Encoder-Decoder Architecture for 3D Seismic Inversion
This addresses the computational bottleneck in seismic inversion for the geophysics industry, though it is an incremental improvement using deep learning on existing data.
The paper tackled the problem of inverting seismic data to build 3D geological structures, which is computationally intensive due to large data volumes and iterative methods like Full Waveform Inversion, and achieved a structural similarity index measure (SSIM) of 0.8554 in the presence of field noise at 10dB signal-to-noise ratio.
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 $\times$ 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 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.