vpuzyrev/geolgan
Geophysical model generation with generative adversarial networks GANs
This tool generates diverse and detailed 2D synthetic subsurface geological models, including density and stratigraphy. It takes a pre-trained generative adversarial network (GAN) as input and outputs a range of artificial geological models. This is ideal for geophysicists, geologists, or researchers who need large, varied datasets to train deep learning models for subsurface parameter estimation.
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Use this if you need to quickly and cost-effectively generate hundreds of thousands or millions of realistic synthetic geophysical models for training machine learning algorithms.
Not ideal if you are looking for tools to directly analyze real-world geophysical measurements or generate 3D models.
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Language
Python
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Last pushed
May 25, 2021
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