Researchers Uncover Insights into Vulcano’s Volcanic Activity

Researchers have made significant strides in understanding the ongoing unrest of the Vulcano volcano, located in the Aeolian archipelago in the Tyrrhenian Sea, southern Italy. Notably, Vulcano has a history of explosive eruptions, with the last recorded activity occurring between 1888 and 1890. Since 2021, the island has shown signs of unrest due to an active hydrothermal system, raising concerns about the potential risks to the influx of tourists during the summer months.

To investigate the underlying causes of this unrest, a team from the University of Geneva has partnered with the National Institute of Geophysics and Volcanology (INGV). They developed a comprehensive 3D model of the volcano’s interior using a combination of nodal seismic networks and artificial intelligence (AI). This innovative approach allows for a deeper examination of volcanic structures that have previously been difficult to study due to their inaccessibility.

Despite earlier studies providing insights into the outlines of volcanic plumbing systems, they often lacked the resolution needed to differentiate between magma and hydrothermal fluids. The researchers deployed a network of 196 seismic sensors across Vulcano and the nearby island of Lipari to collect data on secondary seismic waves, known as S-waves. These waves travel slowly through fluid-rich areas, enabling scientists to identify magma presence more accurately.

The data collection took place over the course of one month, capturing natural ground vibrations during the volcano’s early unrest phase. The resulting model delivered high-resolution imagery akin to an “X-ray” of the volcano. According to lead author Douglas Stumpp, “Our study shows that our end-to-end ambient noise tomography method works with an unprecedented resolution due to using dense nodal seismic networks. The use of deep neural networks allowed us to quickly and accurately measure enormous seismic dispersion data to provide near-real-time monitoring.”

The model revealed no new magma body between Lipari and Vulcano within the first 2 km of the Earth’s crust. However, it identified areas that could contain cooling melts at the base of the hydrothermal system. These melts are thought to potentially release gases and brines if disturbed by tectonic activity, suggesting that fault dynamics may contribute to volcanic unrest.

While the current model does not enable precise eruption predictions, it enhances understanding of the internal dynamics of volcanoes during unrest. The integration of AI allows rapid processing of large datasets, positioning this approach as a possible early warning system for future volcanic activity.

Research advancements could lead to the development of dynamic evacuation plans tailored to real-time volcanic behavior, potentially safeguarding lives. Stumpp noted that while the implementation of such systems will take time due to the challenges of real-time data processing, advancements in machine learning and deep learning are making it increasingly feasible.

Looking ahead, Stumpp emphasized the potential for further research, stating, “Our study paves the ground for 4D ambient noise tomography monitoring—three dimensions of space and one dimension of time. Permanent and maintained seismic nodal networks with telemetric access to the data need to be implemented to achieve this goal.”

The findings of this research have been published in Nature Communications, contributing valuable insights into the monitoring and understanding of volcanic activity in the region.