Geological feature detection using generative adversarial neural networks
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United States of America
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Apr 1, 2025
Grant Date -
Nov 3, 2022
app pub date -
May 29, 2020
filing date -
May 29, 2020
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Abstract
Seismic image data acquired for a subsurface formation from a data acquisition system is input into a deep neural network to generate fault detection data for the subsurface formation comprising probability values at a grid of locations in the subsurface formation. The fault detection data is preprocessed via downsampling and distributed weighted factors and inputted into a generative adversarial network (GAN) upscaling generator to create high resolution fault detection data with minimized distortion and artifacts. The GAN upscaling generator is pre trained on synthetic fault data in a GAN training system using adversarial training against a GAN upscaling discriminator, and both the GAN upscaling generator and the GAN upscaling discriminator learn to approximate the distribution of the synthetic fault data.
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