  Traditional seismic processing workflows (SPW) are expensive, requiring over
a year of human and computational effort. Deep learning (DL) based data-driven
seismic workflows (DSPW) hold the potential to reduce these timelines to a few
minutes. Raw seismic data (terabytes) and required subsurface prediction
(gigabytes) are enormous. This large-scale, spatially irregular time-series
data poses seismic data ingestion (SDI) as an unconventional yet fundamental
problem in DSPW. Current DL research is limited to small-scale simplified
synthetic datasets as they treat seismic data like images and process them with
convolution networks. Real seismic data, however, is at least 5D. Applying 5D
convolutions to this scale is computationally prohibitive. Moreover, raw
seismic data is highly unstructured and hence inherently non-image like. We
propose a fundamental shift to move away from convolutions and introduce SESDI:
Set Embedding based SDI approach. SESDI first breaks down the mammoth task of
large-scale prediction into an efficient compact auxiliary task. SESDI
gracefully incorporates irregularities in data with its novel model
architecture. We believe SESDI is the first successful demonstration of
end-to-end learning on real seismic data. SESDI achieves SSIM of over 0.8 on
velocity inversion task on real proprietary data from the Gulf of Mexico and
outperforms the state-of-the-art U-Net model on synthetic datasets.
