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Machine learning algorithms track the evolving seismicity crisis on the island of Santorini in Greece (Segou et al., in prep.). As expected, machine learning earthquake detection uncovers 10 times more earthquakes (in red) than standard seismic catalogues from the National Observatory of Athens (in grey). This is the first high-resolution image of the complex volcanic/seismic system (30 November 2024 to 8 February 2025). Prepared by Margarita Segou, Rajat Choudhary, Wayne Shelley, Foteini Dervisi and Brian Baptie. BGS © UKRI 2025.
News and events
Artificial intelligence is proving a game changer in tracking the Santorini earthquake swarm

Scientists are harnessing the power of machine learning to help residents and tourists by detecting thousands of seismic events.

A flooded field
News and events
New £38 million project to reduce the impact of floods and droughts

BGS will take a leading role in efforts to better predict the location and effects of extreme weather events.

Landslide aftermath. © Timo Volz, Unsplash
News and events
Studying multi-hazards from space

A new European Space Agency-funded project will study the effect earthquakes have on occurrences of other natural hazards in the long-term.

Mayon_Volcano_with_cloudy_hat-scaled
Research
Living in multi-hazard environments

Assessing geological hazard information to improve detection and forecasting of geo-hazards and build on current disaster risk management practices.

Landslide debris, which has fallen from the top of a cliff covered in grass to a sandy beach below. A broken tarmac road can be seen to the left, with two people in high visibility jackets looking down towards the rubble
News and events
Citizen science: become part of a real-time Global Landslide Detector

BGS and partners are trialling a new social media tool that detects worldwide landslides in real time.

Figure 1. A year of seismicity in the epicentral area of the 2016 M=6.0 Amatrice earthquake (star) in Italy color coded by time of occurrence. (a) Real-time catalog, available at http://cnt.rm.ingv.it/ and (b) machine-learning catalog1 are shown for event magnitudes above their respective magnitude of completeness1,2 Mc=2.2 and Mc=0.5 (from Beroza et al., 2021).
News and events
The future of earthquake forecasting

Machine-learning illuminates earthquake activity with unprecedented detail, leading to improved earthquake forecasts.

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