On finding possible frequencies for recognizing microearthquakes at Cotopaxi volcano: A machine learning based approach

Abstract Adequate detection and classification of seismic events are crucial for understanding the internal status of a Volcano. Machine learning-based classifiers use different features from the time, frequency, and scale domains related to seismic events. Regarding power spectrum-based features, several methods can be used to compute such features. However, the more suitable method for analyzing volcanic activity is undetermined. This paper presents a study about the main frequency bands, which allows maximizing the performance metrics of an automated classifier for long-period (LP) and volcano-tectonic (VT) events based on parametric (Yule-Walker and Burg) and non-parametric (Welch and Multitaper) power spectrum density estimation methods. Feature selection using embedded (pruning) and wrapper (recursive feature elimination) methods was applied to select the main frequencies that maximize the balanced error rate of suitable classification algorithms, such as decision trees (DT) and support vector machines (SVM). Bootstrapping was used to estimate a confidence interval for the frequencies of the microearthquakes. An amplitude threshold difference of at least 3 dB was used to guarantee that possible frequency features that characterize each type of event do not overlap between classes. The method who achieved the worst overall performance was not considered by the voting strategy. A Dataset from Cotopaxi volcano was used to test the proposed classification schema. The best results show for DT classifier a total of 10 key frequencies, while for SVM classifier 39 key frequencies grouped in three main frequency bands, as main features to distinguish LP events from VT earthquakes. The best classification results were achieved by the Welch method with the DT and by the Multitaper method with the SVM classifiers. Furthermore, the study confirms that there is a frequency band above 40 Hz, which seems like a critical feature for the detection and classification of stages.