Seismicity Models of Moderate Earthquakes in Kanto, Japan Utilizing Multiple Predictive Parameters

Imoto, Masajiro; Rhoades, David A.

doi:10.1007/s00024-010-0066-4

Cited by 9 publications

(8 citation statements)

References 14 publications

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“…In the following discussion of the three tests, we will introduce methods to estimate distributions of the test scores for a catalog with uncertain parameters. Using methods already presented in previous papers (Imoto, 2009;Imoto and Rhoades, 2010), these tests can be conducted without using simulated catalogs.…”

Section: N- L- and R-testsmentioning

confidence: 99%

“…Here it is assumed that earthquake sequences conform to the first model (Imoto, 2009;Imoto and Rhoades, 2010). The variance is given by…”

Section: R-testmentioning

confidence: 99%

“…where the superscriptˆ2 denotes the second power in Section 2.2 and 2.3 (Imoto, 2009;Imoto and Rhoades, 2010). When an earthquake in one cell is independent from that in another cell, the mean E[l( 1 )] and variance Var[l(…”

Section: L-test 221 L-score Expected From a Proposed Modelmentioning

confidence: 99%

“…This method is based on the assumptions that the rate in each cell is far less than unity and that at most one earthquake occurs in one cell. For each test, we analytically derive two sets of the mean and variance of the respective score: that expected from a proposed model (Imoto, 2009;Imoto and Rhoades, 2010) and that of the probable score if uncertainties in hypocenter parameters are taken into account. The central limit theorem allows us to regard the distribution of the scores as approximately normal, with the analytically obtained means and variances, if the number of earthquakes in the test area is large enough.…”

Section: Introductionmentioning

confidence: 99%

“…The first model (Hazmap model) is tentatively introduced as a candidate for testing and is a subset of a seismicity model used in estimating the recent seismic hazard maps for Japan. The second one is the EEPAS model Evison, 2004, 2005), which was developed into a horizontal multi-layered model for seismicity in the Kanto region, central Japan (Rhoades and Evison, 2006;Imoto and Rhoades, 2010). The Hazmap model is configured to the tectonic setting in Kanto, where three plates (the Eurasian (North American, or Okhotsk) Plate, the Philippine Sea Plate, and the Pacific Plate) converge.…”

Section: Introductionmentioning

confidence: 99%

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Conventional N-, L-, and R-tests of earthquake forecasting models without simulated catalogs

et al. 2011

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We propose a new procedure for testing the expected number (N-test), log likelihood (L-test), and log likelihood-ratio (R-test) of seismicity models. In these tests, scores obtained from observed earthquakes are compared with distributions of scores estimated from earthquakes expected from the models under test. We introduce a method to estimate the test score distributions analytically where uncertainties in magnitude and hypocentral parameters are involved. The analytical formulas used to estimate expected values and standard deviations of the test scores for earthquakes conforming to the test models were derived in earlier published studies, which allowed calculation of normal approximations by which to test score distributions. Using these two methods simultaneously, we can perform N-, L-, and R-tests for seismicity models without using any simulated catalogs. As a case study, the proposed procedure was applied to two seismicity models for Kanto, central Japan. To compare our procedure with the current one based on the Monte Carlo method, we randomly generated sets of 10,000 earthquake catalogs of two kinds: one set conforming to the model under test, and the other derived from the observed catalog allowing for uncertainties in magnitude and hypocentral parameters. The distributions of L-scores obtained from both sets are in good agreement with those obtained by the proposed procedure. This comparison suggests that the analytical approach presented here could be useful for conducting the N-, L-, and R-tests in a conventional way.

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Section: N- L- and R-testsmentioning

confidence: 99%

“…Here it is assumed that earthquake sequences conform to the first model (Imoto, 2009;Imoto and Rhoades, 2010). The variance is given by…”

Section: R-testmentioning

confidence: 99%

Section: L-test 221 L-score Expected From a Proposed Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Conventional N-, L-, and R-tests of earthquake forecasting models without simulated catalogs

et al. 2011

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Time-varying probabilities of earthquake occurrence in central New Zealand based on the EEPAS model compensated for time-lag

Rhoades

Christophersen

2019

Geophysical Journal International

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SUMMARY The ‘Every Earthquake a Precursor According to Scale’ (EEPAS) model treats every earthquake as a precursor of larger earthquakes to follow it within a time-span ranging from months to decades, depending on magnitude. Each earthquake contributes a transient increment to the expected rate of earthquake occurrence in its vicinity, based on empirical predictive scaling relations associated with the precursory scale increase phenomenon. Incomplete information on precursory earthquakes causes the EEPAS model to underpredict the expected number of earthquakes, in particular when forecasting across a time-lag for periods beginning several years ahead. We demonstrate how the model can be modified to compensate for such time-lags when calculating future forecasts. Given the model parameters, the completeness of precursory information can be expressed as a function of the target earthquake magnitude and the time-lag. We consider two end-members for compensating the model for incompleteness. In one end-member, only the background smoothed-seismicity component of the EEPAS model is compensated, in the other, only the time-varying component of the EEPAS model is compensated. We estimate an optimal mixture of these two end-members for time-lags out to 12 yr, using several different versions of the EEPAS model and subsets of the New Zealand earthquake catalogue to which the models were previously fitted. Performance is checked on an independent test period. The optimal compensated model has increasingly high information gains over the original EEPAS model with increasing time-lags. Using catalogue data complete to 2018, the compensated models forecast increased annual probabilities of earthquake occurrence above magnitude thresholds from 6.0 to 8.0 in central New Zealand in the period 2019–2030 relative to the preceding period 2008–2018.

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An Earthquake Likelihood Model Based on Proximity to Mapped Faults and Cataloged Earthquakes

Rhoades¹,

Stirling²

2012

Bulletin of the Seismological Society of America

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Known earthquake sources include mapped active faults and locations where historical earthquakes have occurred. We propose a long-term earthquake likelihood model that makes use of both types of sources but does not invoke the concepts of fault segmentation or characteristic earthquakes. The model has two components, one based on proximity to the locations of past earthquakes, taking into account their magnitudes, and another based on proximity to mapped faults, taking into account their slip rates. The Gutenberg-Richter law is invoked for earthquake magnitudes and an inverse power law for the diminution of earthquake rate density with distance from past earthquakes and mapped faults. The model is applied to the New Zealand region using the National Seismic Hazard Model fault-source model and the earthquake catalog since 1951. The parameters of each component are optimized for the period 1987-1996, and the rate density of the combined model is estimated as an optimal linear combination of the two components. In a test on the independent period from 1997-2006, the combined model has an information gain (log-likelihood increase) per earthquake of about 0.1 over each of the component models. The model is also optimized over the longer period 1987-2006 for comparison with other longterm models in the New Zealand Earthquake Forecast Testing Centre.

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Seismicity Models of Moderate Earthquakes in Kanto, Japan Utilizing Multiple Predictive Parameters

Cited by 9 publications

References 14 publications

Conventional N-, L-, and R-tests of earthquake forecasting models without simulated catalogs

Conventional N-, L-, and R-tests of earthquake forecasting models without simulated catalogs

Time-varying probabilities of earthquake occurrence in central New Zealand based on the EEPAS model compensated for time-lag

An Earthquake Likelihood Model Based on Proximity to Mapped Faults and Cataloged Earthquakes

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