A new algorithm developed in Spain can identify volcanic warning signals and even forecast when eruptions are likely to fade.
Science confirms it: it is possible to anticipate volcanic eruptions 48 hours in advance in these environments
Spain’s Geological and Mining Institute (IGME-CSIC), the National Geographic Institute (IGN) and the University of Valencia have developed a new methodology capable of anticipating volcanic eruptions in urban environments up to 48 hours in advance.
The research, published in Scientific Reports and disseminated by Spain’s National Research Council (CSIC), has been recognized as a pioneering method worldwide.
The study is based on data collected by CSIC staff during the eruption of the Tajogaite volcano at Cumbre Vieja, on the island of La Palma. According to Raúl Pérez, a researcher at IGME-CSIC, “all of this made it possible to define signals associated with changes in eruptive dynamics, revealing that the eruption lasted 86 days due to five deep magma injections.”
The system relies on a series of algorithms capable of precisely identifying eruption precursor signals around 48 hours before they occur. From a technical standpoint, it detects a ‘transition’ in the long-term memory of the magma, indicating that it has stopped being stagnant or moving slowly.
Point of no return
CSIC explains that “the change in the magma’s pattern, evidenced through irregular seismicity, marks a point of no return prior to an eruption.”
Beyond forecasting whether a volcano is about to erupt, the tool can also detect when eruptive activity is losing strength. Scientists stress that this is made possible by an algorithm that identifies the exhaustion of activity, a key factor for emergency management.
As the state research body notes, “this ability to predict the potential cessation of activity allows authorities to plan the return of the population and the start of reconstruction.”
The system’s potential has been such that it has been incorporated into the United Nations Office for Disaster Risk Reduction via PreventionWeb, a global knowledge-sharing platform aimed at improving emergency and natural disaster management.
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