Processing of hybrid seismic data over the Ancona landslide

This thesis focuses on recovering and enhancing a previously unused marine–land seismic dataset from the Ancona landslide area, Italy, a zone critically important for imaging the transition between offshore and onshore domains. Earlier surveys excluded this dataset due to severe noise contamination, leaving a gap in the seismic interpretation of the landslide’s full geometry. Using the ProMax environment, a tailored workflow combining time- and space-variant filtering, Singular Value Decomposition (SVD), FX deconvolution, and predictive deconvolution was developed to suppress low-frequency Scholte waves, high-frequency electromagnetic noise, and bubble pulse reverberations. The reprocessed stacked section shows significant improvements in resolution and continuity, allowing clear imaging of the landslide toe and its structural connections offshore. By reintegrating this enhanced segment into the complete seismic profile, the study closes an important interpretational gap and demonstrates that even challenging hybrid datasets can yield valuable results when modern processing techniques are carefully applied. This contributes practical insights for future seismic investigations in complex coastal environments