FaciesMamba: Leveraging Sequence Modeling for Parameter-Efficient Facies Classification

Deep learning has emerged as a powerful tool for seismic interpretation. However, many vision models are significantly overparameterized, often relying on deep convolutional hierarchies with millions of parameters to capture spatial patterns. This results in high computational costs and increased data requirements. In this work, we introduce FaciesMamba, a compact and efficient encoder-decoder architecture for seismic facies classification that replaces deep convolutional stacks with trace-wise sequence modeling using Mamba, a structured state space model. To account for local features along the horizontal dimension, we incorporate lightweight convolutional layers. The architecture preserves the vertical (depth) dimension while progressively downsampling only along the horizontal axis to improve efficiency and reduce spatial redundancy. A gated fusion mechanism enables learnable feature integration between encoder and decoder stages. Despite having only 2.8 million parameters, our model achieves better than state-of-the-art performance. Although the model is not the most FLOP-efficient, it benefits from substantial parameter savings and architectural simplicity. Experiments on the Netherlands F3 facies classification benchmark demonstrate that efficient sequence-based modeling can outperform much larger models. These results establish FaciesMamba as a practical, lightweight alternative for seismic facies interpretation, especially in resource-constrained scenarios.