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Open Access Quantifying the reduction of ground modelling uncertainty achieved through seismic data re-processing and re-interpretation

An accurate ground model realistically representing the subsurface geological and geotechnical conditions is a key input for the design of offshore wind farms (OWF). Accuracy of the ground model largely depends on the quality of the subsurface information available: the ground investigation geotechnical data from intrusive ground investigations but also high or ultra high-resolution reflection seismic (UHRS) seismic. Quality of seismic data varies between projects and frequently depends on the equipment used, met ocean conditions, ability of the survey crew during acquisition, and most crucially, the processing techniques applied. Here we present a case study from a OWF site located in the Southern Baltic Sea covering a geologically complex, formerly glaciated area where the original acquired, processed and interpreted UHR seismic data were identified to be of insufficient quality for ground modelling. As a consequence of the insufficient quality, the dataset was re-processed leading to improved interpretations and reduced uncertainties associated with the subsurface conditions. The achieved improvement of the ground model following re-processing and reinterpretation was quantified using a novel approach to highlight the importance and impact of geological and geophysical data quality and process-based seismic data interpretation on OWF design. We propose that the approach developed for the purpose of this study could be applied to other ground models allowing for the identification and quantification of key ground modeling and interpretation uncertainties. This is of particular importance where the geological setting is complex, for example on formerly glaciated continental shelves, and seismic data interpretation is hugely dependent on data quality as well as the understanding on the Quaternary geological history of the region.

Language: English

Document Type: Research Article

Affiliations: 1: University of Aberdeen, Aberdeen UK 2: Atkins member of SNC Lavalin, Epsom, UK 3: RockWave, Bedford, UK 4: OceanWinds, Warsaw, Poland

Publication date: January 1, 2023

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