The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Neste artigo apresentamos um novo método para análise de sinais sísmicos, baseado no ARMA modelagem e um fuzzy QLV método de agrupamento. O objetivo alcançado neste trabalho é detectar as alterações ocorridas natural ou artificialmente no sinal do sismograma, e detectar as fontes que causaram essas alterações (classificação sísmica). Durante o estudo, descobrimos também que o modelo é por vezes capaz de alarmar futuros eventos sísmicos pouco tempo antes do início desses eventos (previsão sísmica). Assim, a aplicação do método proposto tanto em classificação sísmica e previsão sísmica são estudados através dos resultados experimentais. O estudo é baseado no ruído de fundo das gravações telessísmicas de curto período. O ARMA os coeficientes do modelo são derivados para as janelas sobrepostas consecutivas. A modelo básico é então gerado agrupando os parâmetros do modelo calculado, usando o método fuzzy LVQ proposto por Nassery & Faez em [19]. As janelas de tempo, que não participam do processo de geração do modelo [19], são denominadas como janelas de teste. Os coeficientes do modelo janelas de teste são então comparados com os coeficientes do modelo base através de algumas regras de composição predefinidas. O resultado dessa comparação é um valor normalizado gerado como medida de similaridade. O conjunto de medidas de similaridade consecutivas geradas acima produz uma curva versus os índices de janelas de tempo denominados como curvas características. Os resultados numéricos mostraram que as curvas características muitas vezes contêm muita informação sismológica vital e podem ser usadas para fins de classificação e previsão de fontes.
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Payam NASSERY, Karim FAEZ, "A Dynamic Model for the Seismic Signals Processing and Application in Seismic Prediction and Discrimination" in IEICE TRANSACTIONS on Information,
vol. E83-D, no. 12, pp. 2098-2106, December 2000, doi: .
Abstract: In this paper we have presented a new method for seismic signal analysis, based on the ARMA modeling and a fuzzy LVQ clustering method. The objective achieved in this work is to sense the changes made naturally or artificially on the seismogram signal, and to detect the sources, which caused these changes (seismic classification). During the study, we have also found out that the model is sometimes capable to alarm the further seismic events just a little time before the onset of those events (seismic prediction). So the application of the proposed method both in seismic classification and seismic prediction are studied through the experimental results. The study is based on the background noise of the teleseismic short period recordings. The ARMA model coefficients are derived for the consecutive overlapped windows. A base model is then generated by clustering the calculated model parameters, using the fuzzy LVQ method proposed by Nassery & Faez in [19]. The time windows, which do not take part in [19] model generation process, are named as the test windows. The model coefficients of the test windows are then compared to the base model coefficients through some pre-defined composition rules. The result of this comparison is a normalized value generated as a measure of similarity. The set of the consecutive similarity measures generate above, produce a curve versus the time windows indices called as the characteristic curves. The numerical results have shown that the characteristic curves often contain much vital seismological information and can be used for source classification and prediction purposes.
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_12_2098/_p
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@ARTICLE{e83-d_12_2098,
author={Payam NASSERY, Karim FAEZ, },
journal={IEICE TRANSACTIONS on Information},
title={A Dynamic Model for the Seismic Signals Processing and Application in Seismic Prediction and Discrimination},
year={2000},
volume={E83-D},
number={12},
pages={2098-2106},
abstract={In this paper we have presented a new method for seismic signal analysis, based on the ARMA modeling and a fuzzy LVQ clustering method. The objective achieved in this work is to sense the changes made naturally or artificially on the seismogram signal, and to detect the sources, which caused these changes (seismic classification). During the study, we have also found out that the model is sometimes capable to alarm the further seismic events just a little time before the onset of those events (seismic prediction). So the application of the proposed method both in seismic classification and seismic prediction are studied through the experimental results. The study is based on the background noise of the teleseismic short period recordings. The ARMA model coefficients are derived for the consecutive overlapped windows. A base model is then generated by clustering the calculated model parameters, using the fuzzy LVQ method proposed by Nassery & Faez in [19]. The time windows, which do not take part in [19] model generation process, are named as the test windows. The model coefficients of the test windows are then compared to the base model coefficients through some pre-defined composition rules. The result of this comparison is a normalized value generated as a measure of similarity. The set of the consecutive similarity measures generate above, produce a curve versus the time windows indices called as the characteristic curves. The numerical results have shown that the characteristic curves often contain much vital seismological information and can be used for source classification and prediction purposes.},
keywords={},
doi={},
ISSN={},
month={December},}
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TY - JOUR
TI - A Dynamic Model for the Seismic Signals Processing and Application in Seismic Prediction and Discrimination
T2 - IEICE TRANSACTIONS on Information
SP - 2098
EP - 2106
AU - Payam NASSERY
AU - Karim FAEZ
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2000
AB - In this paper we have presented a new method for seismic signal analysis, based on the ARMA modeling and a fuzzy LVQ clustering method. The objective achieved in this work is to sense the changes made naturally or artificially on the seismogram signal, and to detect the sources, which caused these changes (seismic classification). During the study, we have also found out that the model is sometimes capable to alarm the further seismic events just a little time before the onset of those events (seismic prediction). So the application of the proposed method both in seismic classification and seismic prediction are studied through the experimental results. The study is based on the background noise of the teleseismic short period recordings. The ARMA model coefficients are derived for the consecutive overlapped windows. A base model is then generated by clustering the calculated model parameters, using the fuzzy LVQ method proposed by Nassery & Faez in [19]. The time windows, which do not take part in [19] model generation process, are named as the test windows. The model coefficients of the test windows are then compared to the base model coefficients through some pre-defined composition rules. The result of this comparison is a normalized value generated as a measure of similarity. The set of the consecutive similarity measures generate above, produce a curve versus the time windows indices called as the characteristic curves. The numerical results have shown that the characteristic curves often contain much vital seismological information and can be used for source classification and prediction purposes.
ER -