{"id":5626,"date":"2012-03-11T07:38:01","date_gmt":"2012-03-11T07:38:01","guid":{"rendered":"http:\/\/www.iiees.ac.ir\/fa\/?p=5626"},"modified":"2012-03-11T07:38:01","modified_gmt":"2012-03-11T07:38:01","slug":"2012-03-11-07-46-11","status":"publish","type":"post","link":"http:\/\/old.iiees.ac.ir\/fa\/2012-03-11-07-46-11\/","title":{"rendered":"\u062c\u062f\u0627\u06cc\u0634 \u0627\u0646\u0641\u062c\u0627\u0631 \u0627\u0632 \u0632\u0644\u0632\u0644\u0647 \u062f\u0631 \u06a9\u0627\u062a\u0627\u0644\u0648\u06af \u0634\u0628\u06a9\u0647 \u0644\u0631\u0632\u0647 \u0646\u06af\u0627\u0631\u06cc \u0628\u0627\u0646\u062f \u067e\u0647\u0646 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0641\u06cc\u0644\u062a\u0631\u0622\u0631\u0645\u0627"},"content":{"rendered":"
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<\/p>\n

\u0647\u0645\u06a9\u0627\u0631:
\u0633\u0627\u06cc\u0647 \u0645\u06cc\u0631\u0632\u0627\u06cc\u06cc<\/p>\n

<\/p>\n

<\/div>\n

\u0686\u06a9\u06cc\u062f\u0647<\/strong><\/p>\n

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\u0648\u0627\u0698\u0647<\/strong>\u200c\u0647\u0627\u06cc \u06a9\u0644\u06cc\u062f\u06cc<\/strong>
\u062a\u0634\u062e\u06cc\u0635\u060c \u0627\u0646\u0641\u062c\u0627\u0631\u060c \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc\u060c \u0636\u0631\u0627\u06cc\u0628\u060c \u0641\u06cc\u0644\u062a\u0631 ARMA<\/p>\n<\/p>\n

\u200c<\/p>\n

Discrimination Between Explosion And Earthquake In Broad Band Seismic Network Catalogue Using ARMA Filter<\/strong><\/div>\n

Authors: M.Allamehzadeh, S. Mirzaei<\/p>\n<\/p>\n

Abstract<\/strong><\/p>\n

In this paper, the problem of seismic source classification is considered at regional distances. The neural network classifier is fed by Nonlinear AutoRegressive Moving Average (NARMA) coefficients extracted from raw data as feature set. We have devised a supervised neural system to discriminate between earthquakes and chemical explosions applying filter coefficients obtained from the windowed P-wave phase (15 sec). First, the recorded signals are preprocessed to cancel out instrumental and attenuation site effects and obtain a compact representation of seismic records. Then we use a Quadratic Neural Network (QNN) system to extract nonlinear ARMA coefficients for feature extraction in the discrimination problem. These coefficients are then applied to a probabilistic network for the purpose of training and classification. The results have shown that the overall combination of this feature extraction and classifier structure leads to a suitable seismic discrimination performance. The events tested include 36 chemical explosions at the Semipalatinsk test site in Kazakhstan and at test site in China and as well as 61 earthquakes (mb=5.0-6.5) recorded by the Iranian National Seismic Network (INSN). <\/p>\n

Keywords:<\/strong><\/p>\n

Discrimination, Earthquakes, Explosions, Neural Network, ARMA model coefficients.<\/p>\n","protected":false},"excerpt":{"rendered":"

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