The quality factor of coda (Q c ) waves has been estimated by using single backscattering and single isotropic scattering models. The earthquakes used were recorded by three permanent and one temporary network located in the central and eastern Alborz, Iran. The database was composed of 746 local earthquakes with local magnitude from 1.1 to 5.7. The estimated Q c has been found to be similar for lapse times greater than twice the S-wave travel time (2t S ) for both methods.The estimated Q c for central frequencies < 1:0 Hz shows less frequency dependency compared with the higher frequencies. By using a Q 0 f n relation, the average frequency dependence of Q c for the whole area has been estimated as 59f 1:03 , 69f 0:97 , 78f 0:97 , 105f 0:93 , 123f 0:89 , 159f 0:79 , and 203f 0:68 for central lapse times 30, 40, 50, 65, 85, 110, and 155 s, respectively. We found the value of Q 0 decreases at an average depth of 78 km, which may be the result of high dissipating media at this depth. The average Q c values, estimated for central and eastern Alborz and their frequencydependent relationships are similar to those of tectonically active regions.
Summary. We propose a novel method for analyzing precursory seismic data before an earthquake that treats them as a Markov process and distinguishes the background noise from real fluctuations due to an earthquake. A short time (on the order of several hours) before an earthquake the Markov time scale tM increases sharply, hence providing an alarm for an impending earthquake. To distinguish a false alarm from a reliable one, we compute a second quantity, T1, based on the concept of extended self-similarity of the data. T1 also changes strongly before an earthquake occurs. An alarm is accepted if both tM and T1 indicate it simultaneously.
M. Reza Rahimi Tabar , et alCalibrating the method with the data for one region provides a tool for predicting an impending earthquake within that region. Our analysis of the data for a large number of earthquakes indicate an essentially zero rate of failure for the method.
A Quadratic Neural Networks (QNNs) model has been developed for identifying seismic source classification problem at regional distances using ARMA coefficients determination by Artificial Neural Networks (ANNs). We have devised a supervised neural system to discriminate between earthquakes and chemical explosions with filter coefficients obtained by windowed P-wave phase spectra (15 s). First, we preprocess the recording's signals to cancel out instrumental and attenuation site effects and obtain a compact representation of seismic records. Second, we use a QNNs system to obtain ARMA coefficients for feature extraction in the discrimination problem. The derived coefficients are then applied to the neural system to train and classification. In this study, we explore the possibility of using single station three-component (3C) covariance matrix traces from a priori-known explosion sites (learning) for automatically recognizing subsequent explosions from the same site. The results have shown that this feature extraction gives the best classifier for seismic signals and performs significantly better than other classification methods. The events have been tested, which include 36 chemical explosions at the Semipalatinsk test site in Kazakhstan and 61 earthquakes (mb = 5.0-6.5) recorded by the Iranian National Seismic Network (INSN). The 100% correct decisions were obtained between site explosions and some of non-site events. The above approach to event discrimination is very flexible as we can combine several 3C stations.
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