Drones developed by TUM researchers track gas to predict volcanic eruptions
A new drone-based laser system developed by TUM researchers maps volcanic gas concentrations to detect early warning signs of eruptions with high accuracy.
Researchers at the Technical University of Munich (TUM) have developed a drone-based system to track volcanic gas emissions with unprecedented precision, offering a critical tool for predicting eruptions. The technology, tested on the Aeolian island of Vulcano off Sicily, uses laser beams and autonomous algorithms to map gas concentrations, particularly the ratio of carbon dioxide to sulfur dioxide, which serves as a key indicator of subterranean volcanic activity.
The system involves a laser mounted on a ground-based cart that emits an invisible beam toward a drone flying above a volcanic plume. The drone, equipped with a reflector, redirects the beam back to the sensor. As the laser passes through gas clouds, it is absorbed by specific compounds, allowing researchers to calculate concentrations of gases like carbon dioxide. This method avoids interference from background signals such as vegetation or soil emissions, which have historically complicated ground-level measurements.
“This is more precise and safer,” said Prof. Achim Lilienthal, deputy director of TUM’s MIRMI Robotics Institute. The drone follows a pre-determined flight path up to 60 meters away from the laser, taking up to 3,000 measurements over 10 to 15 minutes. An algorithm then generates a map of gas distribution at a given altitude, factoring in wind conditions. Wind tunnel tests have shown the method achieves a 5% measurement error, according to TUM research.
The ratio of carbon dioxide to sulfur dioxide is particularly significant. As magma rises, it releases gases whose composition changes with depth and pressure. Volcanologist Nicole Bobrowski of Heidelberg University noted that this ratio often spikes and then declines before an eruption, providing a potential early warning signal. “For example, the ratio of carbon dioxide to sulfur dioxide initially rises sharply and then falls again before the eruption begins,” she explained.
While TUM’s approach relies on laser-based remote sensing, other teams employ different methods. Researchers at Johannes Gutenberg University Mainz use drones equipped with onboard sensors to measure gas concentrations directly within plumes. These sensors detect light absorption or electrochemical reactions to identify gases like sulfur dioxide. “We fly directly into the volcanic plume, which allows us to determine the gas concentrations along the flight path,” said Prof. Thorsten Hoffmann, whose team has tested similar systems on Mount Etna and the Phlegraean Fields near Naples.
The technology is part of broader efforts to enhance volcanic monitoring. Drones have been used for over a decade to study volcanoes, but recent advancements aim to improve accuracy and reduce risks. For instance, a study published in *Nature* highlighted the development of lightweight drones (under 0.9 kg) capable of reaching remote areas with minimal logistical challenges. These devices, unlike larger models, can be transported on foot and require fewer flight preparations, making them ideal for volatile environments.
On Vulcano, TUM researcher Marius Schaab deployed the system autonomously, marking a milestone in drone-based volcanic monitoring. The drone’s ability to avoid direct exposure to corrosive plumes—by flying behind them—reduces the need for frequent recalibration. “Our drone flies behind the plume and also our ground unit is not in the plume,” Schaab noted. This setup minimizes damage to sensors and ensures safer data collection.
Other applications of drone technology in volcanology include Lidar systems, which use laser pulses to create 3D topographic maps. These maps help track ground deformation and identify potential eruption sites. While Lidar drones are not the focus of TUM’s work, their integration with gas-measuring systems could further refine hazard assessments. “The precision of Lidar technology is beneficial in understanding and predicting various natural phenomena,” said Susanne Buiter of the German Research Centre for Geosciences (GFZ).
The research underscores the growing role of artificial intelligence in volcanic monitoring. TUM’s team aims to automate data interpretation, enabling real-time analysis of gas patterns. “Our goal is to automate the measurement and mapping processes and have artificial intelligence interpret the data,” Lilienthal said. This could lead to faster, more reliable predictions, particularly in regions like Sicily, where Mount Etna’s frequent activity demands continuous surveillance.
The collaboration between TUM, Mainz, and international partners highlights the potential for cross-disciplinary solutions. As Lilienthal emphasized, “Artificial intelligence is shaping our working lives, research, and the world around us.”
