A Bayesian Assessment of Seismic Semi-Periodicity Forecasts

Nava, F. Alejandro; Quinteros, C. B.; Glowacka, Ewa; Frez, J.

doi:10.1007/s00024-015-1056-3

Cited by 7 publications

(12 citation statements)

References 8 publications

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“…The Fourier spectral analysis method used to identify semi-periodic sequences of earthquakes, described and applied by Nava et al (2014) and , is now adapted to the case of labeled point processes, where the labels indicate the size of each earthquake, through its magnitude. These labels are used to assign to each datum a weight wðMÞ so that a function representing the labeled time series recognized as a section of an infinite time series, is built as…”

Section: Labeled Time Series Analysis and The Magnitude Weighting Funmentioning

confidence: 99%

“…Considering as event A the presence of semiperiodicity, evidenced by the identification of a semiperiodic sequence with non-randomness probability Pr ðAÞ ¼ P c , after the occurrence of a later earthquake, event B, Bayes formalism can be used, as proposed in Nava et al (2016), to test whether B is Table 4 Probabilities and probability and information gains for the Japan sequence 7626 1900 1910 1920 1930 1940 1950 1960 1970 1900 1910 1920 1930 1940 1950 1960 1970 t (yr) 1900 1910 1920 1930 1940 1950 1960 1970 t (yr)…”

Section: Bayesian Analysis Of the Aftcastmentioning

confidence: 99%

“…If the interval is sufficiently small, the results of (10) do not depend on its actual size (Nava et al 2016).…”

Section: Bayesian Analysis Of the Aftcastmentioning

confidence: 99%

“…These collapses occur repeatedly, but not in a perfectly periodical way nor having always the same size, since it is a non-linear process and there are physical and geological complexities of the system that cause variations in its behavior. Nava et al (2014) and considered that the occurrence of great earthquakes should have semi-periodic behavior associated to a seismogenic process, resulting in sequences with recurrence period, s, that vary slightly. Hence, an earthquake sequence is semi-periodic if the occurrence times satisfy…”

Section: Introductionmentioning

confidence: 99%

“…The estimation of the standard deviation to be used in forecasts is revised. Finally, we apply the Bayesian formalism described in Nava et al (2016) to evaluate the performance of the forecast method when applied to data series amenable to aftcasting (forecasting of events that have already occurred, based on data previous to them).…”

Section: Introductionmentioning

confidence: 99%

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Forecast of Large Earthquakes Through Semi-periodicity Analysis of Labeled Point Processes

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et al. 2016

Pure Appl. Geophys.

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Large earthquakes have semi-periodic behavior as a result of critically self-organized processes of stress accumulation and release in seismogenic regions. Hence, large earthquakes in a given region constitute semi-periodic sequences with recurrence times varying slightly from periodicity. In previous papers, it has been shown that it is possible to identify these sequences through Fourier analysis of the occurrence time series of large earthquakes from a given region, by realizing that not all earthquakes in the region need belong to the same sequence, since there can be more than one process of stress accumulation and release in the region. Sequence identification can be used to forecast earthquake occurrence with well determined confidence bounds. This paper presents improvements on the above mentioned sequence identification and forecasting method: the influence of earthquake size on the spectral analysis, and its importance in semi-periodic events identification are considered, which means that earthquake occurrence times are treated as a labeled point process; a revised estimation of nonrandomness probability is used; a better estimation of appropriate upper limit uncertainties to use in forecasts is introduced; and the use of Bayesian analysis to evaluate the posterior forecast performance is applied. This improved method was successfully tested on synthetic data and subsequently applied to real data from some specific regions. As an example of application, we show the analysis of data from the northeastern Japan Arc region, in which one semi-periodic sequence of four earthquakes with M C 8.0, having high non-randomness probability was identified. We compare the results of this analysis with those of the unlabeled point process analysis.

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Section: Labeled Time Series Analysis and The Magnitude Weighting Funmentioning

confidence: 99%

Section: Bayesian Analysis Of the Aftcastmentioning

confidence: 99%

“…If the interval is sufficiently small, the results of (10) do not depend on its actual size (Nava et al 2016).…”

Section: Bayesian Analysis Of the Aftcastmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%