Figure | Principle of physics-informed generative network (PIGN). (IMAGE)
Caption
Figure | Principle of physics-informed generative network (PIGN). a Schematic of feature functions acquisition for DAS event data. After projecting DAS event data onto different dimensions, and combining physical models with professional experience, the corresponding feature functions of the events in the respective dimensions can be obtained. Feature curves of an individual event can then be derived from these feature functions. b Network structure and training process of PIGN. Network is based on the U-Net architecture. During training, the loss function is defined by assessing the gap between the feature curves of the output image and the target feature curves, while also incorporating constraints from the DAS system and real-world conditions.
Credit
Yangyang Wan et al.
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CC BY