The Characteristics Influence of The Seismic Signal Noise Using Spectral Analysis

Tarigan, Kerista; Sinambela, Marzuki; Simanullang, Albertus Tua; Sunandar, Hery; Sinaga, Sony Bahagia

doi:10.1088/1742-6596/1116/3/032041

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…This is file is furnished with shear wave splitting commentary using facts taken from some seismic broadband station of the BMKG community in Indonesia (Figure 3). The statement is constant with the preceding studies [7]- [9]. Yet, it gives a denser density of earthquake monitoring station which deployed in Sumatra For-arc.…”

Section: Introductionsupporting

confidence: 62%

Shear Wave Splitting Analysis Beneath Sumatra For-Arc Inferred from Broadband Seismic Network Station

Ristiyono

Nurhafizah

Purba

et al. 2021

JoCPES

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The observation of broadband network seismic had been deployed in Sumatra For-Arc. The waveform data for this study were recorded from January 2014 – December 2016. The earthquake event data were selected with the epicenter of around 950 – 1800 in distance and Magnitude with more than 7 Mw. In this case, we use shear wave splitting to determine an anisotropic structure in Sumatra For-arc. Seismic Anisotropy can perform as a tool to classify and observe anisotropic structures of subsurface deformation processes beneath Sumatra For-Arc. The valid outcomes, in this case, have been gained that they only correspond to the upper layer, which has the delay time duration of 0.5 – 0.8 s is the anisotropic layer located in the Mentawai Island. The fast an anisotropic polarization direction found in Sumatra For-arc are parted into NE-SW direction found on the upper layer.

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Section: Introductionsupporting

confidence: 62%

Shear Wave Splitting Analysis Beneath Sumatra For-Arc Inferred from Broadband Seismic Network Station

Ristiyono

Nurhafizah

Purba

et al. 2021

JoCPES

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“…The figure [2][3][4] shows the result of probability power spectral density, which related plotted in power amplitude (dB) versus periods (s) [7], [12], [14]- [17]. The upper black solid line means that the New High Noise Model (NHNM) and the lower means that the New Low Noise Model.…”

Section: Resultsmentioning

confidence: 99%

“…The observed probability of the power spectral density as a tool to assess the field performance of earthquake monitoring system and the statistical distribution of noise levels across the frequency spectrum [1]. The more statistically approach requires large sample sizes, which become the norm as advances in probability power spectral density [2], [3]. In this study, we use the datasets of the broadband network from DNP, IGBI, and PLAI which deployed in BMKG network, Indonesia.…”

Section: Introductionmentioning

confidence: 99%

Spectral Probability Density for Broadband Seismic Ambient Noise Level

Sari¹,

Panjaitan²,

Sunandar³

et al. 2021

JoCPES

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The time-series approach is commonly utilized to get to the estimation of the likelihood thickness work of control ghostly densities (PDF PSD) of waveform information. This paper is concerned with the introduction of the evaluation of waveform commotion to degree the likelihood thickness work (PDF) be done inside, we utilized the metadata from a stock, a parser occurrence of DNP (Denpasar, Bali, Indonesia), IGBI (Ingas, Bali, Indonesia), and PLAI (Plampang, NTB, Indonesia) from BMKG IA-Networks and computations are based on the schedule utilized by McNamara Demonstrate. The point of this paper to characterize the current and past execution of the stations and recognizing the data on clamor levels at BMKG IA-Networks Station. The result of this paper shows the consistency of the unearthly is displayed the DNP, IGBI, and PLAI organize to confirm the quality of information conjointly acts as a test execution broadband arrange to the time taken by the broadband organize within the field and examination the Lombok earthquake in 2018.

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

Sinambela

Tarigan

Humaidi

et al. 2020

J. Phys.: Conf. Ser.

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Classification of seismic signal waveform is an essential component to realize the characteristics of the signal. The processing of the waveform signal is broadly used for the analysis of the real-time seismic signal. The numerous wavelet filters are developed by spectral synthesis using machine learning python to realize the signal characteristics. Our research aims to generate the performance of seismic signal and processing the waveform from Broadband Network Station by using Wavelet-Based on Machine Learning. In this case, we use Continuous Wavelet Transform (CWT) on Morlet. CWT is also clearly to identify spectral amplitudes and frequency-energy from the component of signal seismic performed by Broadband Network in Indonesia. The characteristic of the digital broadband network in Indonesia is variance. Our project tries to classification and evaluate the Broadband Seismic Network which deployed in Sumatera Region, Indonesia by using Power Spectral Density Probability Density Function (PSDPDF).

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The Characteristics Influence of The Seismic Signal Noise Using Spectral Analysis

Cited by 6 publications

References 2 publications

Shear Wave Splitting Analysis Beneath Sumatra For-Arc Inferred from Broadband Seismic Network Station

Shear Wave Splitting Analysis Beneath Sumatra For-Arc Inferred from Broadband Seismic Network Station

Spectral Probability Density for Broadband Seismic Ambient Noise Level

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

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