Implementation of machine learning for volcanic earthquake pattern classification using XGBoost algorithm

Classification methods with Machine Learning have been used in various fields, including volcanology. This method makes it possible to automatically perform data processing and classification on large scale data in a short time. Seismic time series data from volcanic earthquakes of Mt. Merapi are classified into five types of earthquakes, i.e.: Volcanic Type A (VTA), Volcanic Type B (VTB), Multiphase (MP), Low-frequency (LF) and Rockfall (RF), with a total sample data of 266 earthquakes, could be classified automatically using machine learning. The features of the signal are taken from the dominant frequency value, the spectral pattern that is formed, and the amplitude value to differentiate the earthquake dataset into each class. Classification is done using the XGBoost algorithm. Tests were carried out using various data test–train percentage to get the best result. Based on the XGBoost function test, which the best value obtained with an accuracy of testing the data is 76.67% using the 70%:30% train–test scenario. The results of this accuracy value are greater when compared to the accuracy value of previous studies with the SVM method which is 74.4% accuracy. This research also has better results in spectral analysis with the results of spectral comparison plots that appear to show more significant differences.