Submarine landslides(Underwater Landslides) are dangerous geological events that involve the sudden movement of large sediment masses on the ocean floor. They can damage pipelines, anchors, and cables, jeopardizing offshore structures, and may even trigger tsunamis.
Underneath ocean wind farms and oil rigs, there are large networks of underwater structures like pipelines, anchors, risers, and cables that help collect energy. However, just like buildings on land, these underwater structures can be affected by natural events such as underwater landslides, which can reduce the productivity of these installations.
Researchers at Texas A&M are now able to accurately predict marine landslides using underwater site characterization data.
“One of the main events threatening onshore and offshore facilities is landslides: They can completely wipe out all these installations,” said Zenon Medina-Cetina, associate professor in the Department of Civil & Environmental Engineering. “We show in our paper that information from multiple disciplines in the correct sequence is needed to better understand the probability of landslide development at any place and time.”
Bayesian Model
The Texas A&M team, under the guidance of Professor Zenon Medina-Cetina from the civil and environmental engineering department, developed a model aimed at predicting landslide risk. This innovative model integrates geological data with statistical techniques and employs a Bayesian approach, enabling continuous refinement of predictions through the incorporation of new data.
The Bayesian model comes from the statistical property that uses Bayes Therom. Bayesian models allow for the incorporation of prior knowledge, which can improve the efficiency and accuracy of the model.
RELATED STORIES
https://www.sciencedaily.com/releases/2025/05/250530123805.htm
https://engineering.tamu.edu/news/2025/05/predicting-underwater-landslides-before-they-strike.html
https://www.enn.com/articles/76574-predicting-underwater-landslides-before-they-strike
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https://engineering.tamu.edu/news/2025/05/predicting-underwater-landslides-before-they-strike.html
https://engineering.tamu.edu/civil/index.html
https://link.springer.com/article/10.1007/s10346-025-02486-y