The potential of AI to optimise seabed surveys for offshore wind

Offshore wind energy is a rapidly growing sector, and efficient seabed surveys play a crucial role in its success. These surveys and site investigations, such as geophysical and geotechnical, provide critical information about the seafloor, helping engineers and project managers make informed decisions during the planning, construction, installation and maintenance phases.   

Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks typically requiring human intelligence. These systems learn from data, recognise patterns, and make decisions based on their understanding. In the context of seabed surveys for offshore wind, AI has the potential to play a role in enhancing efficiency and accuracy. 

The challenge: Lengthy surveys and manual interpretation  

Depending on the requirements, a site investigation survey of the seabed can take many months to complete. In addition to unfavourable weather that can create unsuitable conditions for quality data acquisition, a project’s timeline is typically dependent on the size of the survey area, coverage achievable with the equipment used to meet the resolution requirements during acquisition, and processing workflows to turn terabytes of raw data into reliable information. Complementary AI-enhanced processes for the detection of potential seafloor hazards including boulders, UXOs, and abandoned pipelines, has the potential to mitigate some of the risks to a project’s timeline.   

The potential for AI to transform seabed surveys 

AI algorithms also have the potential to analyse sonar data. They can identify patterns, anomalies, and potential hazards on the seafloor. Automating the detection process can significantly reduce the overall time required for surveys. This may raise the question of quality control. However, AI can potentially review and validate data, ensuring its consistency, accuracy, and reliability.  

Currently, manual measurement of thousands of kilometres of seafloor data takes time. AI can automate this process, measuring distances and identifying objects faster. It can improve data quality through its ability to operate consistently regardless of weather conditions, eliminating the risk of false data during storms, for example. Furthermore, AI can adjust resolution settings dynamically, ensuring high-quality data collection.  

The benefits of complementary AI  

✓ Quality assurance: AI can act as a virtual consultant, reviewing data for inconsistencies, errors, and gaps.  

✓ Budget optimisation: By streamlining the survey process, AI can help to allocate resources effectively.  

✓ Risk mitigation: Identifying inaccuracies early can prevent costly mistakes, for example, deploying more vessels due to flawed data.  

However, despite the benefits of AI, human expertise is irreplaceable. AI should be considered as a resource to complement human expertise by automating repetitive tasks, allowing humans to focus on complex analysis and decision-making.  

It is important to remember that if a human cannot see an object on the seafloor, AI will not be able to either, further justifying it as a tool, not a replacement. The advantage is that AI can free up human resources, allowing focus on critical aspects of the project.  

Crucially, AI relies on quality source data, meaning that the requirement for accurate acquisition incorporating soil sampling and geophysical methods is essential and a process that cannot be replicated.  

Conclusion  

AI has the potential to optimise seabed surveys for offshore wind projects. By automating tasks, improving data quality, and acting as a reliable consultant, AI can accelerate the critical path, the process of identifying the string of tasks and dependencies that take the longest time to complete in a project, and ensure successful project execution. As the industry embraces AI and models improve over time with more data, enhancing their accuracy, we look forward to seeing how it will enable faster, more accurate surveys.