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Deep Learning for Seismic Data Processing and Interpretation. (Applications de l'Apprentissage Profond pour le Traitement et l'Interprétation Sismique)

Gaining knowledge of the geology of the Earth’s subsurface with seismic reflection is a long and challenging process. Many algorithms are employed to transform the signal, attenuate the noise and help interpreting the image. Those algorithms are designed by experts and require to be carefully parametrized. Additionally, many tasks have to be performed manually by geoscientists when algorithms fail to deliver good results. In recent years, deep learning, a subfield of artificial intelligence, has rose to prominence. Learning models have been shown to outperform traditional algorithms in many applications across numerous scientific disciplines. They also allow to automate certain processes that were until then only feasible by humans. However, fulfilling the necessary conditions to exploit their potential may be challenging. In this thesis, we identify the main impediments to the use of deep learning, in particular the uncertainties in the interpretation of the data and the dependency of the training procedure on examples supplied by experts, and propose a series of methodologies that aim to overcome them. We demonstrate the validity and practicability of our methods on a set of challenging seismic interpretation and processing problems.