Innovative Methods for Detecting, Understanding and Predicting Seabed Level Dynamics

With the unprecedented future increase in offshore wind energy production, we are also facing major challenges related to seabed dynamics. Especially tidal sand waves, large-scale dynamic bed forms, can induce risks for offshore structures such as cables. Estimating their future dynamics and related uncertainties is challenging, especially when data is scarce. For that reason, we are expanding our horizon by including new technologies in our seabed morphology assessments. We have found that remote sensing data can be used for sand wave detection, even at large water depths, offering a passive, long-term data source. Combining this with numerical modelling and machine learning can enable morphodynamic assessments in data-scarce areas. In the case of human interventions, such as cable trenching, high-resolution numerical modelling can help us understand and estimate the complex interaction between human interventions and the morphodynamic environment. Moreover, these numerical models, combined with data analysis, offer us better insight into the uncertainties in seabed morphodynamics, which are extrapolated into probabilistic estimates of future seabed level changes. By further developing and utilizing these methods the construction and maintenance of offshore structures can be made safer and more cost-effective, aiming to support the green energy transition.