Abstract
A volcano-Tectonic earthquake, commonly referred to as VT, is an earthquake caused by magma intrusion that increases the pressure below the volcano’s surface. The accumulation of stress that continuously affects the elasticity of rocks causes fractures when the elasticity limit of rocks is exceeded. VT is one of the earthquakes used as a parameter to decide the level of volcanic activity. To understand the characteristics of VT, it is necessary to do features engineering, which is a process of extracting features so that the characteristics of VT are obtained. The data used in this study was the VT earthquake when Agung was in crisis in 2017. The extraction process is conducted by performing statistics calculations in temporal and spectral domains. The waveform of VT is univariate time series data, and to perform the extraction of features, this study uses changes in amplitude value to the time taken from the waveform. Features that were successfully extracted from this study are as many as 48 features. The result of the extraction of these features can be used as input parameters in performing auto-classification of VT using machine learning.
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