Visual Attention-Guided Learning With Incomplete Labels for Seismic Fault Interpretation

Annotating geological faults on 3-D seismic volumes is a laborious process. Typically, only a fraction of the actual faults are manually interpreted, leaving many others unlabeled. This is due to the way attention selectivity works to drive human perception. The human brain selectively focuses its attention on certain salient regions in a visual scene marked by prominent changes in color, contrast, and other low-level signal cues. This bottom-up attention is further modulated by the individual's goals, expectations, and constraints with respect to the task at hand, also called top-down attention. The fault annotations created by seismic interpreters reflect this cognitive process comprising both bottom-up and top-down attentional mechanisms. The 3-D convolutional neural networks (CNNs) pretrained on synthetic seismic data for fault mapping can be finetuned on select seismic lines extracted and labeled on a real seismic volume of interest. Traditional finetuning approaches treat all pixels on labeled sections as the absolute ground truth. This leads to the network incorrectly learning to predict regions of missing fault labels as negatives. We propose an attention-guided training framework that models and incorporates human visual attention to: 1) condition the process of sampling training data and 2) modulate the loss value for each pixel. Through quantitative and qualitative evaluation of results on a real seismic volume from North Western Australia, we demonstrate that the proposed approach is able to predict both the annotated and the unlabeled faults significantly better compared to baseline approaches.