Classification of Seismic Signal by Evaluating Broadband Networks Station in Sumatera Fore-Arc

Sinambela, Marzuki; Tarigan, Kerista; Humaidi, Syahrul; Situmorang, Manihar

doi:10.1088/1742-6596/1485/1/012054

Cited by 2 publications

(1 citation statement)

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“…Wavelet-Based computer mastering is an accurate, efficient, and efficacious method to enhance the seismic signal's nice. Classification of the signal from waveforms data depends on considerable lookup in digital sign processing and seismology [6]- [10]. The main problem of this study, the frequency of the earthquakes from January to April 2020 often occurred and very local in Northern Sumatra.…”

Section: Introductionmentioning

confidence: 96%

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

Sinambela¹,

Situmorang²,

Tarigan³

et al. 2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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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.

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Section: Introductionmentioning

confidence: 96%