Discrimination Analysis of Earthquakes and Man-Made Events Using ARMA Coefficients Determination by Artificial Neural NetworksReport as inadecuate




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Natural Resources Research

, Volume 20, Issue 4, pp 367–375

First Online: 20 September 2011Received: 30 May 2009Accepted: 31 August 2011

Abstract

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.

KeywordsClassification earthquakes explosions ARMA coefficients neural networks  Download fulltext PDF



Author: Mostafa AllamehZadeh

Source: https://link.springer.com/







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