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Detection of seismic quiescences before 1991 Uttarkashi (MW 6.8) and 1999 Chamoli MW (6.6) earthquakes and its implications for stress change sensor

Chingtham Prasanta, Sharma Babita

Acta Geophysica

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2022

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Vol. 70, no 2

509--523

EN

The statistically point process model known as epidemic-type aftershock sequence (ETAS) model is employed for systematically investigating the seismic quiescence or seismic anomalies around the focal regions of large/strong earthquakes for NW Himalaya. For this propose, the model predicted the expected occurrence rates of earthquakes by estimating the model parameters from the earthquake occurrences times using maximum likelihood method, has been used. Then the exhibited relative quiescence due to decreasing occurrence rates from the modeled ones can be identified by inspecting the abnormally downward deviated plot from the extended cumulative curve of the Residual Point Process (RPP) events. Examination of such RPP events in the whole time interval exhibits significant 1.5 years and 2.0 years of relative seismic quiescence before the strong 1991 Uttarkashi (MW 6.8) and 1999 Chamoli (MW 6.6) earthquakes, respectively. Considering the optimally oriented planes of Uttarkashi earthquake, the Coulomb stress changes (ΔCFS) have been investigated to check the rate of seismicity around the focal region of Chamoli earthquake. It has been found that ΔCFS of Uttarkashi earthquake exhibits stress shadow in or near the source zone of Chamoli earthquake and eventually decreases seismicity rates due to seismic quiescence in the source zone. On the other hand, the detected quiescence and activation relative to the predicted seismicity rate are consistent with the obtained Coulomb stress to depict the associated anomalies being sensitive enough to detect a slight stress change in the study region. Henceforth, the increased or decreased seismic activity due to seismic activation or quiescence is found to be consistent with the patterns of the Coulomb’s stress changes calculated on the ruptured fault planes of Uttarkashi earthquake. Hence, this ETAS model based on statistical technique can thus be incorporated with other sensitive geophysical instruments for identifying seismically quiet period not only in the seismic gaps, but also in its neighborhoods along the Himalayan range for mitigating seismic hazards due to impending great earthquakes.

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An updated version of the ETAS model based on multiple change points detection

Benali Amel, Zhuang Jiancang, Talbi Abdelhak

Acta Geophysica

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2022

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Vol. 70, no 5

2013--2031

EN

The stationary Epidemic-Type Aftershock Sequence (ETAS) model is applied to seismicity in Central Italy, in order to study the temporal changes of the corresponding earthquakes time series. However, the residual analysis reveals that some features of the observed seismicity cannot be captured by the stationary ETAS model in its standard formulation. In this case, a decision-tree algorithm is developed to deal with inference problems linked to the estimation of specific time points where stationarity may be potentially broken. Specifically, this algorithm considers the subdivision of the whole time period into two or more subintervals that join in specific time points called change points, where significant time variation in the ETAS parameters is observed. As a result, a three-stage ETAS model with two change points is selected as the best model describing seismicity of the Central Apennines region during the time period 2005–2017, compared to the standard ETAS model. The variation of the estimated ETAS parameters is statistically significant from one stage to another. In particular, the three-stage ETAS estimates of background seismicity rates are found to be increasing from one stage to another over time.

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Declustering of Iran earthquake catalog (1983–2017) using the epidemic‑type aftershock sequence (ETAS) model

Davoudi N., Tavakoli H. R., Zare M., Jalilian A.

Acta Geophysica

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2018

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Vol. 66, no. 6

1359--1373

EN

The main goal of this article is to decluster Iranian plateau seismic catalog by the epidemic-type aftershock sequence (ETAS) model and compare the results with some older methods. For this purpose, Iranian plateau bounded in 24°–42°N and 43°–66°E is subdivided into three major tectonic zones: (1) North of Iran (2) Zagros (3) East of Iran. The extracted earthquake catalog had a total of 6034 earthquakes (Mw > 4) in the time span 1983–2017. The ETAS model is an accepted stochastic approach for seismic evaluation and declustering earthquake catalogs. However, this model has not yet been used to decluster the seismic catalog of Iran. Until now, traditional methods like the Gardner and Knopoff space–time window method and the Reasenberg link-based method have been used in most studies for declustering Iran earthquake catalog. Finally, the results of declustering by the ETAS model are compared with result of Gardner and Knopoff (Bull Seismol Soc Am 64(5):1363–1367, 1974), Uhrhammer (Earthq Notes 57(1):21, 1986), Gruenthal (pers. comm.) and Reasenberg (Geophys Res 90:5479–5495, 1985) declustering methods. The overall conclusion is difficult, but the results confirm the high ability of the ETAS model for declustering Iranian earthquake catalog. Use of the ETAS model is still in its early steps in Iranian seismological researches, and more parametric studies are needed.

4

Emulation of simulated earthquake catalogues

Gallucci M., Guillas S., Vallianatos F.

Acta Geophysica

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2012

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Vol. 60, no. 3

699-718

EN

In earthquake occurrence studies, the so-called q value can be considered both as one of the parameters describing the distribution of inter-event times and as an index of non-extensivity. Using simulated datasets, we compare four kinds of estimators, based on principle of maximum entropy (POME), method of moments (MOM), maximum likelihood (MLE), and probability weighted moments (PWM) of the parameters (q and τ0) of the distribution of inter-events times, assumed to be a generalized Pareto distribution (GPD), as defined by Tsallis (1988) in the frame of non-extensive statistical physics. We then propose to use the unbiased version of PWM estimators to compute the q value for the distribution of inter-event times in a realistic earthquake catalogue simulated according to the epidemic type aftershock sequence (ETAS) model. Finally, we use these findings to build a statistical emulator of the q values of ETAS model. We employ treed Gaussian processes to obtain partitions of the parameter space so that the resulting model respects sharp changes in physical behaviour. The emulator is used to understand the joint effects of input parameters on the q value, exploring the relationship between ETAS model formulation and distribution of inter-event times.