Jiancang Zhuang

Stochastic Declustering of Space-Time Earthquake Occurrences

This article is concerned with objective estimation of the spatial intensity function of the background earthquake occurrences from an earthquake catalog that includes numerous clustered events in space and time, and also with an algorithm for producing declustered catalogs from the original catalog. A space-time branching process model (the ETAS model) is used for describing how each event generates offspring events. It is shown that the background intensity function can be evaluated if the total spatial seismicity intensity and the branching structure can be estimated. In fact, the whole space-time process is split into two subprocesses, the background events and the clustered events. The proposed algorithm combines a parametric maximum likelihood estimate for the clustering structures using the space-time ETAS model and a nonparametric estimate of the background seismicity that we call a variable weighted kernel estimate. To demonstrate the present methods, we estimate the background seismic activities in the central region of New Zealand and in the central and western regions of Japan, then use these estimates to produce catalogs of background events.

A study on the background and clustering seismicity in the Taiwan region by using point process models

[1] This paper investigates the shallow seismicity occurring in the Taiwan region during the 20th century using a stochastic declustering method that has been developed on the basis of the theory of the epidemic-type aftershock sequence model. It provides a probability based tool to objectively separate the space-time occurrences of earthquakes into a background and a clustering component. On the basis of the background and clustering seismicity rates, we discuss the correlation between the distribution of the cluster ratio and the regional seismotectonic structures. Specifically, we find that the areas of the highest clustering ratio correspond to the major strike-slip fault traces in and around Taiwan. Additionally, in the Taiwan inland region, during the period 1960-1990, the outputs for the stochastically declustered catalogue show a clear quiescence in background seismicity preceding the recovery of activation and the occurrences of the 1999 Chi-Chi earthquake of M L 7.3, while the other active regions show stationary background activity. This could be interpreted as an effect of the aseismic slip in the Chi-Chi rupture fault, whereby the inland region around the Chi-Chi source becomes a stress shadow.

Statistical analysis of ULF seismomagnetic phenomena at Kakioka, Japan, during 2001–2010

To clarify and verify the ultralow frequency (ULF) seismomagnetic phenomena, we have performed statistical studies on the geomagnetic data observed at the Kakioka (KAK) station, Japan, during 2001–2010. We investigated the energy of ULF geomagnetic signals of the frequency around 0.01 Hz using wavelet transform analysis. To minimize the influences of artificial noises and global geomagnetic perturbations, we used only the geomagnetic data observed at nighttime (LT 2:30 A.M. to 4:00 A.M.) and utilized observations from a remote station, Kanoya, as a reference. Statistical results of superposed epoch analysis have indicated that ULF magnetic anomalies are more likely to appear before sizable earthquake events (Es > 108) rather than after them, especially 6–15 days before the events. Further statistical investigations show clearly that the ULF geomagnetic anomalies at KAK station are more sensitive to larger and closer events. Finally, we have evaluated the precursory information of ULF geomagnetic signals for local sizable earthquakes using Molchans error diagram. The probability gain is around 1.6 against a Poisson model. The above results have indicated that the ULF seismomagnetic phenomena at KAK clearly contain precursory information and have a possibility of improving the forecasting of large earthquakes.

Data completeness of the Kumamoto earthquake sequence in the JMA catalog and its influence on the estimation of the ETAS parameters

Abstract This study investigates the missing data problem in the Japan Meteorological Agency catalog of the Kumamoto aftershock sequence, which occurred since April 15, 2016, in Japan. Based on the assumption that earthquake magnitudes are independent of their occurrence times, we replenish the short-term missing data of small earthquakes by using a bi-scale transformation and study their influence on the maximum likelihood estimate (MLE) of the epidemic-type aftershock sequences (ETAS) parameters by comparing the analysis results from the original and the replenished datasets. The results show that the MLEs of the ETAS parameters vary when this model is fitted to the recorded catalog with different cutoff magnitudes, while those MLEs remain stable for the replenished dataset. Further analysis shows that the seismicity becomes quiescent after the occurrence of the second major shock, which can be regarded as a precursory phenomenon of the occurrence of the subsequent

Comparison of seismicity declustering methods using a probabilistic measure of clustering

M_J7.3

Statistical Evaluation of Efficiency and Possibility of Earthquake Predictions with Gravity Field Variation and its Analytic Signal in Western China

MJ7.3 mainshock. This relative quiescence is demonstrated more clearly by the analysis of the replenished dataset.Graphical abstract(Left 6 panels) Illustration of applying the replenishing algorithm to the short missing of aftershocks in the Kumamoto aftershock sequence. (Right 6 panels) ETAS parameters estimated from the Kumamoto aftershock sequence with different magnitude thresholds. See text for details.

Three Regimes of the Distribution of the Largest Event in the Critical ETAS Model

This study incorporates the rupture geometry of big earthquakes in the formulation of the Epidemic-Type Aftershock Sequence (ETAS) model, which is a point process model widely applied in the study of spatiotemporal seismicity, rather than regarding every earthquake occurring at a point in space and time. We apply the new model to the catalog from Sichuan province, China, between 1990 and 2013, during which the Wenchuan Mw7.9 earthquake occurred in May 2008. Our results show that the modified model has better performance in both data fitting and aftershock simulation, confirming that the elliptic aftershock zone is caused by the superposition of the isotropic triggering effect from each patch of the rupture zone. Moreover, using the technique of stochastic reconstruction, we inverted the fault geometry and verified that direct aftershocks of the main shock more likely occur in the transitive parts from high-slip parts to low/median slip parts of the main shock fault area.

Heterogeneity of direct aftershock productivity of the main shock rupture

We present a new measure of earthquake clustering and explore its use for comparing the performance of three different declustering methods. The advantage of this new clustering measure over existing techniques is that it can be used for non-Poissonian background seismicity and, in particular, to compare the results of declustering algorithms where different background models are used. We use our approach to study inter-event times between successive earthquakes using earthquake catalog data from Japan and southern California. A measure of the extent of clustering is introduced by comparing the inter-event time distributions of the background seismicity to that of the whole observed seismicity. Theoretical aspects of the clustering measure are then discussed with respect to the Poissonian and Weibull models for the background inter-event time distribution. In the case of a Poissonian background, the obtained clustering measure shows a decrease followed by an increase, defining a V-shaped trend, which can be explained by the presence of short- and long-range correlation in the inter-event time series. Three previously proposed declustering methods (i.e., the methods of Gardner and Knopoff, Reasenberg, and Zhuang et al.) are used to obtain an approximation of the residual “background” inter-event time distribution in order to apply our clustering measure to real seismicity. The clustering measure is then estimated for different values of magnitude cutoffs and time periods, taking into account the completeness of each catalog. Plots of the clustering measure are presented as clustering attenuation curves (CACs), showing how the correlation decreases when inter-event times increase. The CACs demonstrate strong clustering at short inter-event time ranges and weak clustering at long time ranges. When the algorithm of Gardner and Knopoff is used, the CACs show strong correlation with a weak background at the short inter-event time ranges. The fit of the CACs using the Poissonian background model is successful at short and intermediate inter-event time ranges, but deviates at long ranges. The observed deviation shows that the residual catalog obtained after declustering remains non-Poissonian at long time ranges. The apparent background fraction can be estimated directly from the CAC fit. The CACs using the algorithms of Reasenberg and Zhuang et al. show a relatively similar behavior, with a time correlation decreasing more rapidly than the CACs of Gardner and Knopoff for shorter time ranges. This study offers a novel approach for the study of different types of clustering produced as a result of various hypotheses used to account for different backgrounds.