Optimizing Seismic Facies Classification Through Differentiable Network Architecture Search

Seismic facies classification involves assigning geological meaning to seismic amplitudes based on distinct sedimentary facies responses. Various deep learning approaches have been developed for seismic facies classification. Currently, most deep neural networks applied for this task are manually engineered based on domain expertise. However, these human-designed architectures may not be optimal for seismic facies classification. To address this, we introduce differentiable architecture search with partial channel connections (PC-DARTS), enabling automated architecture search instead of manual design. We modify the PC-DARTS search space and propose PC-DARTS for seismic facies classification (PC-DARTS-SFC) to determine architectures tailored for this problem. We apply PC-DARTS-SFC on the Netherlands F3 seismic volume. The results demonstrate the superiority of the architecture discovered by PC-DARTS-SFC over original PC-DARTS, conventional networks, and a previous method. This confirms the potential of leveraging network architecture search (NAS) to find specialized networks surpassing human design for seismic facies classification.