Spatially Constrained Self-Organizing Maps for Seismic Facies Analysis

Abstract

Understanding and characterizing seismic facies is essential for reconstructing depositional settings, identifying stratigraphic boundaries, and supporting reservoir evaluation in both exploration and development contexts. In this study, we present a spatially constrained self-organizing map (SOM) framework for seismic facies analysis that integrates multi-attribute information while incorporating local spatial consistency during training. Unlike conventional SOM approaches that rely solely on attribute similarity, the proposed method introduces a spatial regularization term into the best matching unit (BMU) assignment, promoting consistent mapping among neighboring samples while preserving discontinuities associated with structural or stratigraphic boundaries. The spatial constraint is implemented through a local weighting function that reflects structural continuity; in this implementation, seismic coherence is used as an indicator, although the formulation is not restricted to a specific attribute. Following SOM training, the attribute manifold is further represented using a geodesic-based one-dimensional ordering that captures intrinsic relationships among SOM nodes, providing a continuous representation of facies variability and improving interpretability. Application to a deep-water 3D seismic dataset demonstrates that the proposed framework reduces local instability in facies mapping, enhances spatial continuity, and preserves boundary sharpness compared with conventional SOM-based workflows.