Submarine landslides can disrupt the operations of offshore installations, posing a significant threat to productivity and safety. However, researchers at Texas A&M University have made a groundbreaking advancement in accurately predicting the occurrence of marine landslides using underwater site characterization data.

According to Zenon Medina-Cetina, an associate professor in the Department of Civil & Environmental Engineering, the sequencing of tasks performed by personnel with diverse expertise is crucial in understanding the underwater terrain. Failure to follow a systematic order could lead to uncertainties in landslide predictions.

Medina-Cetina emphasized the importance of starting with geophysical surveys, followed by geological analysis, and integrating data from geomatics and geotechnical engineers. This structured approach ensures that landslide models are well-calibrated, enhancing the accuracy of predictions.

Offshore companies investing in subsea projects often face financial losses when uncertainties exist regarding the resilience of civil infrastructures to geohazards. To address this challenge, Medina-Cetina and his team developed a model calibration methodology using Bayesian statistics. This probabilistic approach maximizes the information derived from site investigation data, improving the reliability of landslide predictions.

The researchers introduced a systematic method to establish prior probability distributions of model parameters based on integrated marine site investigation data. By comparing these distributions with posterior probabilities derived from in situ soil testing, they assess the likelihood of marine landslide occurrences.

Key parameters for calibration include soil unit weight, layer thickness, seismic coefficients, and slope angles. The undrained shear strength of soil serves as a reference parameter for calibration, enabling a comprehensive analysis of soil properties influencing landslide risks.

The findings of this research have been published in the journal Landslides, showcasing the effectiveness of the proposed model calibration methodology in enhancing the accuracy and confidence of marine landslide predictions.