Time-Frequency Analysis of Digital Broadband Station Based on Padang Lawas Earthquake

Sinambela, Marzuki; Tarigan, Kerista; Situmorang, Manihar; Humaidi, Syahrul; Rahayu, Teguh; Ainun, Ayun Ria

doi:10.1109/mecnit48290.2020.9166633

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…Other methods for extracting and analyzing the waveforms data using wavelet-based machine learning to computed the waveforms in machine learning python (Mlpy) in time-frequency [16]. Mlpy is the module for desktop learning constructed on the pinnacle of NumPy/Scipy and the GNU, and we mix the mlpy to extract the nuclear waveform and plotting the spectrum based on continuous wavelet seriously change [17].…”

Section: Methodsmentioning

confidence: 99%

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: Methodsmentioning

confidence: 99%

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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“…Large events are usually recorded with a high signal-to-noise ratio (SNR), while weak events are recorded by a small number of stations with noisy waveforms and reduced coherence [13], [14]. On the other hand, we also try to catch weaker events, which is important to reduce the exhaustiveness of the catalog.…”

Section: Introductionmentioning

confidence: 99%

A Preliminary Earthquake Detection from The Seismic Temporary Network in The Opak Fault

Simanjuntak

Saragih

Anshori

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Microseismic observation is a mandatory tool for analysing and imaging the progress of source earthquake mechanism. The observation of low-quality signal is usually found because of false detection, transient signals by natural noise, or related to seismometer condition and some human activities. Therefore, we try to figure the microseismic phenomenon after major earthquake in the Jogjakarta fault that was generated by an active fault namely Opak Fault. We used the seismic data recording from the seismic project that was installed in 2006 cover the Jogjakarta region. We used one week data to see the potential detection and highlight the possible of positive or negative false detection. With total 16 stations, we got ~500 events in only one week recording. We used a various threshold with 110 to scan all the dataset and we obtained ~450 events with 50-100 events with possible of false positives. Meanwhile, the threshold of 112 has < 50 events that could be suitable with the waveforms. The example results of 120 thresholds figure a strong event that is located by highly resolution of stack coherent from some stations with precise of P and S phase fitting. This study present the first automatic earthquake locations that can provide more detail of seismic structure information in the Jogjakarta region.

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Spatial Cluster Analysis of Land Seismicity in the Northem Part of Sumatra Using the K-Medoids Algorithm

Arrizal,

Sinambela,

Widodo

et al. 2023

2023 International Conference on Information Technology and Computing (ICITCOM)

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Time-Frequency Analysis of Digital Broadband Station Based on Padang Lawas Earthquake

Cited by 3 publications

References 10 publications

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

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

A Preliminary Earthquake Detection from The Seismic Temporary Network in The Opak Fault

Spatial Cluster Analysis of Land Seismicity in the Northem Part of Sumatra Using the K-Medoids Algorithm

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