  Recent applications of deep learning in the seismic domain have shown great
potential in different areas such as inversion and interpretation. Deep
learning algorithms, in general, require tremendous amounts of labeled data to
train properly. To overcome this issue, we propose a semi-supervised framework
for acoustic impedance inversion based on convolutional and recurrent neural
networks. Specifically, seismic traces and acoustic impedance traces are
modeled as time series. Then, a neural-network-based inversion model comprising
convolutional and recurrent neural layers is used to invert seismic data for
acoustic impedance. The proposed workflow uses well log data to guide the
inversion. In addition, it utilizes a learned seismic forward model 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 elastic impedance using 20 AI traces for training.
