Volcanic eruptions cause significant loss of lives and property around the world each year. Their importance is highlighted by the sheer number of volcanoes for which eruptive activity is probable. These volcanoes are classified as in a state of unrest. The Global Volcano Project maintained by the Smithsonian Institution estimates that approximately 600 volcanoes, many proximal to major urban areas, are currently in this state of unrest. A spectrum of phenomena serve as precursors to eruption, including ground deformation, emission of gases, and seismic activity. The precursors are caused by magma upwelling from the Moho to the shallow (2-5 km) subsurface and magma movement in the volcano conduit immediately preceding eruption.
Precursors have in common the fundamental petrologic processes of melt generation in the lithosphere and subsequent magma differentiation. Our ultimate objective is to apply state-of-the-art machine learning techniques to volcano eruption forecasting. In this paper, we applied machine learning techniques to the precursor data, such as the 1999 eruption of Redoubt volcano, Alaska, for which a comprehensive record of precursor activity exists as USGS public domain files and global data bases, such as the Smithsonian Institution Global Volcanology Project and Aerocom (which is part of the HEMCO data base). As a result, we get geophysically meaningful results.