Encoder-Decoder Architecture for 3D Seismic Inversion
Abstract
Inverting seismic data to build 3D geological structures is a challenging task due to the overwhelming amount of acquired seismic data, and the very-high computational 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 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 learning solution for the reconstruction of realistic 3D models in the presence of field noise recorded in seismic surveys. We implement and analyze a convolutional encoder-decoder architecture that efficiently processes the entire collection of hundreds of seismic shot-gather cubes. The proposed solution demonstrates that realistic 3D models can be reconstructed with a structural similarity index measure (SSIM) of 0.8554 (out of 1.0) in the presence of field noise at 10dB signal-to-noise ratio.
Cite
@article{arxiv.2207.14789,
title = {Encoder-Decoder Architecture for 3D Seismic Inversion},
author = {Maayan Gelboim and Amir Adler and Yen Sun and Mauricio Araya-Polo},
journal= {arXiv preprint arXiv:2207.14789},
year = {2022}
}