Benchmarking Current and Emerging Approaches to Infrasound Signal Classification

Abstract

Abstract Low-frequency sound ≤20  Hz, known as infrasound, is generated by a variety of natural and anthropogenic sources. Following an event, infrasonic waves travel through a dynamic atmosphere that can change on the order of minutes. This makes infrasound event classification a difficult problem, as waveforms from the same source type can look drastically different. Event classification usually requires ground-truth information from seismic or other methods. This is time consuming, inefficient, and does not allow for classification if the event locates somewhere other than a known source, the location accuracy is poor, or ground truth from seismic data is lacking. Here, we compare the performance of the state of the art for infrasound event classification, support vector machine (SVM) to the performance of a convolutional neural network (CNN), a method that has been proven in tangential fields such as seismology. For a 2-class catalog of only volcanic activity and earthquake events, the fourfold average SVM classification accuracy is 75%, whereas it is 74% when using a CNN. Classification accuracies from the 4-class catalog consisting of the most common infrasound events detected at the global scale are 55% and 56% for the SVM and CNN architectures, respectively. These results demonstrate that using a CNN does not increase performance for infrasound event classification. This suggests that SVM should be the preferred classification method, as it is a simpler and more trustworthy architecture and can be tied to the physical properties of the waveforms. The SVM and CNN algorithms described in this article are not yet generalizable to other infrasound event catalogs. We anticipate this study to be a starting point for development of large and comprehensive, systematically labeled, infrasound event catalogs, as such catalogs will be necessary to provide an increase in the value of deep learning on event classification.