  Earth structural heterogeneities have a remarkable role in the petroleum
economy for both exploration and production projects. Automatic detection of
detailed structural heterogeneities is challenging when considering modern
machine learning techniques like deep neural networks. Typically, these
techniques can be an excellent tool for assisted interpretation of such
heterogeneities, but it heavily depends on the amount of data to be trained.
  We propose an efficient and cost-effective architecture for detecting seismic
structural heterogeneities using Convolutional Neural Networks (CNNs) combined
with Attention layers. The attention mechanism reduces costs and enhances
accuracy, even in cases with relatively noisy data. Our model has half the
parameters compared to the state-of-the-art, and it outperforms previous
methods in terms of Intersection over Union (IoU) by 0.6% and precision by
0.4%. By leveraging synthetic data, we apply transfer learning to train and
fine-tune the model, addressing the challenge of limited annotated data
availability.
