Testing a machine learning approach to geophysical inversion

A common problem in the geosciences is the need to deduce unseen physical structure based on limited observations. For instance, a ground-penetrating radar observation attempts to infer underground structure without any in situ measurements. This class of problems is called inversion, in which an assumed physical model is repeatedly adjusted until it is consistent with observations.


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Source: Phys.org