Volcano Video Data Characterized and Classified Using Computer Vision and Machine Learning Algorithms

Video cameras are common at volcano observatories, but their utility is often limited during periods of crisis due to the large data volume from continuous acquisition and time requirements for manual analysis. For cameras to serve as effective monitoring tools, video frames must be synthesized into relevant time series signals and further analyzed to classify and characterize observable activity. In this study, we use computer vision and machine learning algorithms to identify periods of volcanic activity and quantify plume rise velocities from video observations. Data were collected at Villarrica Volcano, Chile from two visible band cameras located ~17 km from the vent that recorded at 0.1 and 30 frames per second between February and April 2015. Over these two months, Villarrica exhibited a diverse range of eruptive activity, including a paroxysmal eruption on 3 March. Prior to and after the eruption, activity included nighttime incandescence, dark and light emissions, inactivity, and periods of cloud cover. We quantify the color and spatial extent of plume emissions using a blob detection algorithm, whose outputs are fed into a trained artificial neural network that categorizes the observable activity into five classes. Activity shifts from primarily nighttime incandescence to ash emissions following the 3 March paroxysm, which likely relates to the reemergence of the buried lava lake. Time periods exhibiting plume emissions are further analyzed using a row and column projection algorithm that identifies plume onsets and calculates apparent plume horizontal and vertical rise velocities. Plume onsets are episodic, occurring with an average period of ~50 s and suggests a puffing style of degassing, which is commonly observed at Villarrica. However, the lack of clear acoustic transients in the accompanying infrasound record suggests puffing may be controlled by atmospheric effects rather than a degassing regime at the vent. Methods presented here offer a generalized toolset for volcano monitors to classify and track emission statistics at a variety of volcanoes to better monitor periods of unrest and ultimately forecast major eruptions