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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 지진 신호 분석을 위한 새로운 방법을 제시했습니다. ARMA 모델링과 퍼지 LVQ 클러스터링 방법. 이 연구에서 달성된 목표는 지진 기록 신호에서 자연적으로 또는 인위적으로 발생한 변화를 감지하고 이러한 변화를 일으킨 원인을 탐지하는 것입니다(지진 분류). 연구 중에 우리는 모델이 때때로 추가 지진 사건이 발생하기 바로 직전에 추가 지진 사건을 경보할 수 있다는 사실도 발견했습니다(지진 예측). 그래서 제안한 방법을 두 곳 모두에 적용하면 지진 분류 and 지진 예측 실험결과를 통해 연구된다. 이 연구는 텔레지진 단기간 녹음의 배경 소음을 기반으로 합니다. 그만큼 ARMA 연속적으로 겹쳐진 창에 대해 모델 계수가 파생됩니다. ㅏ 기본 모델 그런 다음 [19]에서 Nassery & Faez가 제안한 퍼지 LVQ 방법을 사용하여 계산된 모델 매개변수를 클러스터링하여 생성됩니다. [19] 모델 생성 과정에 참여하지 않는 시간 창은 다음과 같이 명명됩니다. 테스트 창. 모델 계수는 테스트 창 그런 다음 미리 정의된 일부 구성 규칙을 통해 기본 모델 계수와 비교됩니다. 이 비교의 결과는 유사성의 척도로 생성된 정규화된 값입니다. 위에서 생성된 연속 유사성 측정 세트는 다음과 같이 호출되는 시간 창 인덱스에 대한 곡선을 생성합니다. 특성 곡선. 수치 결과에 따르면 특성 곡선에는 종종 중요한 지진 정보가 많이 포함되어 있으며 소스 분류 및 예측 목적으로 사용될 수 있습니다.
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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},}
부
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 -