Generating synthetic CPTs from marine seismic reflection data using a neural network approach

For the development of an offshore wind farm, understanding the geological and geotechnical conditions in the upper 100 m below seafloor is crucial when reducing ground risk and designing (cost-effective) wind turbine foundations. We developed and tested a neural network approach to derive predictive (i.e. synthetic) values for CPT parameters– specifically net cone resistance (qn*) – from seismic reflection data. The synthetic parameter values come with a uncertainty bandwidth. Subsequently, the trained neural network was applied to 2D UHR MCS data that were acquired at the planned Hollandse Kust (west) Wind Farm Zone. The goal was to calculate continuous cone resistance values (i.e. qn*) from seismic data using a supervised neural network. Different network architectures and seismic attributes were tested and compared for this purpose. Here, the input data consisted of seismic attributes determined from 2D UHR MCS dataset, and the interpreted geological soil units (based on interpretation of these 2D data). The target training dataset consisted of an extracted subset of seafloor based CPTs. In general, predicted and measured qn* values showed good agreement, especially for the upper 20m below seafloor. Trend-type prediction applies to transitional and strongly layered (<1m scale) soil, as expected.