CPC G06T 17/05 (2013.01) [G06N 3/045 (2023.01); G06N 3/088 (2013.01)] | 20 Claims |
1. A geological modeling system comprising:
a generator neural network and a discriminator neural network, wherein at least one of the generator neural network and the discriminator neural network is realized by a processor, wherein, in a training phase, the generator neural network is trained to map a first combination of a first noise vector and a first category code vector as input to a simulated image of geological facies to obtain a trained generator neural network, and wherein the discriminator neural network is trained to map at least one image of geological facies provided as input to corresponding probability that the at least one image of geological facies provided as input is a training image of geological facies or a simulated image of geological facies produced by the generator neural network and to obtain a trained discriminator neural network,
wherein, in an online phase, the trained generator neural network is configured to receive input data comprising a second combination of a second noise vector and a second category code vector, and to output a simulated image of geological facies, wherein the geological facies are defined by a texture, a mineralogy, a grain size, a depositional environment, or a combination thereof, and wherein each of the first and second category code vectors comprises an identification of a type of the depositional environment.
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