首页> 外国专利> GENERATING GEOLOGICAL FACIES MODELS WITH FIDELITY TO THE DIVERSITY AND STATISTICS OF TRAINING IMAGES USING IMPROVED GENERATIVE ADVERSARIAL NETWORKS

GENERATING GEOLOGICAL FACIES MODELS WITH FIDELITY TO THE DIVERSITY AND STATISTICS OF TRAINING IMAGES USING IMPROVED GENERATIVE ADVERSARIAL NETWORKS

机译:利用改进的生成对抗性网络生成了对训练图像的多样性和统计的生成地质相模型

摘要

Neural network systems and related machine learning methods for geological modeling are provided that employ an improved generative adversarial network including a generator neural network and a discriminator neural network. The generator neural network is trained to map a combination of a noise vector and a category code vector as input to a simulated image of geological facies. 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.
机译:提供了用于地质模型的神经网络系统和相关机器学习方法,其采用一种改进的生成的对抗网络,包括发电机神经网络和鉴别者神经网络。发电机神经网络训练以映射噪声矢量和类别代码向量的组合作为地质相的模拟图像的输入。鉴定鉴别器神经网络训练以映射作为输入的输入的至少一个地质相的图像,即作为输入提供的地质相的至少一个图像是地质相的训练图像或由此产生的地质相的模拟图像发电机神经网络。

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