How AI and satellites could help predict volcanic eruptions

Abstract

V olcanologists are combining satellite measurements of ground movements with artificial intelligence to more accurately monitor-and eventually predict-volcanic eruptions. Although about 800 million people live within 100 kilometres of a volcano, very few of these potential natural hazards are monitored consistently. But emerging methods are now enabling researchers to keep a constant eye on volcanoes, says Juliet Biggs, a volcanologist at the University of Bristol, UK. Her team will present its work, which uses machine learning to spot ground distortion around volcanoes, on 20 March at a conference in Santa Fe, New Mexico. Biggs and her colleagues use radar observations from two satellites that make up the European Sentinel-1 mission. Depending on their location as they orbit Earth, the craft collect data on the world's volcanoes every 6, 12 or 24 days. As they repeatedly pass over the same spot, the satellites measure the distance between themselves and the ground. This can indicate whether that distance has changed over time-which can happen when the ground lifts or drops as magma shifts beneath a volcano. But interpreting such data is not always straightforward. Water vapour in the atmosphere can mimic the signal of shifting ground, and researchers must account for this when looking at radar observations. These atmospheric distortions are particularly problematic when scientists are trying to work in near-real time. Agung volcano on the Indonesian island of Bali erupts in November 2017.

How can seismo-volcanic catalogues be improved or created using robust neural networks through weakly supervised approaches?

“…Understanding the dynamics of active volcanoes, and even more so, carrying out Early Warning protocols for volcanic eruptions require multiparametric observations focused on accomplish accurate and effective monitoring (Sparks, 2003). The objective of identifying precursors that warn of a possible volcanic eruption involves the analysis of long temporal series of data, characterizing and relating them with source models associated with the internal dynamics of the volcano (Witze, 2019;Palmer, 2020). Currently, the availability of multiparametric long-time data series, such as seismology, deformation, from previous eruptive episodes (Chouet, 2003;McNutt, 2015).…”