US 11,893,495 B2
Dual neural network architecture for determining epistemic and aleatoric uncertainties
Ravinath Kausik Kadayam Viswanathan, Sharon, MA (US); Lalitha Venkataramanan, Lexington, MA (US); and Augustin Prado, Lausanne (CH)
Assigned to SCHLUMBERGER TECHNOLOGY CORPORATION, Sugar Land, TX (US)
Filed by Schlumberger Technology Corporation, Sugar Land, TX (US)
Filed on Sep. 8, 2020, as Appl. No. 16/948,183.
Claims priority of provisional application 62/896,339, filed on Sep. 5, 2019.
Prior Publication US 2021/0073631 A1, Mar. 11, 2021
Int. Cl. G06N 3/08 (2023.01); G06N 5/04 (2023.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01); G06N 3/084 (2023.01); G06F 18/214 (2023.01); G06F 18/2415 (2023.01)
CPC G06N 3/084 (2013.01) [G06F 18/2155 (2023.01); G06F 18/24155 (2023.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01); G06N 3/08 (2013.01); G06N 5/04 (2013.01)] 24 Claims
OG exemplary drawing
 
1. A neural network system comprising:
a first neural network configured to predict a mean value output and epistemic uncertainty of the output given input data;
a second neural network configured 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
one or more processors configured to run a software module that determines 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.