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Found 4 papers, 1 papers with code
Revisit Geophysical Imaging in A New View of Physics-informed Generative Adversarial Learning
Seismic full waveform inversion (FWI) is a powerful geophysical imaging technique that produces high-resolution subsurface models by iteratively minimizing the misfit between the simulated and observed seismograms.
Quantitative reconstruction of defects in multi-layered bonded composites using fully convolutional network-based ultrasonic inversion
In the training process of this method, an FCN establishes a non-linear mapping from measured ultrasonic data to the corresponding velocity models of multi-layered bonded composites.
Robust Phase Unwrapping via Deep Image Prior for Quantitative Phase Imaging
Our work paves the way to reliable phase imaging of thick and complex samples with QPI.
Deep-learning inversion: a next generation seismic velocity-model building method
Seismic velocity is one of the most important parameters used in seismic exploration.
