Implementation of Machine Learning Algorithms for Seismic Events Classification

Abstract

The classification of seismic events has been crucial for monitoring underground nuclear explosions and unnatural seismic events as well as natural earthquakes. This research is an attempt to apply different machine learning (ML) algorithms to classify various types of seismic events into chemical explosions, collapses, nuclear explosions, damaging earthquakes, felt earthquakes, generic earthquakes and generic explosions for a dataset obtained from IRIS-DMC. One major objective of this research has been to identify some of the best ML algorithms for such seismic events classification. The ML algorithms we are implementing in this study include logistic regression, support vector machine (SVM), Na\"ive Bayes, random forest, K-nearest neighbors (KNN), decision trees, and linear discriminant analysis. Our implementation of the above ML classifier algorithms required to prepare and preprocess the dataset we obtained so that it will be fit for the ML training and testing applications we sought. After the implementation of the ML algorithms, we were able to classify the seismic event types into seven classes in the dataset, and a comparison of each classifier is made to identify the best algorithm for the seismic data classification. Finally, we made predictions of the different event types using the different classifier algorithms, and evaluated each of the various classifier algorithms for seismic prediction using different evaluation metrics. These evaluation metrics helped us to measure the performance of each algorithm. After implementing the seven ML algorithms and a comparison among those various ML algorithms, it has been demonstrated that the best accuracy among these classifiers happened for the Random Forest (RF) algorithm, with an accuracy of 93.5%.