Calderas Beneath the Waves: AI-Powered Exploration of Subaqueous Volcanism

Submarine calderas remain some of the least explored volcanic systems on the planet, even though the recent Hunga Tonga-Hunga Ha’apai event has demonstrated their capacity to generate significant geohazards, including tsunamis, damage to seafloor infrastructure, and atmospheric disturbances. Their global identification has long been limited by sparse bathymetric coverage and operational constraints. In this work, we apply a machine‑learning caldera detection algorithm (CDA) to global bathymetric datasets, enabling a systematic search for previously unrecognised submarine calderas. We identify 78 potential calderas spanning a broad range of water depths (down to 5,600 m), diameters (up to 20 km), and tectonic environments (divergent, convergent, and intraplate). Among these, eight shallow‑water calderas, mostly located in volcanic arcs, were highlighted as high‑priority targets due to their elevated hazard potential. This new global dataset addresses a major observational gap and provides a reproducible, extensible framework for submarine volcano characterisation, hazard evaluation, and deep‑sea exploration. The results emphasise the importance of incorporating submarine calderas into future global hazard models and monitoring strategies.