  In exploration seismology, seismic inversion refers to the process of
inferring physical properties of the subsurface from seismic data. Knowledge of
physical properties can prove helpful in identifying key structures in the
subsurface for hydrocarbon exploration. In this work, we propose a workflow for
predicting acoustic impedance (AI) from seismic data using a network
architecture based on Temporal Convolutional Network by posing the problem as
that of sequence modeling. The proposed workflow overcomes some of the problems
that other network architectures usually face, like gradient vanishing in
Recurrent Neural Networks, or overfitting in Convolutional Neural Networks. The
proposed workflow was used to predict AI on Marmousi 2 dataset with an average
$r^{2}$ coefficient of $91\%$ on a hold-out validation set.
