Distance Transform Loss: Boundary-aware segmentation of seismic data

The segmentation of seismic data is a challenging exercise given the complexity and high variability of subsurface sources. This arduous task is effective in the identification of geological features, including facies classification, fault detection, and horizon interpretation. As a result, this work introduces a new cost function entitled Distance Transform Loss (DTL) that punishes deep networks when class boundaries are misclassified in exchange for more accurate contour delineations, an important aspect in the geological field. DTL consists of four key steps: contour detection, distance transform mapping, pixel-wise multiplication, and the summation of all grid elements. We conduct a comprehensive evaluation of deep convolutional architectures using publicly available seismic datasets, demonstrating that the proposed approach consistently enhances semantic segmentation performance. The results highlight DTL as a robust and architecture-agnostic loss function, capable of addressing class imbalance and boundary delineation challenges that commonly arise in seismic interpretation tasks.