Patent ID: 11893495
Assignee: SCHLUMBERGER TECHNOLOGY CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 9:
10. A machine learning method comprising:
in a training phase, training a first neural network to predict an output and epistemic uncertainty of the output given input data, and training a second neural network to predict total uncertainty of the output of the first neural network, wherein the second neural network is trained to predict the total uncertainty of the output of the first neural network given the input data through a training process involving minimizing a cost function that involves differences between a predicted mean value of a geophysical property of a geological formation from the first neural network and a ground-truth value of the geophysical property of the geological formation; and
in an inference phase, supplying input data to the trained first neural network to predict an output and epistemic uncertainty of the output given the input data, using the trained second neural network to predict total uncertainty of the output of the trained first neural network, and determining aleatoric uncertainty of the output of the first neural network based on the epistemic uncertainty of the output and the total uncertainty of the output.