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

Shcherbakov, R.

doi:10.1029/2020jb020887

Cited by 14 publications

(14 citation statements)

References 77 publications

(140 reference statements)

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“…To evaluate the number of forecasted earthquakes by a specific model in the forecasting time interval, the N-test can be used [4,17,19,49]. It tests the distribution range of the number of the forecasted events versus the number of observed earthquakes.…”

Section: Forecast Validationmentioning

confidence: 99%

“…It tests the distribution range of the number of the forecasted events versus the number of observed earthquakes. In addition, in order to test the magnitude distribution of the forecasted earthquakes the M-test can be applied [4,17,19,49]. The N and M-tests examine the consistency of the forecasts with respect to observations, and the R-test can be used to compare the performance of different forecasting models [17].…”

Section: Forecast Validationmentioning

confidence: 99%

“…) is the log-likelihood ratio of the two models. The detailed explanation and implementation of these tests applied to the time-dependent models such as the ETAS and OU rates can be found in Shcherbakov [4].…”

Section: Forecast Validationmentioning

confidence: 99%

“…Moreover, the occurrence of large aftershocks poses a significant risk to the infrastructure that has been affected by a mainshock. Therefore, estimating the probabilities for the occurrence of the largest expected aftershocks plays an important role in post-mainshock decision-making [3,4]. One of the earliest empirical studies of the difference between the magnitude of the mainshock and its largest aftershock was conducted by Båth [5], who postulated that the largest aftershock is on average 1.2 magnitudes lower than the mainshock regardless of the magnitude of the mainshock.…”

Section: Introductionmentioning

confidence: 99%

“…Various forecasting algorithms can also be compared using the aforementioned likelihood-based tests. For example, retrospective aftershock forecasting of the 2011 Tohoku, Japan; 2010 Canterbury, New Zealand; 2016 Kaikoura, New Zealand; and 2019 Ridgecrest, California earthquakes were tested by using this approach [4,[21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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The Analysis of the Aftershock Sequences of the Recent Mainshocks in Alaska

Sedghizadeh

Shcherbakov

2022

Applied Sciences

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The forecasting of the evolution of natural hazards is an important and critical problem in natural sciences and engineering. Earthquake forecasting is one such example and is a difficult task due to the complexity of the occurrence of earthquakes. Since earthquake forecasting is typically based on the seismic history of a given region, the analysis of the past seismicity plays a critical role in modern statistical seismology. In this respect, the recent three significant mainshocks that occurred in Alaska (the 2002, Mw 7.9 Denali; the 2018, Mw 7.9 Kodiak; and the 2018, Mw 7.1 Anchorage earthquakes) presented an opportunity to analyze these sequences in detail. This included the modelling of the frequency-magnitude statistics of the corresponding aftershock sequences. In addition, the aftershock occurrence rates were modelled using the Omori–Utsu (OU) law and the Epidemic Type Aftershock Sequence (ETAS) model. For each sequence, the calculation of the probability to have the largest expected aftershock during a given forecasting time interval was performed using both the extreme value theory and the Bayesian predictive framework. For the Bayesian approach, the Markov Chain Monte Carlo (MCMC) sampling of the posterior distribution was performed to generate the chains of the model parameters. These MCMC chains were used to simulate the models forward in time to compute the predictive distributions. The calculation of the probabilities to have the largest expected aftershock to be above a certain magnitude after a mainshock using the Bayesian predictive framework fully takes into account the uncertainties of the model parameters. Moreover, in order to investigate the credibility of the obtained forecasts, several statistical tests were conducted to compare the performance of the earthquake rate models based on the OU formula and the ETAS model. The results indicate that the Bayesian approach combined with the ETAS model produced more robust results than the standard approach based on the extreme value distribution and the OU law.

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Section: Forecast Validationmentioning

confidence: 99%

Section: Forecast Validationmentioning

confidence: 99%

Section: Forecast Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Analysis of the Aftershock Sequences of the Recent Mainshocks in Alaska

Sedghizadeh

Shcherbakov

2022

Applied Sciences

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A generalized ground-motion model for consistent mainshock–aftershock intensity measures using successive recurrent neural networks

Fayaz

Galasso

2022

Bull Earthquake Eng

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Several recent studies have investigated the risk posed to structures by earthquake sequences, proposing state-dependent fragility/vulnerability models for assets in damaged conditions. However, a critical component for such efforts, i.e., ground-motion record selection, has received relatively minor consideration. Specifically, utilization of “consistent” mainshock (MS)–aftershock (AS) ground motions is desirable in practical applications. Such consistency in selecting MS–AS sequences requires proper consideration of the correlations between and within the intensity measures of MS and AS ground motions. Most of the studies in this domain utilize spectral accelerations as the considered ground-motion intensity measures and rely on empirical linear correlation models between the intensity measures of MS and AS ground motions for developing, for instance, record selection approaches. This study proposes a generalized ground-motion model (GGMM) to estimate consistent 30 × 1 vectors of intensity measures for mainshocks (denoted as IMMS) and aftershocks (denoted as IMAS) using a framework of successive long-short-term-memory (LSTM) recurrent neural network (RNN). The vectors of IMMS and IMAS consist of geometric means of significant duration ($$D_{5 - 95,geom}$$ D 5 - 95 , g e o m ), Arias intensity ($$I_{a,geom}$$ I a , g e o m ), cumulative absolute velocity ($$CAV_{geom}$$ C A V geom ), peak ground velocity ($$PGV_{geom}$$ P G V geom ), peak ground acceleration ($$PGA_{geom}$$ P G A geom ) and RotD50 spectral acceleration ($$S_{a} \left( T \right)$$ S a T ) at 25 periods for both MS and AS ground motions. The proposed RNN-based GGMM is trained on a carefully selected set of ~ 700 crustal and subduction recorded MS–AS sequences. The inputs to the framework include a 5 × 1 vector of source and site parameters for MS and AS recordings. The residuals of the trained LSTM-based RNN are further used to develop empirical covariance structures for IMMS and IMAS. The proposed framework is finally illustrated to select MS–AS ground motions based on IMMS and IMAS using a multi-criteria objective function. The selected MS–AS ground motion sequences are then used to perform non-linear time-history analyses of a case-study two-spanned symmetric bridge structure. The obtained engineering demand parameters are evaluated and critically discussed.

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