Abstract
A broad spectrum of seabed deformation structures, including pockmarks and domes, related to various processes, have been observed on continental margins worldwide. This study provides the first regional-scale inventory and quantitative characterization of pockmarks and domes potentially linked to subsurface fluid-escape processes on the continental shelf and upper slope in the Gulf of Lions (NW Mediterranean). Using high-resolution multibeam bathymetry and seismic reflection data, approximately 29,000 pockmarks and domes were identified. Morphometric and seismic analyses reveal distinct groups that differ in size, shape, and spatial organization. Among them a widespread population of domes rooted on the Holocene maximum flooding surface, which represent ~ 85% of the occurrences. The remaining 15% are pockmarks, divided into five main subtypes. Spatial association of domes and pockmarks suggests a genetic relationship between dome formation and subsequent collapse (pockmarks). Machine learning–based spatial modeling extends the estimated total to ~ 80,000 between 10 and 1000 m water depth, including zones not covered by swath-bathymetric surveys. Among various controlling factors, sediment thickness, water depth, and grain-size variability exert primary controls on the distribution of pockmarks and domes. These results demonstrate that seabed deformations in the Gulf of Lions are spatially organized, reflecting the interplay between fluid-related processes, stratigraphic architecture, and sediment properties.
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Data availability
Geophysical data: The raw public data, acquired by IFREMER vessels, are openly available on the SISMER website at [https://data.ifremer.fr/#/home]. Other raw geophysical data that support the findings of this study are available upon request to MAB with the permission of third party owners (see Supplementary Table 1). The surface sedimentological data that support the findings of this study are openly available on SEANOE website at [https://doi.org/10.17882/81430]. The interpretations that support the findings of this study are available from the corresponding author, AL, upon reasonable request.
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Acknowledgements
We thank OFB (notably Grégory Agin), IFREMER (Gwénaël Jouet), Shom (Yann Le Faou), RTE (Jules Lacombe), EDF (Thierry Denois), DGEC (Guillaume Poirier) and Engie (Samuel Lemière) for providing access to data, as well as Genavir and Technoambiente teams for data acquisition. Additional seismic data were acquired by the DEM’EAUX project, funded by the French Government, Région Occitanie, Agence de l’Eau, Perpignan Métropôle and département des Pyrénées Orientales. We are grateful to Marina Rabineau and Estelle Leroux for access to the AMMED cruise dataset, and to Olivier Raynal and Bertil Hébert (CEFREM) for their participation in data acquisition. We also thank Lies Loncke (CEFREM) for valuable suggestions.
Funding
This research did receive funding. André Lion received funding from French Ministry of Higher Education, Research and Space. André Lion received funding from Office Français de la Biodiversité.
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AL contributed to conceptualization, data analysis, interpretation and writing. SB contributed to conceptualization, data acquisition and analysis, interpretation and writing. MAB contributed to conceptualization, interpretation and writing. RJ contributed to conceptualization, interpretation and writing.
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Lion, A., Bassetti, MA., Berné, S. et al. Understanding tens of thousands of pockmarks and domes using machine learning (Gulf of Lions, NW Mediterranean Sea). Sci Rep (2026). https://doi.org/10.1038/s41598-026-42740-4
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DOI: https://doi.org/10.1038/s41598-026-42740-4
