A.1 Experiment 1 A.1.1 Participants Experiment 1 recruited 21 participants (11 females, aged 18-25). All participants had provided informed consent before the experiment. Cover story Participants were told that they were apprentice magicians in a magical world. In this world, dangerous magic lava rocks were emitted from an unknown number of invisible volcano(es). On each trial, they observed past landing locations of lava rocks in a specific area (on the screen), and their job was to predict the probability density of future landing locations. More specifically, they were asked to draw a probability density by reporting, using click-and-drag mouse gestures, three key properties of the volcano(es), corresponding to the mean, the weight, and the standard deviation of a Gaussian component. They were told that their bonus payment depended on the accuracy of the reported predictive density.

Karoline Leavitt's family member'abruptly arrested' by ICE after living in US for decades Sir Richard Branson reveals his wife Joan died'quickly and painlessly' while in hospital for a back injury - as he says'life will never be the same' without his'shining star' Residents in liberal Western US city feel'isolated' as state turns extremely red What HAS happened to Beyoncé? Suddenly desperate, I know what's really going on... and it's ugly: CAROLINE BULLOCK LIZ JONES: Sorry, but it's now time for Kate to stop making excuses Teenager dragged from car'by migrant gang' and raped in front of her fiancé describes her night of hell and reveals they warned her'if you scream we'll kill you' Virginia Giuffre's family is at war over who gets Andrew's multi-million payout after she died without leaving a will Prince Philip nicknamed Meghan Markle'DOW' and warned Royal Family about her'eerie similarities' with Wallis Simpson, royal author reveals Sports broadcaster's wife suffers unimaginable tragedy just before he goes on air New'Hollywood of the South' emerges as booming industry generates $1bn... but long-time residents are furious University of Minnesota program offers guidelines to'reverse the whiteness pandemic' Putin'sends top general to Venezuela along with troops tasked with training up President Maduro's forces' as US considers attacking South American country Forget Yellowstone or Etna! 'Hidden' volcanoes pose the greatest risk to the world, scientists warn - after little-known mount erupts in Ethiopia READ MORE: Scientists discover a new hole in one of Yellowstone's basins A little-known Ethiopian volcano has erupted for the first time in at least 12,000 years - sparking fears that'hidden' volcanoes are being missed. Professor Mike Cassidy, a volcanologist at the University of Birmingham, says the world's overlooked volcanoes'pose the greatest threat'. Known as'hidden' volcanoes, they're less famous than Yellowstone or Etna even among scientists - which means they're not being monitored as much. Examples include El Chichón in Mexico, Mount Pinatubo in the Philippines, Mount Merapi in Indonesia and La Soufrière on the Caribbean island of Saint Vincent.

Scientists reveal what triggered Santorini'earthquake swarm' The swarm of tens of thousands of earthquakes near the Greek island of Santorini earlier this year was triggered by molten rock pumping through an underground channel over three months, scientists have discovered. They used physics and artificial intelligence to work out exactly what caused the more than 25,000 earthquakes, which travelled about 20km (12 miles) horizontally through the Earth's crust. They used each of the tremors as virtual sensors, then used artificial intelligence to analyse patterns associated with them. One of the lead researchers, Dr Stephen Hicks from UCL, said combining physics and machine learning in this way could help forecast volcanic eruptions. The seismic activity started to stir beneath the Greek islands of Santorini, Amorgos, and Anafi in January 2025.

Natural disasters, such as volcanic eruptions, pose significant challenges to daily life and incur considerable global economic losses. The emergence of next-generation small-satellites, capable of constellation-based operations, offers unparalleled opportunities for near-real-time monitoring and onboard processing of such events. However, a major bottleneck remains the lack of extensive annotated datasets capturing volcanic activity, which hinders the development of robust detection systems. This paper introduces a novel dataset explicitly designed for volcanic activity and eruption detection, encompassing diverse volcanoes worldwide. The dataset provides binary annotations to identify volcanic anomalies or non-anomalies, covering phenomena such as temperature anomalies, eruptions, and volcanic ash emissions. These annotations offer a foundational resource for developing and evaluating detection models, addressing a critical gap in volcanic monitoring research. Additionally, we present comprehensive benchmarks using state-of-the-art models to establish baselines for future studies. Furthermore, we explore the potential for deploying these models onboard next-generation satellites. Using the Intel Movidius Myriad X VPU as a testbed, we demonstrate the feasibility of volcanic activity detection directly onboard. This capability significantly reduces latency and enhances response times, paving the way for advanced early warning systems. This paves the way for innovative solutions in volcanic disaster management, encouraging further exploration and refinement of onboard monitoring technologies.

