Enhancing the classification of seismic events with supervised machine learning and feature importance

The accurate classification of seismic events into natural earthquakes (EQ) and quarry blasts (QB) is crucial for geological understanding, seismic hazard mitigation, and public safety. This paper proposes a machine-learning approach to discriminate seismic events, particularly differentiating between natural EQs and man-made QBs. The core of this study is to integrate different features into a unified dataset to train some linear and nonlinear supervised machine learning (ML) models. The proposed approach considers a collection of 837 events (EQs and QBs) with local magnitudes of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>1.5</mml:mn> <mml:mo>≤</mml:mo> <mml:msub><mml:mi>M</mml:mi> <mml:mi>L</mml:mi></mml:msub> <mml:mo>≤</mml:mo> <mml:mn>3.3</mml:mn></mml:mrow> </mml:math> from the Egyptian National Seismic Network (ENSN) seismic event catalog between 2009 and 2015. This paper's principal contribution is applying feature selection techniques and feature importance analysis to identify the best features leading to the best events' discrimination. In other words, the proposed approach enhances classification accuracy and provides insights into which features are most crucial for distinguishing between EQ and QB events. The results show that with only three features, corner frequency, power of event, and spectral ratio, the best-developed ML model accomplishes a discrimination accuracy of 100% among several benchmarks of linear and non-linear models.