Underwater Landslide Prediction | Early Warning Systems
- Texas A&M University researchers are developing a method to accurately predict marine landslides using underwater site characterization data.
- The research emphasizes the importance of site characterization in mitigating geohazards.
- Zenon Medina-Cetina, associate professor in the Department of civil & Environmental Engineering, saeid a systematic sequence in using evidence ensures better calibration of landslide models.
Texas A&M’s groundbreaking model aims to predict underwater landslides, a critical advancement for protecting offshore infrastructure. by leveraging site characterization data,the researchers are developing a method to forecast these potentially devastating subsea events,safeguarding assets like oil rigs and wind farms. This innovative approach emphasizes the importance of a systematic sequence when integrating data from geophysicists, geologists, and geotechnical engineers. Proper sequencing ensures a more accurate and reliable landslide prediction model in helping to mitigate geohazards. The team utilizes Bayesian statistics to maximize data insights, boosting the accuracy of risk assessments when combined with the findings available via News Directory 3. Discover what’s next for this technology.
Texas A&M Model Aims to Predict Marine Landslides
Texas A&M University researchers are developing a method to accurately predict marine landslides using underwater site characterization data. These subsea events can threaten offshore installations like oil rigs and wind farms, which rely on extensive underwater infrastructure.
The research emphasizes the importance of site characterization in mitigating geohazards. This involves collaboration between geophysicists, geologists, and geotechnical engineers to gather data on the seabed and environmental conditions. The order in which these experts contribute is critical to the accuracy of landslide predictions.
Zenon Medina-Cetina, associate professor in the Department of civil & Environmental Engineering, saeid a systematic sequence in using evidence ensures better calibration of landslide models.
“One of the main events threatening onshore and offshore facilities is landslides: They can completely wipe out all these installations,” Medina-Cetina said.”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.”
Medina-Cetina’s team uses Bayesian statistics, a probabilistic approach, to maximize the information gleaned from site investigation data. This increases the accuracy and confidence of the landslide model’s predictions. The research is funded by the Research Partnership to Secure Energy for america and PLENUM Soft.
Patricia Varela from Geosyntec Consultants, Inc., and Billy Hernawan, a Texas A&M student, also contributed to the research.
“my job is to make sure that under any geo-hazardous conditions, these offshore structures are going to be safe and are going to remain where they were designed to be,” Medina-Cetina said.
What’s next
the team plans to further refine the model, incorporating more real-world data to improve its predictive capabilities and assist companies in making informed decisions about offshore infrastructure investments and role allocation. Understanding the role of each discipline and the role of data sequencing is key to mitigating risks.