Seismic horizon-tracking strategy integrating local and global constraints

ABSTRACT Seismic horizon-tracking is fundamental for reconstructing subsurface geological history, yet traditional methods often struggle with complex geological features such as faults and unconformities. To address this challenge, horizon-tracking is reformulated as a three-dimensional scatter point classification problem, balancing local and global tracking. A density-based clustering algorithm (DBSCAN) is applied to group spatially continuous stratigraphic interface points extracted from seismic phase attributes, ensuring accurate local horizon-tracking and generating multiple horizon patches. These patches are then treated as states within a Markov chain, where their connectivity is optimized using an accessibility matrix, enhancing global horizon-tracking accuracy. This framework enables the delineation of multiple stratigraphic interfaces while preserving fine-scale structural details in regions with fault-induced stratigraphic offsets and unconformity-induced stratigraphic terminations. The integration of DBSCAN clustering and Markov modeling with seismic phase attributes allows for an adaptive and scalable approach to horizon-tracking. Benchmark tests on seismic datasets demonstrate the method’s effectiveness in capturing geological complexities and refining subsurface structural interpretation. By improving the continuity of stratigraphic surfaces and accommodating structural discontinuities, this approach provides insights into depositional environments, tectonic activities, and erosional events. The proposed methodology enhances the accuracy and reliability of horizon-tracking, making it a valuable tool for reservoir characterization, seismic interpretation, and subsurface exploration.