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Tomography: a Deep Learning vs Full-Waveform Inversion Comparison
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, First EAGE Workshop on High Performance Computing for Upstream in Latin America, Sep 2018, Volume 2018, p.1 - 5
Abstract
Summary
We explore the feasibility of a deep learning approach for tomography by comparing it with the current velocity prediction techniques used in the industry. This is accomplished through quantitative and qualitative comparisons of velocity models predicted by a Machine Learning (ML) system and those of two variations of full-waveform inversion (FWI). Additionally, we compare the computational aspects of the two approaches. The results show that the ML-based reconstructed models are competitive to the FWI-produced models in terms selected metrics, and widely less expensive to compute.
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