Deep Learning as a Method for Inversion of NMR Signals
Julian B. B. Beckmann Mick D. Mantle Andrew J. Sederman Lynn F. Gladden
The concept of deep learning is employed for the inversion of NMR signals and
it is shown that NMR signal inversion can be considered as an image-to-image
regression problem, which can be treated with a convolutional neural net. It is
further outlined, that inversion through deep learning provides a clear
efficiency and usability advantage compared to regularization techniques such
as Tikhonov and modified total generalized variation (MTGV), because no
hyperparemeter selection prior to reconstruction is necessary. The inversion
network is applied to simulated NMR signals and the results compared with
Tikhonov- and MTGV-regularization. The comparison shows that inversion via deep
learning is significantly faster than the latter regularization methods and
also outperforms both regularization techniques in nearly all instances.