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An Integrated Workflow for Windfarm Site Characterization by Deep Learning
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, Third EAGE Digitalization Conference and Exhibition, Mar 2023, Volume 2023, p.1 - 6
Abstract
With the target of net-zero carbon emissions by 2050, the demand for renewable energy is increasing exponentially, and offshore wind farms are identified of the most potential of investment. Developing an offshore wind farm requires robust characterization of the near subsurface soils for turbine foundation design, construction, and monitoring, which faces with many challenges related to seafloor topography mapping, shallow geohazard detection, structure interpretation and modeling, soil type analysis, geotechnical parameter estimation and so on. This work revisits these existing challenges in from the perspective of pattern recognition, investigates the feasibility of deep learning in resolving them, and proposes an integrated workflow of great potential in accelerating the process of windfarm site characterization. Its values are demonstrated through applications to the public HKZ dataset within the Dutch wind farm zone for accelerating three major tasks, including picking multiple major horizons, mapping the seafloor topography, and estimating the essential geotechnical parameters. Future efforts are expected for more components in the proposed workflow for further integration and automation.