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Real-time hydrogen-storage monitoring via energy-efficient deep learning

CORDIS · observation · Publication date unknown

Real-time hydrogen-storage monitoring via energy-efficient deep learning The use of renewable hydrogen as green fuel and energy storage was deemed key to achieve the European Green Deal. However, its large-scale storage is still facing significant challenges. Measurement inversion via deep learning (DL) is a state-of-the-art approach used for underground storage-site detection and monitoring. However: 1) It requires a huge amount of training data; 2) DL training is expensive, and 3) There are no efficient and reliable DL techniques for multiscale electromagnetic measurement inversion. The goal of GEOLEARN is to guide hydrogen storage technologies by inverting subsurface multiscale electromagnetic measurements in real time using energy-efficient DL methods. For this purpose, GEOLEARN will leverage mixed-precision (MP) computations to maximise energy- and cost-efficiency, and ensure scalability. GEOLEARN proposes to address the above challenges a

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recordType

award

status

TERMINATED

region

EU

value

181152.96

unit

EUR