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.

Machine learning of seismic waveform is core component to realize the characteristics of signal. The nuclear explosion is 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 nuclear explosion. The wavelet method by Continuous Wavelet Transform (CWT) is clearly to identify of amplitudes and frequency-energy from component of nuclear test performed by the North Korea that occurred on September 03, 2017. Finally, by machine learning python with Morlet wavelet allows good time resolution for identified and performed of Broadband Seismic from Comprehensive Nuclear-Test Ban Treaty Organization (CTBTO) in Indonesia.

Machine learning of nuclear explosion signal waveform is core component to realize the characteristics of signal. The processing of waveform explosion signal is broadly used for analysis of real time explosion signal. The numerous wavelet filters are developed by spectral synthesis using machine learning python to realize the signal characteristics. Our paper aims to generate the performance of nuclear explosion signal and processing the waveform from seismic sensor by using Continuous Wavelet Transform (CWT). CWT is also 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.

The high pulse voltage low power supply can be built with fly-back transformer as a converter. Generation by using fly-back converter is a significant method which can be improved the circuits of the device to make it simpler, and occupies a smaller space, and cheaper. The study aimed to study and develop a device that will obtain a high pulse voltage low power supply. The results of the study obtained are, the high pulse voltage has reached 50 kV at the frequency of 100 kHz. In addition, when the high pulse voltage is fed to the plane parallel, then at the distance of 50 cm there is a spark, which means it is can generate high pulsed electric fields, it was 50kV/cm.

The observation of earthquake monitoring station had deployed in Indonesia. The data for this study were earthquake data recorded from BMKG Seismic Network. We use the 2016 Aceh earthquake, struck the Indonesia Island of Sumatra with Mw of 6.5 in Aceh province on December 7th 2016, at 05:03 WIB (22:03 UTC December 6th 2016) with duration 14 days from December 1st 2016, 00:00:00 UTC to December 14th 2016 00:00:00 UTC. Spectral analysis response to determine the level of background noise in the seismic station by using the Power Spectral Density and the Probability of Density function from the spectral result. The results of our background noise analysis are useful for characterizing the performance of existing BMKG Network Station, for detecting operational problem.