity of this kind of modeling, its ex-ante skill in terms of earthquake predictability gain is still unknown.Here we show that even in synthetic systems that are rooted on the physics of fault interaction using the Coulomb stress changes, such a kind of modeling often does not increase significantly earthquake predictability.Earthquake predictability of a fault may increase only when the Coulomb stress change induced by a nearby earthquake is much larger than the stress changes caused by earthquakes on other faults and by the intrinsic variability of the earthquake occurrence process.

View Article: PubMed Central - PubMed

Affiliation: INGV Rome, Italy.

ABSTRACT

Space-time clustering is the most striking departure of large earthquakes occurrence process from randomness. These clusters are usually described ex-post by a physics-based model in which earthquakes are triggered by Coulomb stress changes induced by other surrounding earthquakes. Notwithstanding the popularity of this kind of modeling, its ex-ante skill in terms of earthquake predictability gain is still unknown. Here we show that even in synthetic systems that are rooted on the physics of fault interaction using the Coulomb stress changes, such a kind of modeling often does not increase significantly earthquake predictability. Earthquake predictability of a fault may increase only when the Coulomb stress change induced by a nearby earthquake is much larger than the stress changes caused by earthquakes on other faults and by the intrinsic variability of the earthquake occurrence process.

No MeSH data available.


As for Figure 3 but relative to some different parametrizations of the model. The results for other parametrizations of the model are reported in the supporting information.
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fig04: As for Figure 3 but relative to some different parametrizations of the model. The results for other parametrizations of the model are reported in the supporting information.

Mentions: Figure 4 and Table 1 show the results for different model parametrizations (see also Figures A2–A5 in the supporting information). Probability gain is almost never significantly larger than 1; there is only one parametrization (with α = 0, C-ITALY fault network) in which the probability gain is significantly larger than 1, and it is about 5. The synchronization parameter shows that for most parametrizations, D is small and faults do not synchronize in time. Just in one parametrization (LINE fault network and α = 0), D is close to 1, implying strong time synchronization. All these behaviors are well described by the influence factor ξ. For very small values of ξ, the probability gain and the time synchronization are both negligible. The largest ξ values (∼0.3) are obtained for strongly deterministic processes (α = 0) where OP-fault and F-fault have a strong time synchronization (D∼ 1) or show a marked probability gain. In summary, the joint effect in fault interaction modeling played by the fault network geometry, the intrinsic variability of the earthquake occurrence process on each fault, and magnitude of the stress changes induced by surrounding earthquakes can be captured by the influence parameter that is positively correlated to the increase of earthquake predictability of a fault.


On the earthquake predictability of fault interaction models.

Marzocchi W, Melini D - Geophys Res Lett (2014)

As for Figure 3 but relative to some different parametrizations of the model. The results for other parametrizations of the model are reported in the supporting information.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4459208&req=5

fig04: As for Figure 3 but relative to some different parametrizations of the model. The results for other parametrizations of the model are reported in the supporting information.
Mentions: Figure 4 and Table 1 show the results for different model parametrizations (see also Figures A2–A5 in the supporting information). Probability gain is almost never significantly larger than 1; there is only one parametrization (with α = 0, C-ITALY fault network) in which the probability gain is significantly larger than 1, and it is about 5. The synchronization parameter shows that for most parametrizations, D is small and faults do not synchronize in time. Just in one parametrization (LINE fault network and α = 0), D is close to 1, implying strong time synchronization. All these behaviors are well described by the influence factor ξ. For very small values of ξ, the probability gain and the time synchronization are both negligible. The largest ξ values (∼0.3) are obtained for strongly deterministic processes (α = 0) where OP-fault and F-fault have a strong time synchronization (D∼ 1) or show a marked probability gain. In summary, the joint effect in fault interaction modeling played by the fault network geometry, the intrinsic variability of the earthquake occurrence process on each fault, and magnitude of the stress changes induced by surrounding earthquakes can be captured by the influence parameter that is positively correlated to the increase of earthquake predictability of a fault.

Bottom Line: Notwithstanding the popularity of this kind of modeling, its ex-ante skill in terms of earthquake predictability gain is still unknown.Here we show that even in synthetic systems that are rooted on the physics of fault interaction using the Coulomb stress changes, such a kind of modeling often does not increase significantly earthquake predictability.Earthquake predictability of a fault may increase only when the Coulomb stress change induced by a nearby earthquake is much larger than the stress changes caused by earthquakes on other faults and by the intrinsic variability of the earthquake occurrence process.

View Article: PubMed Central - PubMed

Affiliation: INGV Rome, Italy.

ABSTRACT

Space-time clustering is the most striking departure of large earthquakes occurrence process from randomness. These clusters are usually described ex-post by a physics-based model in which earthquakes are triggered by Coulomb stress changes induced by other surrounding earthquakes. Notwithstanding the popularity of this kind of modeling, its ex-ante skill in terms of earthquake predictability gain is still unknown. Here we show that even in synthetic systems that are rooted on the physics of fault interaction using the Coulomb stress changes, such a kind of modeling often does not increase significantly earthquake predictability. Earthquake predictability of a fault may increase only when the Coulomb stress change induced by a nearby earthquake is much larger than the stress changes caused by earthquakes on other faults and by the intrinsic variability of the earthquake occurrence process.

No MeSH data available.