  Nowadays, subsurface salt body localization and delineation, also called
semantic segmentation of salt bodies, are among the most challenging
geophysicist tasks. Thus, identifying large salt bodies is notoriously tricky
and is crucial for identifying hydrocarbon reservoirs and drill path planning.
This work proposes a Data Augmentation method based on training two generative
models to augment the number of samples in a seismic image dataset for the
semantic segmentation of salt bodies. Our method uses deep learning models to
generate pairs of seismic image patches and their respective salt masks for the
Data Augmentation. The first model is a Variational Autoencoder and is
responsible for generating patches of salt body masks. The second is a
Conditional Normalizing Flow model, which receives the generated masks as
inputs and generates the associated seismic image patches. We evaluate the
proposed method by comparing the performance of ten distinct state-of-the-art
models for semantic segmentation, trained with and without the generated
augmentations, in a dataset from two synthetic seismic images. The proposed
methodology yields an average improvement of 8.57% in the IoU metric across all
compared models. The best result is achieved by a DeeplabV3+ model variant,
which presents an IoU score of 95.17% when trained with our augmentations.
Additionally, our proposal outperformed six selected data augmentation methods,
and the most significant improvement in the comparison, of 9.77%, is achieved
by composing our DA with augmentations from an elastic transformation. At last,
we show that the proposed method is adaptable for a larger context size by
achieving results comparable to the obtained on the smaller context size.
