The maze of oil rigs, pipelines, cables and other offshore installations are vulnerable to submarine landslides. The ability to predict the occurrence of these threats improves with a modeling system developed by researchers from Geosyntec Consultants Inc. (Texas) and Texas A&M University.

To tackle the challenges posed by underwater landslides and protect subsea installations, a modeling scheme based on underwater site characterization data was devised. The model calibration methodology described in the journal Landslides uses a probabilistic approach — Bayesian statistics — to maximize the information produced in site investigation data. This approach increases the accuracy and confidence of the landslide model when it makes predictions.

The site characterization data collected before offshore projects are launched help to mitigate potential geohazards and strengthen the design and construction of offshore structures. A systematic approach is applied to produce prior probability distributions of the model parameters based on an actual integrated marine site investigation including geological, geophysical and geomatics data. These are then compared with a posterior probability distribution of the same model parameters but estimated after collecting in situ soil samples and testing them in the laboratory to define the corresponding soil strength properties.

This comparison allows for consideration of the influence of the number of in situ samples, a landslide factor of safety and the soil heterogeneity on the potential occurrence of a marine landslide. The model parameters that are considered for calibration include the initial state of the submerged and saturated soil unit weight, the thickness of the soils’ unit layers, the pseudo-static seismic coefficient and the slope angle. The soil undrained shear strength is considered as the reference parameter to conduct the calibration.