Estimating soil strength using ultra high-resolution seismic data: A case study from a shallow water wind farm

The increasing global reliance on offshore wind farms as a sustainable energy source necessitates precise soil assessment for foundation design due to their high foundation costs and complex integration into marine environments. This study explores the use of ultrahigh-resolution seismic (UHRS) data in conjunction with cone penetration test data for soil strength estimation in offshore wind farm development. We develop a convolutional neural network-based approach that leverages UHRS data for direct soil strength estimation, aiming to address the challenges of limited cone penetration test measurements and potential overfitting in complex geologic settings. Our methodology involves preprocessing UHRS and cone penetration test data, interpreting and integrating geologic unit (GU) information, and applying repeated k-fold cross validation to evaluate model performance. The field data results demonstrate the model's efficacy in accurately predicting cone penetration test curve trends, albeit with some amplitude discrepancies. We highlight the significance of geologic interpretation in predicting soil strength and identify the dependency on valid data within each GU as a limitation.