Estimation of completeness magnitude considering daily variation in earthquake detection capability

Iwata, T.

doi:10.1093/gji/ggt208

Cited by 26 publications

(16 citation statements)

References 28 publications

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“…2. We can see that the biggest missing events are no less than magnitude 3.0 immediately after the first and the third major shocks, much higher than the completeness level of the usual detection ability of the network in this area, which goes down to about 0.5 for shallow events (up to 30 km deep) and about 1.0 for slightly deeper events (30-60 km) (Nanjo et al 2010;Iwata 2013).…”

Section: Datamentioning

confidence: 73%

“…and Iwata 2008Iwata , 2013Iwata , 2014 and developed methods of making probabilistic earthquake forecasting with missing earthquakes taken into account (e.g., Ogata 2006;Omi et al 2013Omi et al , 2014Omi et al , 2015. A non-Bayesian procedure that corrects such temporally varying incomplete detection of earthquakes can be found in Marsan and Enescu (2012), where they assumed that the b-value is constant and that the occurrence rate of earthquakes follows the Omori-Utsu formula or the ETAS model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Zhuang

Ogata

Wang

2017

Earth Planets Space

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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 M J 7.3 mainshock. This relative quiescence is demonstrated more clearly by the analysis of the replenished dataset.

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Section: Datamentioning

confidence: 73%

Section: Introductionmentioning

confidence: 99%

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

Zhuang

Ogata

Wang

2017

Earth Planets Space

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“…In this case, μ corresponds to the magnitude at which the detection probability is equal to 50%. Examples of how well this density function fits an observed magnitude distribution are shown in Ogata & Katsura (, ) and Iwata (, , , b).…”

Section: Methodsmentioning

confidence: 95%

“…Of the three parameters ( μ , β , and σ ) contained in this probability density function, μ plays the most important role in the quantitative evaluation of the detection capability. This is because μ is a location parameter of the probability density function f ( M | μ , β , σ ) (see also figure 1 of Iwata ); thus the value of μ dominantly affects the quality of the detection capability. A higher value of μ corresponds to lower detection capability and vice versa.…”

Section: Methodsmentioning

confidence: 99%

Decomposition of Seasonality and Long‐term Trend in Seismological Data: A Bayesian Modelling of Earthquake Detection Capability

Iwata

2014

Aus NZ J of Statistics

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This study demonstrates the decomposition of seasonality and long-term trend in seismological data observed at irregular time intervals. The decomposition was applied to the estimation of earthquake detection capability using cubic B-splines and a Bayesian approach, which is similar to the seasonal adjustment model frequently used to analyse economic time-series data. We employed numerical simulation to verify the method and then applied it to real earthquake datasets obtained in and around the northern Honshu island, Japan. With this approach, we obtained the seasonality of the detection capability related to the annual variation of wind speed and the long-term trend corresponding to the recent improvement of the seismic network in the studied region.

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“…This method maximises the statistical measure called the 'marginal likelihood' and is known in geophysics as Akaike's Bayesian Information Criterion (ABIC) (Akaike, 1980). Since Akaike (1980), inversion methods based on empirical Bayes have been applied to many geophysical problems (Ogata et al, 1991;Koketsu and Higashi, 1992;Sekiguchi et al, 2000;Cho et al, 2006;Fukahata and Wright, 2008;Iwata, 2013Iwata, , 2014 and this approach therefore appears to be well established (Matsu'ura et al, 2007;Fukahata, 2009).…”

Section: Introductionmentioning

confidence: 99%

Development and numerical tests of a Bayesian approach to inferring shallow velocity structures using microtremor arrays

Cho

Iwata

2018

Exploration Geophysics

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Abstract. We propose an empirical Bayesian approach to inferring shallow (depth ranges from a few to several tens of metres) S-wave velocity structures using microtremor arrays and execute numerical tests to assess the feasibility of this approach. In our approach, the estimate of the S-wave structure (posterior) is derived from an empirical S-wave structure model (prior) and phase velocities of Rayleigh waves obtained with microtremor arrays. In other words, we aim to find a model that is close to the empirical model and is able to explain phase velocities with a 1D surface-wave theory. The inversion is stabilised by the constraints from the prior model so that model parameterisation with many thin layers can be adopted. The velocity structure is individually estimated for each of two cases (assumptions): the case where we assume fundamental-mode dominance and the case where we take into account the higher modes. Optimal values of the model parameters (e.g. a thickness parameter) are found, based on Akaike's Bayesian Information Criterion (ABIC), and the choice of the better assumption of the surface-wave theory is also based on ABIC. Numerical tests, where synthetic data is generated from a horizontally stratified two-layer model, indicate that the relative weight between a prior model and the observed data is appropriately adjusted by ABIC. It is revealed that a value of the thickness parameter required to reproduce the given two-layer model is successfully found by ABIC. We also suggest that we can make a plausible choice of the assumption of the surface-wave theory with ABIC, unless observation error is extremely large.

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Estimation of completeness magnitude considering daily variation in earthquake detection capability

Cited by 26 publications

References 28 publications

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

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

Decomposition of Seasonality and Long‐term Trend in Seismological Data: A Bayesian Modelling of Earthquake Detection Capability

Development and numerical tests of a Bayesian approach to inferring shallow velocity structures using microtremor arrays

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