  Recent applications of machine learning algorithms in the seismic domain have
shown great potential in different areas such as seismic inversion and
interpretation. However, such algorithms rarely enforce geophysical constraints
- the lack of which might lead to undesirable results. To overcome this issue,
we have developed a semi-supervised sequence modeling framework based on
recurrent neural networks for elastic impedance inversion from multi-angle
seismic data. Specifically, seismic traces and elastic impedance (EI) traces
are modeled as a time series. Then, a neural-network-based inversion model
comprising convolutional and recurrent neural layers is used to invert seismic
data for EI. The proposed workflow uses well-log data to guide the inversion.
In addition, it uses seismic forward modeling to regularize the training and to
serve as a geophysical constraint for the inversion. The proposed workflow
achieves an average correlation of 98% between the estimated and target EI
using 10 well logs for training on a synthetic data set.
