  Seismic inversion helps geophysicists build accurate reservoir models for
exploration and production purposes. Deep learning-based seismic inversion
works by training a neural network to learn a mapping from seismic data to rock
properties using well log data as the labels. However, well logs are often very
limited in number due to the high cost of drilling wells. Machine learning
models can suffer overfitting and poor generalization if trained on limited
data. In such cases, well log data from other surveys can provide much needed
useful information for better generalization. We propose a learning scheme
where we simultaneously train two network architectures, each on a different
dataset. By placing a soft constraint on the weight similarity between the two
networks, we make them learn from each other where useful for better
generalization performance on their respective datasets. Using less than 3$\%$
of the available training data, we were able to achieve an average $r^{2}$
coefficient of 0.8399 on the acoustic impedance pseudologs of the SEAM dataset
via joint learning with the Marmousi dataset.
