Earthquake productivity law

Shebalin, P.; Narteau, C.; Baranov, Sergey

doi:10.1093/gji/ggaa252

Cited by 33 publications

(15 citation statements)

References 23 publications

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“…This work continues our research into spatiotemporal patterns of seismicity in mining regions. Conducting our investigation into the subject, we validated the productivity law established in our previous works (Baranov and Shebalin, 2020;Shebalin et al, 2020) for the conditions of mining-induced seismicity by showing that the number of the triggered events shocks initiated by an earlier event (productivity) obeys exponential distribution (Baranov et al, 2020). Here, the single parameter of the exponential distribution is independent of the magnitude of the triggering event.…”

Section: Introductionsupporting

confidence: 60%

Spatial Distribution of Triggered Earthquakes in the Conditions of Mining-Induced Seismicity

Baranov

Motorin²,

Shebalin

2021

Izv., Phys. Solid Earth

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Abstract—The spatial distribution of the triggered seismic events in mining conditions in the tectonically loaded rock masses is studied using the example of seismicity in the Khibiny Mountains. It is shown that the distribution of distances from the triggering to triggered events, on average, obeys the power-law with a parameter independent of the magnitude of the triggering event. The model of the maximum distances from a triggering event’s hypocenter to the triggered shocks expected with a given probability is derived. It is shown that the model is consistent with the real data. Based on the error diagram analysis, the guidelines are proposed for the practical use of the model.

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Section: Introductionsupporting

confidence: 60%

Spatial Distribution of Triggered Earthquakes in the Conditions of Mining-Induced Seismicity

Baranov

Motorin²,

Shebalin

2021

Izv., Phys. Solid Earth

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“…Another disadvantage of the stochastic declustering methods is their basic hypothesis that the number of aftershocks is a function of the magnitude of the corresponding main shock. This hypothesis, as was recently found, is not true (Shebalin et al, 2020).…”

Section: Selection Of Aftershock Sequencesmentioning

confidence: 70%

“…All aftershocks (as described above, we consider only direct aftershocks of the large earthquakes considered using the nearest neighbor approach), both onshore and offshore ones, were used to estimate the parameters of the Omori-Utsu law (Utsu, 1961), but only the offshore aftershocks were used to calculate the effects of ocean tides on seismicity. For selection of direct aftershocks in Kamchatka and New Zealand we used the parameters listed in Supplementaty Table S1 of (Shebalin et al, 2020).…”

Section: Selection Of Aftershock Sequencesmentioning

confidence: 99%

Aftershock Rate Changes at Different Ocean Tide Heights

Shebalin

Baranov

2020

Front. Earth Sci.

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The differential probability gain approach is used to estimate quantitatively the change in aftershock rate at various levels of ocean tides relative to the average rate model. An aftershock sequences are analyzed from two regions with high ocean tides, Kamchatka and New Zealand. The Omori-Utsu law is used to model the decay over time, hypothesizing an invariable spatial distribution. Ocean tide heights are considered rather than phases. A total of 16 sequences of M ≥6 aftershocks off Kamchatka and 15 sequences of M ≥6 aftershocks off New Zealand are examined. The heights of the ocean tides at various locations were modeled using FES 2004. Vertical stress changes due to ocean tides are here about 10–20 kPa, that is, at least several times greater than the effect due to Earth tides. An increase in aftershock rate is observed by more than two times at high water after main M ≥6 shocks in Kamchatka, with slightly less pronounced effect for the earthquakes of M = 7.8, December 15, 1971 and M = 7.8, December 5, 1997. For those two earthquakes, the maximum of the differential probability gain function is also observed at low water. For New Zealand, we also observed an increase in aftershock rate at high water after thrust type main shocks with M ≥6. After normal-faulting main shocks there was the tendency of the rate increasing at low water. For the aftershocks of the strike-slip main shocks we observed a less evident impact of the ocean tides on their rate. This suggests two main mechanisms of the impact of ocean tides on seismicity rate, an increase in pore pressure at high water, or a decrease in normal stress at low water, both resulting in a decrease of the effective friction in the fault zone.

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“…However, large uncertainties associated with these model parameters can result in significant underestimation/overestimation of the probabilities for the largest expected events or the numbers of earthquakes above a certain magnitude during the forecasting time intervals. This is particularly evident for the Omori‐Utsu law, where the productivity of the process is controlled by the K o parameter, which is typically estimated with large uncertainties (Marsan & Helmstetter, 2017; Shebalin et al., 2020). On the other hand, the Bayesian framework fully incorporates these model uncertainties into the computation of the probabilities.…”

Section: Discussionmentioning

confidence: 99%

Statistics and Forecasting of Aftershocks During the 2019 Ridgecrest, California, Earthquake Sequence

Shcherbakov

2021

JGR Solid Earth

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The 2019 Ridgecrest, California, earthquake sequence represents a complex pattern of seismicity that is characterized by the occurrence of a well‐defined foreshock sequence followed by a mainshock and subsequent aftershocks. In this study, a detailed statistical analysis of the sequence is performed. Particularly, the parametric modeling of the frequency‐magnitude statistics and the earthquake occurrence rate is carried out. It is shown that the clustering of earthquakes plays an important role during the evolution of this sequence. In addition, the problem of constraining the magnitude of the largest expected aftershocks to occur during the evolution of the sequence is addressed. In order to do this, two approaches are considered. The first one is based on the extreme value theory, whereas the second one uses the Bayesian predictive framework. The latter approach has allowed to incorporate the complex earthquake clustering through the Epidemic Type Aftershock Sequence (ETAS) process and the uncertainties associated with the model parameters into the computation of the corresponding probabilities. The results indicate that the inclusion of the foreshock sequence into the analysis produces higher probabilities for the occurrence of the largest expected aftershocks after the M7.1 mainshock compared to the approach based on the extreme value distribution combined with the Omori‐Utsu formula for the earthquake rate. Several statistical tests are applied to verify the forecast.

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Earthquake productivity law

Cited by 33 publications

References 23 publications

Spatial Distribution of Triggered Earthquakes in the Conditions of Mining-Induced Seismicity

Spatial Distribution of Triggered Earthquakes in the Conditions of Mining-Induced Seismicity

Aftershock Rate Changes at Different Ocean Tide Heights

Statistics and Forecasting of Aftershocks During the 2019 Ridgecrest, California, Earthquake Sequence

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