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Inverting seismic data to build 3D geological structures is a challengingtask due to the overwhelming amount of acquired seismic data, and the very-highcomputational load due to iterative numerical solutions of the wave equation,as required by industry-standard tools such as Full Waveform Inversion (FWI).For example, in an area with surface dimensions of 4.5km $\times$ 4.5km,hundreds of seismic shot-gather cubes are required for 3D model reconstruction,leading to Terabytes of recorded data. This paper presents a deep learningsolution for the reconstruction of realistic 3D models in the presence of fieldnoise recorded in seismic surveys. We implement and analyze a convolutionalencoder-decoder architecture that efficiently processes the entire collectionof hundreds of seismic shot-gather cubes. The proposed solution demonstratesthat realistic 3D models can be reconstructed with a structural similarityindex measure (SSIM) of 0.8554 (out of 1.0) in the presence of field noise at10dB signal-to-noise ratio.

Encoder-Decoder Architecture for 3D Seismic Inversion