AI识别+环境DNA检测

Search documents
载人+无人 我国双潜器在北极深海实现多个首次
Huan Qiu Wang Zi Xun· 2025-10-04 04:27
Core Insights - The 15th Arctic scientific expedition by China has successfully completed its mission, marking the first underwater collaboration between manned and unmanned submersibles in polar regions [1][2] Group 1: Manned and Unmanned Submersible Collaboration - The "Jiaolong" manned submersible and an unmanned remotely operated vehicle (ROV) conducted the world's first underwater collaborative operation in the Arctic during the 15th Arctic scientific expedition [2][4] - The collaboration aimed to enhance the operational capabilities of the "Jiaolong" submersible, which traditionally operated in a single-point mode, by addressing challenges in communication and positioning [4][6] - The first joint dive on August 14 tested underwater positioning and communication functions, while the second dive on August 15 involved collaborative operations, including video documentation of sampling activities [6][8] Group 2: Innovative Research Techniques - The expedition introduced a novel investigation model combining AI recognition and environmental DNA detection for precise surveys of seabed organisms [9][10] - Thousands of deep-sea biological images collected by the research team supported AI biological recognition, which was further validated by environmental DNA analysis [10][12] - The AI recognition technology revealed significant variations in benthic organism density, biodiversity, and individual morphology across extensive spatial ranges [12] Group 3: Comprehensive Data Collection - The 15th Arctic expedition, involving four vessels, is noted as the largest Arctic scientific investigation conducted by China to date, reaching as far north as latitude 77.5° [13][15] - The "Jiaolong" submersible collected a substantial number of rock, sediment, and seawater samples, including 183 biological samples across 12 categories [15][17] - The expedition also deployed multiple underwater imaging observation systems at different depths and time scales, yielding multi-faceted data on the ice edge zone [17][19]