Waveform analysis of broadband seismic station using machine learning Python based on Morlet wavelet

Darnila, Eva; Ula, Mutammimul; Tarigan, Kerista; Limbong, Tonni; Sinambela, Marzuki

doi:10.1088/1757-899x/420/1/012048

Cited by 15 publications

(8 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…The height of the signal peak and its location in the frequency spectrum are ideal characteristics for classifiers (random forest, gradient boosting, logistic regression, etc.) used in machine learning [11]. However, studies in [8][9][10][11][12] confirm that filtering using only the continuous wavelet transform does not solve the problem of filtering high-frequency and low-frequency noise.…”

Section: Analysis Of the Literature Data And A Formulation Of The Pro...mentioning

confidence: 99%

Analysis of Discrete Wavelet Spectra of Broand Signals

Oliinyk,

Taranenko,

Lopatin

2023

Computer Modeling and Intelligent Systems

View full text Add to dashboard Cite

The article is devoted to the development of a mathematical model for autoregressive and autocoherent analysis of discrete wavelet spectra with approbation on known experimental data. The model is used to detect anomalies or emergency states of the object after wavelet filtering of noise and zero crossing. The proposed method for processing noisy signals, combining noise filtering and Mahalanobis proximity analysis, shows better machine learning results than neural networks and other methods for detecting classification and recognition anomalies. The wavelet spectra were processed using autoregressive and autocoherent analyses. The possibility of using these functions as classification features for identifying possible anomalous states of devices is shown. Before forming the wavelet spectra, the procedure of direct with filtering and inverse transformation of the already filtered signal is carried out. As a result, it seems possible to identify the characteristic features of the signal -anomalies or emergency conditions. The main factor in the classification of device status signals is the moment when changes in the signal spectrum begin to develop, therefore, all analysis, both in the time and frequency domain, is tied either to the number of samples by the time the signal was received, and in some cases even to the extended date format -month number.

show abstract

Waveform analysis of broadband seismic station using machine learning Python based on Morlet wavelet

Cited by 15 publications

References 3 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

Analysis of Discrete Wavelet Spectra of Broand Signals

Contact Info

Product

Resources

About