Traditional physics-based approaches to infer sub-surface properties such as fullwaveform inversion or reflectivity inversion are time consuming and computationally expensive. We present a deep-learning technique that eliminates the need for these computationally complex methods by posing the problem as one of domain transfer. Our solution is based on a deep convolutional generative adversarial network and dramatically reduces computation time. Training based on two different types of synthetic data produced a neural network that generates realistic velocity models when applied to a real data set. The system's ability to generalize means it is robust against the inherent occurrence of velocity errors and artifacts in both training and test datasets.
We've all heard a proselytizing hyperbolist make the artificial-intelligence-is-going-to-steal-my-job speech. If you subscribe, look at the code in the notebook accompanying this tutorial at https://github.com/seg/tutorials-2018 . It demonstrates a small neural network. You'll find a simple system composed chiefly of multiply and add operations. That's really all that happens inside a neural network. Multiply and add. There's no magic here.
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