This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Whakaari/White Island's sudden eruption in December 2019, which killed 22 people and severely injured 25 others, was New Zealand's deadliest volcanic disaster in recent history. But it was not a freak event.
Over the past 20 years, New Zealand has experienced another half-dozen sudden volcanic explosions with the potential to kill or injure people nearby. In many respects, these were near misses, occurring at night or when few tourists or workers were present. Yet none was successfully forecast early enough to warn people or evacuate them beforehand.
The danger posed by such sudden eruptions is not unique to New Zealand. In 2014, Japan's Mount Ontake erupted with little warning, killing 63 people, many of whom had been hiking near the summit. Our new research published in Nature Communications suggests machine-learning techniques could help provide more warning.
By detecting subtle changes in the continuous vibrations around a volcano, they can potentially identify signs of an impending eruption hours beforehand. But what if faster warnings also mean more false alarms? How many should we accept if earlier warnings can save lives?
Under conventional warning systems, experts must interpret complex unrest signals and assess the level of risk before sounding the alarm. This is essential for understanding a developing volcanic crisis. But it faces a fundamental challenge when escalation occurs over minutes or hours rather than days or weeks.
Automated warnings, drawing directly on real-time monitoring data, could complement expert judgment by responding rapidly when volcanic unrest suddenly changes. Volcano observatories have good reasons for caution. An algorithm might miss an eruption, detect signals scientists cannot yet fully explain, lack corroborating evidence or generate too many false alarms.
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