A.1 Experiment 1 A.1.1 Participants Experiment 1 recruited 21 participants (11 females, aged 18-25). All participants had provided informed consent before the experiment. Cover story Participants were told that they were apprentice magicians in a magical world. In this world, dangerous magic lava rocks were emitted from an unknown number of invisible volcano(es). On each trial, they observed past landing locations of lava rocks in a specific area (on the screen), and their job was to predict the probability density of future landing locations. More specifically, they were asked to draw a probability density by reporting, using click-and-drag mouse gestures, three key properties of the volcano(es), corresponding to the mean, the weight, and the standard deviation of a Gaussian component. They were told that their bonus payment depended on the accuracy of the reported predictive density.

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It's been left unread for nearly 2,000 years, last glimpsed when the Roman Empire ruled over Europe. Now, scientists have used AI to virtually unfurl one of the Herculaneum scrolls – the ancient documents buried by the eruption of Mount Vesuvius in AD 79. 'It's an incredible moment in history as librarians, computer scientists and scholars of the classical period are collaborating to see the unseen,' said Richard Ovenden, senior executive Bodleian Libraries. 'The astonishing strides forward made with imaging, and AI are enabling us to look inside scrolls that have not been read for almost 2,000 years.' The Herculaneum scrolls are thought to contain profound philosophical and literary texts from ancient Greek and Roman scholars. The problem is that any attempts to unroll the burnt cylinders will turn them to dust because they are so fragile – meaning the words would be lost forever.

Scientists have warned that an underwater volcano off the coast of the northwestern US is likely to blow sometime in 2025. The volcano, called Axial Seamount, is more than 3,600-feet-tall and sits half a mile underwater just 300 miles off the coast of Oregon. Experts made the prediction on December 10 after detecting seafloor swelling around Axial that mimicked a level seen immediately before an eruption in 2015. Seismic activity has also increased, with hundreds of earthquakes generated around the volcano per day and earthquake swarms greater than 500 per day. 'Based on the current trends, and the assumption that Axial will be primed to erupt when it reaches the 2015 inflation threshold, our current eruption forecast window is between now (July 2024) and the end of 2025,' researchers said in the new study.

In volcano monitoring, effective recognition of seismic events is essential for understanding volcanic activity and raising timely warning alerts. Traditional methods rely on manual analysis, which can be subjective and labor-intensive. Furthermore, current automatic approaches often tackle detection and classification separately, mostly rely on single station information and generally require tailored preprocessing and representations to perform predictions. These limitations often hinder their application to real-time monitoring and utilization across different volcano conditions. This study introduces a novel approach that utilizes Semantic Segmentation models to automate seismic event recognition by applying a straight forward transformation of multi-channel 1D signals into 2D representations, enabling their use as images. Our framework employs a data-driven, end-to-end design that integrates multi-station seismic data with minimal preprocessing, performing both detection and classification simultaneously for five seismic event classes. We evaluated four state-of-the-art segmentation models (UNet, UNet++, DeepLabV3+ and SwinUNet) on approximately 25.000 seismic events recorded at four different Chilean volcanoes: Nevados del Chill\'an Volcanic Complex, Laguna del Maule, Villarrica and Puyehue-Cord\'on Caulle. Among these models, the UNet architecture was identified as the most effective model, achieving mean F1 and Intersection over Union (IoU) scores of up to 0.91 and 0.88, respectively, and demonstrating superior noise robustness and model flexibility to unseen volcano datasets.

A new technique that analyzes seismic signals to predict days in advance when America's most dangerous volcano will erupt. Mount St Helens, located in Washington State, has recently showed signs of recharging and scientists have developed a machine learning tool to find patterns of volcanic activity to provide better emergency plans. The system was able to determine when the volcano experienced unrest, pre-eruptive and eruptive periods. Using the data, the technology predicted at least three days in advance when the volcano would erupt - with 95 percent accuracy. The study comes less than 10 days since the Pacific Northwest Seismic Network revealed it detected with 350 earthquakes in the region since February, which are signs the volcano may be awakening.