  Seismic inversion refers to the process of estimating reservoir rock
properties from seismic reflection data. Conventional and machine
learning-based inversion workflows usually work in a trace-by-trace fashion on
seismic data, utilizing little to no information from the spatial structure of
seismic images. We propose a deep learning-based seismic inversion workflow
that models each seismic trace not only temporally but also spatially. This
utilizes information-relatedness in seismic traces in depth and spatial
directions to make efficient rock property estimations. We empirically compare
our proposed workflow with some other sequence modeling-based neural networks
that model seismic data only temporally. Our results on the SEAM dataset
demonstrate that, compared to the other architectures used in the study, the
proposed workflow is able to achieve the best performance, with an average
$r^{2}$ coefficient of 79.77\%.
