Machine Learning Approaches to Identifying Changes in Eruptive State Using Multi-Parameter Datasets From the 2006 Eruption of Augustine Volcano, Alaska

Abstract

Understanding the timing of critical changes in volcanic systems, such as the beginning and end of eruptive behavior, is a key goal of volcanic monitoring. Traditional approaches to forecasting these changes have used models motivated by the underlying physics of eruption onset, which assume that geophysical precursors will consistently display similar patterns prior to transition in volcanic state. We present a machine learning classification approach for detecting significant changes in patterns of volcanic activity, potentially signaling transitions during the onset or end of volcanic activity, which does not require a model of the physical processes underlying critical changes. We apply novelty detection, where models are trained only on data prior to eruption, to the precursory unrest at Augustine Volcano, Alaska in 2005. This approach looks promising for geophysically monitored volcanic systems which have been in repose for some time, as no eruptive data is required for model training. We compare novelty detection results with multi-class classification, where models are trained on examples of both non-eruptive and eruptive data. We contextualize the results of these classification models using constraints from petrological, satellite and visual observations from the 2006 eruption of Augustine Volcano. The transition from non-eruptive to eruptive behavior we identify in mid-November 2005 is in agreement with previous estimates of the initiation of dike intrusion prior to the 2006 eruption. We find that models which include multiple types of data (seismic, deformation, and gas emissions) can better distinguish between non-eruptive and eruptive data than models formulated on single data types.