Analysis of coherence in global seismic noise for 1997–2012

The coherent behavior of four parameters characterizing the global field of low-frequency (periods from 2 to 500 min) seismic noise is studied. These parameters include logarithmic variance, kurtosis (coefficient of excess), width of support of multifractal singularity spectrum, and minimal normalized entropy of the distribution of the squared orthogonal wavelet coefficients. The analy)sis is based on the data from 229 broadband stations of GSN, GEOSCOPE, and GEOFON networks for a 16-year period from the beginning of 1997 to the end of 2012. The entire set of stations is subdivided into eight groups, which, taken together, provide full coverage of the Earth. The daily median values of the studied noise parameters are calculated in each group. This procedure yields four 8-dimensional time series with a time step of 1 day with a length of 5844 samples in each scalar component. For each of the four 8-dimensional time series, the frequency-time diagram of the evolution of the spectral measure of coherence (based on canonical coherences) is constructed in the moving time window with a length of 365 days. Besides, for each parameter, the maximum-frequency values of the coherence measure and their mean over the four analyzed noise parameters are calculated as a measure of synchronization that depends on time only. Based on the conducted analysis, it is concluded that the increase in the intensity of the strongest (M ≥ 8.5) earthquakes after the mega-earthquake on Sumatra on December 26, 2004 was preceded by the enhancement of synchronization between the parameters of global seismic noise over the entire time interval of observations since the beginning of 1997. This synchronization continues growing up to the end of the studied period (2012), which can be interpreted as a probable precursor of the further increase in the intensity of the strongest earthquakes all over the world.     

The Relationship between Multifractal and Entropy Properties of Seismic Noise in Kamchatka and Irregularity of the Earth’s Rotation

Izvestiya, Physics of the Solid Earth - Abstract—The connection between the properties of seismic noise continuously recorded by the network of 21 broadband seismic stations in...     

Reply to the Comment by V.A. Saltykov on the Paper “The Relationship between Multifractal and Entropy Properties of Seismic Noise in Kamchatka and Irregularity of the Earth’s Rotation” by A.A. Lyubushin,G.N. Kopylova,and Yu.K. Serafimova

Izvestiya, Physics of the Solid Earth -     

Comparison of Bayesian estimates of peak ground acceleration (<Emphasis Type="Italic">A</Emphasis>max) with PSHA in Iran

Bayesian probability theory is an appropriate and useful method for estimating parameters in seismic hazard analysis. The analysis in Bayesian approaches is based on a posterior belief, also their special ability is to take into account the uncertainty of parameters in probabilistic relations and a priori knowledge. In this study, we benefited the Bayesian approach in order to estimate maximum values of peak ground acceleration (A_max) also quantiles of the relevant probabilistic distributions are figured out in a desired future interval time in Iran. The main assumptions are Poissonian character of the seismic events flow and properties of the Gutenberg-Richter distribution law. The map of maximum possible values of A_max and also map of 90% quantile of distribution of maximum values of A_max on a future interval time 100 years is presented. According to the results, the maximum value of the A_max is estimated for Bandar Abbas as 0.3g and the minimum one is attributed to Esfahan as 0.03g. Finally, the estimated values in Bayesian approach are compared with what was presented applying probabilistic seismic hazard (PSH) methods based on the conventional Cornel (1968) method. The distribution function of A_max for future time intervals of 100 and 475 years are calculated for confidence limit of probability level of 90%.     

Statistical estimation of maximum peak ground acceleration at a given point of a seismic region

A straightforward procedure is suggested for statistical estimation of maximum peak ground acceleration, A_max (T), that will occur at a given point of a seismic region in future time period T. This procedure is based on the Bayesian approach and includes estimation of three unknown parameters;b, the slope of acceleration-frequency law; , the maximum regression acceleration; , the rate of significant accelerations at the point under question. Uncertainty characteristics of A_max (T)-estimates as well as of all estimated parameters are given. The suggested approach is illustrated for two sites in Southern California.     

Microseismic impulses as earthquake precursors

Records of the IRIS broadband stations in Petropavlovsk-Kamchatski, Yuzhno-Sakhalinsk, Magadan, Yakutsk, Arti, and Obninsk obtained before the Kronotskii (Kamchatka Peninsula) M = 7.7 earthquake of December 5, 1997, and the Neftegorsk (Sakhalin Island) M = 7.0 earthquake of May 27, 1995, are investigated with the use of the Spectra Analyzer interactive program, designed for the analysis of properties of scalar time series. It is found that, 5 to 10 days before the shocks, stations nearest to the sources of these earthquakes recorded pulsed vibrations a few minutes long that were separated by intervals of a few tens of minutes. The shape asymmetry of the pulses characterized by different amplitudes of positive and negative polarity phases increased toward the earthquake onset time, as did the frequency and regularity of the pulse sequence. It is assumed that the nature of this phenomenon is related to self-organization properties of the seismic process in a metastable lithosphere and to the synchronization of vibrations in the inner and outer shells of the Earth, including chaotic and quasi-periodic components.     

Statistical methods for river runoff prediction

Methods used to analyze one type of nonstationary stochastic processes—the periodically correlated process—are considered. Two methods of one-step-forward prediction of periodically correlated time series are examined. One-step-forward predictions made in accordance with an autoregression model and a model of an artificial neural network with one latent neuron layer and with an adaptation mechanism of network parameters in a moving time window were compared in terms of efficiency. The comparison showed that, in the case of prediction for one time step for time series of mean monthly water discharge, the simpler autoregression model is more efficient.Translated from Vodnye Resursy, Vol. 32, No. 2, 2005, pp. 133–145.Original Russian Text Copyright © 2005 by Pisarenko, Lyubushin, Bolgov, Rukavishnikova, Kanyu, Kanevskii, Saveleva, Demyanov, Zalyapin.