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Waveforms Classification of Northern Sumatera Earthquakes for New Mini Region Stations Using Support Vector Machine

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@article{IJASEIT12503,
   author = {Marzuki Sinambela and Marhaposan Situmorang and Kerista Tarigan and Syahrul Humaidi and Makmur Sirait},
   title = {Waveforms Classification of Northern Sumatera Earthquakes for New Mini Region Stations Using Support Vector Machine},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {2},
   year = {2021},
   pages = {489--494},
   keywords = {Classification; SVM; wavelet-based; machine learning; new mini region station.},
   abstract = {

We develop and evaluate the new mini region station in Northern Sumatera for discrimination and feature extract seismic events form shallow and intermediate based on waveforms recorded. Machine learning approaches are employed to classification the waveforms and seismic features of the recoded signal in the time-frequency domain. The most issue of this study are the recurrence of the seismic tremors in January to April 2020 regularly happened, and exceptionally local in Northern Sumatra. This can be also in related to the establishment of modern sensors, for that it will be fundamental to create a high-performance technique for automated clustering of seismic tremors recorded of the modern smaller than expected locale sensors on a limited assortment of floor collectors based on their supply depths. We applied the technique to 25 earthquakes that started January to April 2020, with the depth are smaller than 100 km in the land. A selected set of features were then used to train the system to discriminate from events with a hypo-central depth between 10 to 100 km with 96.01 percent accuracy using the SVM model. The result shows that the spectral feature using wavelet-based with machine learning python (mlpy) package has the highest energy correlation. Wavelet spectral in the time-frequency domain is all-new mini region stations that are promising for seismic event classification. The used machine learning approaches have a good classification of low energy signals recorded at the new mini region station in Northern Sumatera.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12503},    doi = {10.18517/ijaseit.11.2.12503} }

EndNote

%A Sinambela, Marzuki
%A Situmorang, Marhaposan
%A Tarigan, Kerista
%A Humaidi, Syahrul
%A Sirait, Makmur
%D 2021
%T Waveforms Classification of Northern Sumatera Earthquakes for New Mini Region Stations Using Support Vector Machine
%B 2021
%9 Classification; SVM; wavelet-based; machine learning; new mini region station.
%! Waveforms Classification of Northern Sumatera Earthquakes for New Mini Region Stations Using Support Vector Machine
%K Classification; SVM; wavelet-based; machine learning; new mini region station.
%X 

We develop and evaluate the new mini region station in Northern Sumatera for discrimination and feature extract seismic events form shallow and intermediate based on waveforms recorded. Machine learning approaches are employed to classification the waveforms and seismic features of the recoded signal in the time-frequency domain. The most issue of this study are the recurrence of the seismic tremors in January to April 2020 regularly happened, and exceptionally local in Northern Sumatra. This can be also in related to the establishment of modern sensors, for that it will be fundamental to create a high-performance technique for automated clustering of seismic tremors recorded of the modern smaller than expected locale sensors on a limited assortment of floor collectors based on their supply depths. We applied the technique to 25 earthquakes that started January to April 2020, with the depth are smaller than 100 km in the land. A selected set of features were then used to train the system to discriminate from events with a hypo-central depth between 10 to 100 km with 96.01 percent accuracy using the SVM model. The result shows that the spectral feature using wavelet-based with machine learning python (mlpy) package has the highest energy correlation. Wavelet spectral in the time-frequency domain is all-new mini region stations that are promising for seismic event classification. The used machine learning approaches have a good classification of low energy signals recorded at the new mini region station in Northern Sumatera.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12503 %R doi:10.18517/ijaseit.11.2.12503 %J International Journal on Advanced Science, Engineering and Information Technology %V 11 %N 2 %@ 2088-5334

IEEE

Marzuki Sinambela,Marhaposan Situmorang,Kerista Tarigan,Syahrul Humaidi and Makmur Sirait,"Waveforms Classification of Northern Sumatera Earthquakes for New Mini Region Stations Using Support Vector Machine," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 2, pp. 489-494, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.2.12503.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Sinambela, Marzuki
AU  - Situmorang, Marhaposan
AU  - Tarigan, Kerista
AU  - Humaidi, Syahrul
AU  - Sirait, Makmur
PY  - 2021
TI  - Waveforms Classification of Northern Sumatera Earthquakes for New Mini Region Stations Using Support Vector Machine
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 2
Y2  - 2021
SP  - 489
EP  - 494
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Classification; SVM; wavelet-based; machine learning; new mini region station.
N2  - 

We develop and evaluate the new mini region station in Northern Sumatera for discrimination and feature extract seismic events form shallow and intermediate based on waveforms recorded. Machine learning approaches are employed to classification the waveforms and seismic features of the recoded signal in the time-frequency domain. The most issue of this study are the recurrence of the seismic tremors in January to April 2020 regularly happened, and exceptionally local in Northern Sumatra. This can be also in related to the establishment of modern sensors, for that it will be fundamental to create a high-performance technique for automated clustering of seismic tremors recorded of the modern smaller than expected locale sensors on a limited assortment of floor collectors based on their supply depths. We applied the technique to 25 earthquakes that started January to April 2020, with the depth are smaller than 100 km in the land. A selected set of features were then used to train the system to discriminate from events with a hypo-central depth between 10 to 100 km with 96.01 percent accuracy using the SVM model. The result shows that the spectral feature using wavelet-based with machine learning python (mlpy) package has the highest energy correlation. Wavelet spectral in the time-frequency domain is all-new mini region stations that are promising for seismic event classification. The used machine learning approaches have a good classification of low energy signals recorded at the new mini region station in Northern Sumatera.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12503 DO - 10.18517/ijaseit.11.2.12503

RefWorks

RT Journal Article
ID 12503
A1 Sinambela, Marzuki
A1 Situmorang, Marhaposan
A1 Tarigan, Kerista
A1 Humaidi, Syahrul
A1 Sirait, Makmur
T1 Waveforms Classification of Northern Sumatera Earthquakes for New Mini Region Stations Using Support Vector Machine
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 2
YR 2021
SP 489
OP 494
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Classification; SVM; wavelet-based; machine learning; new mini region station.
AB 

We develop and evaluate the new mini region station in Northern Sumatera for discrimination and feature extract seismic events form shallow and intermediate based on waveforms recorded. Machine learning approaches are employed to classification the waveforms and seismic features of the recoded signal in the time-frequency domain. The most issue of this study are the recurrence of the seismic tremors in January to April 2020 regularly happened, and exceptionally local in Northern Sumatra. This can be also in related to the establishment of modern sensors, for that it will be fundamental to create a high-performance technique for automated clustering of seismic tremors recorded of the modern smaller than expected locale sensors on a limited assortment of floor collectors based on their supply depths. We applied the technique to 25 earthquakes that started January to April 2020, with the depth are smaller than 100 km in the land. A selected set of features were then used to train the system to discriminate from events with a hypo-central depth between 10 to 100 km with 96.01 percent accuracy using the SVM model. The result shows that the spectral feature using wavelet-based with machine learning python (mlpy) package has the highest energy correlation. Wavelet spectral in the time-frequency domain is all-new mini region stations that are promising for seismic event classification. The used machine learning approaches have a good classification of low energy signals recorded at the new mini region station in Northern Sumatera.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12503 DO - 10.18517/ijaseit.11.2.12503