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

Abstract

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.

The ISC-GEM Earthquake Catalogue (1904–2014): status after the Extension Project

“…Since then the ISC-GEM Catalogue has been used by many researchers investigating seismicity rates, patterns of seismicity and earthquake forecast (e.g. Cambiotti et al, 2016;Geist, 2014;Ikuta et al, 2015;Kagan, 2017;Kagan and Jackson, 2016;Katsumata, 2015;Pollitz et al, 2014;Quinteros Cartaya et al, 2016;Roth et al, 2017;Zaliapin and Kreemer, 2017;Zechar et al, 2016;Zhan and Shearer, 2015) as well as by groups working on earthquake catalogues for seismic hazard purposes (e.g. Alvarez et al, 2016;Deif et al, 2017;Ghasemi et al, 2016;Kadirioglu et al, 2016;Markušić et al, 2015;Mikhailova et al, 2015;Poggi et al, 2017;Weatherill et al, 2016) and other seismological studies (e.g.…”

The versatility of spectrum analysis for forecasting financial time series

“…Fourier analysis may be used for time series with seasonality or repetitive patterns. Examples involve the forecast of temperature movements (Zong-chang, 2013), of business cycle (Thomson & Van Vuuren, 2016), of demand in the fashion industry (Fumi, Pepe, Scarabotti, & Schiraldi, 2013), of large earthquakes (Quinteros Cartaya, Nava Pichardo, Glowacka, Gómez Treviño, & Dmowska, 2016) or of droughts (Moreira, Martins, & Pereira, 2015). Its application in finance involved Fourier transform methods in the evaluation of derivative contracts (Cherubini, Della Lunga, Mulinacci, & Rossi, 2009).…”