Waveform Analysis Of Broadband Seismic Station Using Machine Learning Python Based On Morlet Wavelet
DOI:
https://doi.org/10.55606/juisik.v3i3.920Keywords:
earthquake clustering analysis, clustering algorithms—K-means, DBSCANAbstract
Wavelet signal processing 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 solve and evaluating the frequencies-energy characteristic of earthquake. The wavelet method by Continuous Wavelet Transform (CWT) is able to clearly and simultaneously of amplitudes and frequency-energy from component between the seismogram which seismic sensor broadband recorded in the January 16, 2017 in Medan, North Sumatra. Finally, from machine learning python with morlet wavelet allows good time resolution for high frequencies, and good frequency resolution for low frequencies
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