3rd EAGE/SUT Workshop on Integrated Site Characterization for Offshore Renewable Energy

Human activities that utilize the ocean, such as increased vessel traffic, marine leisure activities, offshore platforms and drilling facilities, and more recently, offshore wind farms, are steadily on the rise. As a result, the risk of maritime accidents has also grown, leading to a heightened demand for comprehensive and multidimensional marine information to ensure safe maritime operations. Against this backdrop, acoustic tomography technology, which analyzes the travel time of underwater sound waves, is gaining global attention. This technology enables the analysis of ocean currents and sound speed using acoustic travel times. Since sound speed is closely related to factors such as water temperature, salinity, and depth, it also allows for indirect analysis of ocean temperature. Traditionally, the Doppler current profiler—based on the Doppler effect of sound waves—has been the most common method for observing ocean currents. In coastal areas, HF-RADAR systems are used to monitor surface currents over a wide area. However, each has limitations and advantages. In this context, by analyzing the travel times between multiple signal paths, Coastal Acoustic Tomography (CAT) is emerging as a promising technology capable of providing real-time, wide-area, three-dimensional observations of ocean currents and temperature distribution.

Geophysical & geotechnical site characterization surveys for offshore wind (OW) can take multiple years. This sequential and iterative survey strategy is sub-optimal and ultimately unsustainable. We will show that an integrated geophysical and hydrographic survey incorporating Ultra High Resolution (UHR) 3D seismic data – utilizing advanced processing and imaging methods - to characterize the seabed & shallow subsurface is a viable way to both optimize the OW site characterization process and reduce the overall duration & cost.

Cone penetration tests (CPT) provide tip resistance (qt) and sleeve friction (fs) profiles for pile design, but data are often sparse due to cost and access, creating uncertainty at untested locations. This study proposes a novel ML-based method for CPT probabilistic prediction at untested locations in layered grounds, incorporating CPT and soil type profiles. The method consists of: 1) soil classification at CPT locations with no borehole data available, 2) stratigraphy interpolation in between CPT locations, and 3) CPT probabilistic predictions for pile capacity evaluation using spatial coordinates and soil type as inputs. Simulated qt and fs data were generated using Random Field with five soil types and a defined correlation structure. Results showed that including soil type in addition to coordinates improved prediction accuracy for both qt and fs, with higher R² and lower errors compared to the coordinate-only model. Using the distribution of predicted CPT, the Unified CPT-based axial pile capacity method was applied to an assumed closed-ended pile, showing ∼30% reduction in CoV and ∼60% reduction in error at several untested locations. Overall, findings from simulated data indicate the potential of this ML-based approach; however, further validation is needed using real-world field measurements.

Free-fall penetrometers (FFPs) enable rapid seabed characterization, but interpreting their data and relating it to soil properties remains challenging. This study employs Bayesian inference to quantify uncertainties in converting FFP measurements, such as acceleration and velocity, into quasi-static tip resistance. In parallel, a multilayer perceptron (MLP) model is trained using laboratory FFP tests in kaolin clay. Results demonstrate that Bayesian inference effectively optimizes semi-empirical equations for FFP data interpretation, even with limited datasets. Furthermore, the MLP achieves substantially higher predictive accuracy in estimating quasi-static tip resistance compared to conventional approaches.