First Pockmark susceptibility map of the Italian continental margins

Daniele Spatola*, A. Dahal, L. Lombardo, Daniele Casalbore, Francesco Latino Chiocci

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
11 Downloads (Pure)

Abstract

Scientific advancements often emerge from pivotal discoveries and technological breakthroughs, expanding the frontiers of exploration. In geoscience, natural hazard studies have predominantly focused on terrestrial environments, while submarine settings remain relatively unexplored due to the scarcity of high-resolution data, particularly in deep-sea regions. In recent years, explainable machine and deep learning methodologies have shown significant promise in geohazard prediction, enhancing both predictive reliability and process understanding. A key submarine geohazard is linked to fluid flow processes, which influences seabed morphology and geological processes. Pockmarks (seafloor depressions) formed by fluid flow are widespread across diverse geodynamic settings but remain enigmatic in terms of formation mechanisms. This study examines 5932 pockmarks mapped along the Italian continental margins, primarily on gently sloping, muddy sand seafloor with Plio-Quaternary sediment layers up to 400 m thick. Stylistically, pockmark distribution appears to be closely linked to fault systems. By integrating field observations with machine and deep learning techniques, we developed a neural network-based pockmark susceptibility model, the first of its kind for the Italian continental margins. Susceptibility maps, widely used in geohazard assessments, differentiate between high- and low-risk areas based on past occurrences and predictive modelling. This tool has significant applications in the planning of submarine and floating infrastructure, navigation safety, and environmental studies related to fluid seepage, climate change, and marine biodiversity. Our findings contribute to a better understanding of submarine geohazards and demonstrate the potential of artificial intelligence in improving geoscientific assessments.

Original languageEnglish
Article number107337
Number of pages14
JournalMarine and petroleum geology
Volume176
Early online date19 Feb 2025
DOIs
Publication statusE-pub ahead of print/First online - 19 Feb 2025

Keywords

  • Bathymetry
  • Fluid flow
  • Italian continental margin
  • Pockmarks
  • Susceptibility map
  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-HYBRID

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