A machine learning based sensing and measurement framework for timing of volcanic eruption and categorization of seismic data

Abstract

The circumstances and factors which determine the volcanic explosive ejection are unknown, and currently, there is no effective way to determine the end of a volcanic explosive ejection. At present, the end of an eruption is determined by either generalized standards or the measurement which is unique to the volcano. We investigate the use of controlled machine learning techniques such as Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and Gaussian Process Classifiers (GPC), and create a decisiveness index D to assess the uniformity of the groups provided by these machine learning models. We analyzed the measured end-date obtained by seismic information categorization is two to four months later than the end-dates determined by the earliest instance of visible eruption for both volcanic systems. Likewise, the measurement systems, measurement technology becomes key elements in the seismic data analysis. The findings are consistent across models and correspond to previous, broad definitions of ejection. Obtained classifications demonstrate a more significant relationship between eruptive movement and visual activity than information base records of ejection start and completion timings. Our research has presented a new measurement-based categorization technique for studying volcanic eruptions, which provides a reliable tool for determining whether or not an emission has stopped without the need for visual confirmation.