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Found 3 papers, 3 papers with code
70 years of machine learning in geoscience in review
I explore the shift of kriging towards a mainstream machine learning method and the historic application of neural networks in geoscience, following the general trend of machine learning enthusiasm through the decades.
Complex-valued neural networks for machine learning on non-stationary physical data
While it has been shown that phase content can be restored in deep neural networks, we show how including phase information in feature maps improves both training and inference from deterministic physical data.
Including Physics in Deep Learning -- An example from 4D seismic pressure saturation inversion
Approaching these problems can benefit from including prior information from physics models or transforming data to a beneficial domain.
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