研究業績

Seafloor hydrothermal activity provides significant mineral resources for various metals and serves as an important medium for elucidating the origin of life and material cycling between the ocean and solid Earth. Previous articles have demonstrated that multibeam echo sounders (MBES) are effective for exploring undiscovered hydrothermal activity. However, conventional methods require human observers to continuously monitor MBES data converted to images, often for extended periods overnight. We have previously proposed an automated observation of MBES images using object detection, a type of deep-learning technique. In this article, we conducted an MBES survey at Higashi–Aogashima Knoll Caldera near the Izu–Bonin arc in the western North Pacific Ocean and applied this AI-based method for practical exploration. From approximately 25 000 images collected over about 25 h of surveying, the model identified the prominent area potentially associated with hydrothermal activity. Notably, in one of the prominent areas, a new occurrence of hydrothermal vent was confirmed through a subsequent remotely operated vehicle survey.