From Subsurface to Seafloor: Understanding Fault Activity Through Pockmark Mapping and Machine Learning-Driven Seismic Attribute Analysis in the NW Sicily Channel

Abstract. This study explores fault activity and fluid migration in the NW Sicily Channel, focusing on the Adventure Plateau, using 2D seismic data, multi-attribute seismic analysis, and machine-learning-based fault detection. We assess the structural controls on fluid escape pathways and pockmark formation by applying seismic attributes such as fault probability, similarity, dip variance, and textural entropy.

Our findings reveal that pockmarks are spatially confined near structural highs, where near-vertical faults cross-cut the Terravecchia Formation and extend to the seafloor. Additionally, polygonal faults – despite hosting gas/fluids – do not contribute to pockmark formation, suggesting that tectonic discontinuities, rather than sediment compaction alone, govern active seepage.

To enhance fault characterization, we introduce a machine-learning-derived Fault Probability Section, providing a quantitative assessment of fault likelihood, surpassing traditional seismic interpretation. Our study proposes that pockmarks serve as proxies for active faulting, offering a new approach for offshore fault characterization. This method holds implications for seismic hazard assessment, hydrocarbon exploration, and geohazard monitoring.