Open AccessTime Frequency Signal Classification Using Continuous Wavelet Transformation
Author(s) -
Helbert Sirait,
Kerista Sebayang,
Syahrul Humaidi,
Timbangen Sembiring,
Kerista Tarigan,
Kurnia Sembiring,
Teguh Rahayu,
Ayun Ria Ainun,
Marzuki Sinambela
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/851/1/012045
Subject(s) - seismometer , broadband , wavelet , waveform , signal (programming language) , energy (signal processing) , geology , acoustics , signal processing , digital signal , seismology , computer science , telecommunications , digital signal processing , engineering , mathematics , artificial intelligence , statistics , electronic engineering , physics , radar , programming language
Time-frequency analysis can provide useful information in digital signal seismic data processing and interpretation. The energy concentration of the spectrum depends on the consistency of function of the time-frequency analysis and instantaneous frequency variation digital signal. In this case, we used the digital signal seismic from selected seismometer broadband which deployed in Sumatera Island. The aim of this study to classify the waveform based on the time-frequency analysis using continuous wavelet transform (CWT). The sample data used the earthquake of 20 February 2018 in North Sumatera. The result indicated the classification between the horizontal and vertical components from the seismometer broadband is different. The classification of vertical is affected by seismic source and horizontal component affected the site effect.