The search functionality is under construction.

The search functionality is under construction.

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.

- Publication
- IEICE TRANSACTIONS on Information Vol.E83-D No.12 pp.2098-2106

- Publication Date
- 2000/12/25

- Publicized

- Online ISSN

- DOI

- Type of Manuscript
- PAPER

- Category
- Pattern Recognition

The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.

Copy

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

Copy

@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},}

Copy

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 -