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May 28 2025
6Offshore wind farms, oil rigs, and other marine energy infrastructures rely on extensive networks of underwater components, such as pipelines, risers, anchors, and cables. These vital structures, while submerged far beneath the ocean's surface, are not immune to the forces of nature. Among the most destructive of these are submarine landslides, which pose a significant risk to the integrity and function of subsea installations.
Researchers at Texas A&M University have now developed a new method to anticipate these underwater landslides with greater precision, using data collected during early-stage site investigations. Their findings have been published in the journal Landslides.
“Landslides are a major hazard to both onshore and offshore facilities. They can devastate entire installations,” said Zenon Medina-Cetina, associate professor in the Department of Civil & Environmental Engineering at Texas A&M. “Our study shows that integrating multidisciplinary information in a specific sequence is key to accurately assessing the likelihood of landslides at any given location and time.”
Before launching offshore operations such as oil extraction or wind energy production, specialists conduct site characterization studies. These investigations involve collecting information on seabed and sub-seabed conditions, along with broader environmental data. This process informs the design and construction of offshore infrastructure and plays a critical role in mitigating geological hazards.
Site characterization is inherently collaborative, involving geophysicists, geomatics experts, geotechnical engineers, and geologists. Medina-Cetina emphasized that the sequence in which these professionals contribute is just as crucial as their individual roles. A disrupted or incorrect order—often due to time or budget constraints—can introduce uncertainties into the final prediction models.
“To get reliable results, you need to start with geophysicists, then involve geologists, followed by geomatics specialists working closely with geotechnical engineers,” said Medina-Cetina. “It’s like trying to teach a baby to run before they’ve learned to walk. When data is collected and integrated in the correct order, our models become more accurate and informative.”
The Texas A&M team’s approach relies on Bayesian statistical methods to refine model predictions using available data. By calibrating landslide prediction models with properly sequenced site information, the team enhances both the accuracy and reliability of forecasts—critical factors for companies investing in expensive offshore projects.
“When the risk of geohazards is unclear, companies are less confident in their designs, often resulting in financial losses,” added Medina-Cetina. “Our goal is to ensure that under any hazardous conditions, these offshore structures will stay safe and stable where they were originally intended to be.”
The research was carried out in collaboration with Patricia Varela of Geosyntec Consultants, Inc., and Billy Hernawan, a student in Texas A&M’s civil and environmental engineering program.
Source: https://phys.org/news/2025-05-underwater-landslides.html
This is non-financial/medical advice and made using AI so could be wrong.
