✉news SciencePhysics first seen 2 d ago, last 2 d ago, peak #15
Physics-based machine learning targets seafloor sulfide deposits
Original: Encoding physics to learn marine self-potential: application to seafloor sulfide exploration
Researchers have presented a method that encodes physics directly into a machine learning framework for interpreting marine self-potential data, with the goal of locating seafloor massive sulfide deposits. The approach, published in an Oxford Academic journal, aims to make deep-sea mineral exploration more reliable by combining physical modelling with data-driven learning, a topic of growing interest amid demand for battery metals.
Why now: Growing interest in deep-sea mineral exploration and new AI methods for geophysical surveying.
Oxford Academicseafloor massive sulfide depositsmarine self-potential method
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- Encoding physics to learn marine self-potential: application to seafloor sulfide exploration · Oxford Academic
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