Segmentation applications in scientific imaging domains such as seismic interpretation require large quantities of labeled data annotated by expert interpreters, which is a costly and time-consuming process. Where existing works to minimize dependence on labeled data assume the data annotation process to already be completed, active learning—a field of machine learning—works by selecting the most important training samples for the interpreter to annotate in real time simultaneously with the training of the interpretation model itself, thereby reducing cost and effort to produce annotated training samples while minimizing the negative impact on performance. We develop a unique and first-of-a-kind active learning framework for seismic facies interpretation using the manifold learning properties of deep autoencoders.

Accurate interpretation of visual data for relevant information forms an important component of many real-world applications such as medical disease diagnosis, geological hazard assessment, hydrocarbon exploration, etc. Producing fine-grained annotations on computational images is an expensive, laborious, and time-consuming process. The human brain is wired to selectively focus its attention on certain aspects of the visual scene. This perception mechanism is driven both by low-level signal cues, such as changes in color, contrast, intensity, shapes etc., as well as high-level cognitive factors such as one’s prior knowledge, goals, expectations, and constraints with respect to the task at hand. By leveraging and modeling knowledge of this attention process into the training of deep neural networks, we demonstrate the network to perform significantly better even with sparse annotations.