1887

Abstract

Summary

Traditional physics-based approaches to infer sub-surface properties such as full-waveform 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 net 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.

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/content/papers/10.3997/2214-4609.201800734
2018-06-11
2020-04-02
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