Machine Learning For Waveform Spectral Analysis On Signal Seismic With Broadband Vertical Component

Marzuki Sinambela, Janner Simarmata, Eva Darnila, Naikson Fandier Saragih, Parulian Siagian, Putri Ramadhani, Kerista Tarigan, Sunardi Sunardi

Abstract


Machine learning of seismic signal waveform is core component to realize the characteristics of signal. The processing of waveform signal is broadly used for analysis of real time seismic signal. The numerous wavelet filters are developed by spectral synthesis using machine learning python to realize the signal characteristics. Our paper aims to generate and processing the row data of waveform from seismic sensor by using Continuous Wavelet Transform (CWT). CWT is clearly to identify of spectral amplitudes and frequency-energy from component of signal seismic performed by Broadband Network in Indonesia. Finally, by machine learning python allows good time resolution for identified and performed of seismic signal from broadband which deployed in Indonesia.


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References


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