Automated machine learning for borehole resistivity measurements. (arXiv:2207.09849v1 [cs.LG])

Deep neural networks (DNNs) offer a real-time solution for the inversion of
borehole resistivity measurements to approximate forward and inverse operators.
It is possible to use extremely large DNNs to approximate the operators, but it
demands a considerable training time. Moreover, evaluating the network after
training also requires a significant amount of memory and processing power. In
addition, we may overfit the model. In this work, we propose a scoring function
that accounts for the accuracy and size of the …

arxiv automated machine learning learning lg machine machine learning