Celtic Sea Floating Offshore Wind – Site Intelligence & Spatial Characterization

Executive Summary

This study establishes the site intelligence layer of Morie Analytics by transforming publicly available marine geospatial datasets into engineering-ready inputs for floating offshore wind development.

Using Celtic Sea lease areas, GEBCO 2025 bathymetry, and EMODnet seabed classification, the workflow converts raw regional data into structured spatial products that support early-stage engineering decisions.

The result is a reproducible Python-based pipeline that replaces fragmented GIS workflows with a scalable and transparent offshore data-processing framework.

This module represents the entry point of the workflow, where raw geospatial data is converted into structured engineering inputs.

Site intelligence → Layout generation → Soil reconstruction → Mooring physics → Anchor verification → Cable optimization

Project Scope

• Screening of 5 Celtic Sea lease areas
• Processing of GEBCO 2025 bathymetry
• Integration of EMODnet Folk 7 seabed classification
• Generation of lease-scale bathymetry and soil maps
• Validation of local results against regional datasets

This study converts regional marine data into engineering-ready spatial constraints.

Engineering Context

Early-stage floating offshore wind design requires rapid and consistent evaluation of:

• Water depth distribution
• Seabed conditions
• Anchor feasibility constraints
• Mooring footprint implications
• Installation constraints

These assessments are often performed manually in GIS environments, leading to inconsistencies across projects.

This workflow introduces a structured computational approach, where public datasets are transformed into standardized engineering inputs suitable for downstream design modules.

Inputs and Data Sources

This study integrates publicly available marine geospatial datasets:

• Celtic Sea lease area boundaries
• GEBCO 2025 global bathymetry grid
• EMODnet Folk 7 seabed classification

All datasets are:

• Harmonized into a projected coordinate system
• Spatially aligned
• Processed into engineering-ready formats

This provides the spatial data foundation for downstream modules.

Technical Architecture

The workflow is implemented in Python using:

• geopandas → lease boundary processing
• xarray → GEBCO bathymetry access
• numpy → grid generation and numerical operations
• matplotlib → visualization
• famodel → 2D plotting and soil/bathymetry integration

Core modules:

• read_lease_boundary → lease area extraction
• make_lonlat_grid → structured grid generation
• sample_gebco_depths → bathymetry sampling on grid
• label_substrate → soil classification assignment
• convert_and_write → export to engineering-ready formats

System Flow

Raw Geospatial Data → Spatial Processing → Engineering Inputs

This modular structure ensures reproducibility, clarity, and scalability.

Processing Workflow

For each lease area:

1. Import lease boundary
2. Transform coordinates to projected system
3. Load GEBCO bathymetry
4. Mask bathymetry within lease polygon
5. Load EMODnet seabed data
6. Intersect seabed classification with lease
7. Generate plots and structured outputs

This converts regional datasets into engineering-ready spatial constraints.

Regional Lease Context

Figure 1 – Regional lease areas used as the screening context for floating offshore wind development.

Engineering Significance

Offshore design begins at regional scale, where:

• Lease areas compete for feasible conditions
• Bathymetry and soils vary spatially
• Design assumptions must remain consistent

Bathymetry Characterization

Figure 2 – Lease-scale bathymetry used to constrain feasible layout regions.

The bathymetric dataset reveals water depths across the selected Celtic Sea lease areas typically ranging between:

• ~85 m to 100 m approx.
• Gentle slopes with gradual depth transitions

This results in a bathymetrically smooth environment, well-suited for floating offshore wind deployment.

Engineering Significance

Bathymetry directly informs:

• Floater type feasibility
• Mooring line configurations and geometry
• Anchor radius and footprint
• Cable routing constraints

From an engineering perspective, the observed depth range implies:

• Floating wind is prefered (fixed-bottom solutions are not economically viable at this depth scale)
• Mooring systems will operate in a deep-water regime (even if its in the shallowest range), where line length and compliance dominate behavior
• Anchor locations must be designed considering relevant horizontal offsets and footprint expansion

The relatively mild seabed slopes enable:

• Stable and predictable mooring layouts
• Reduced risk of localized load amplification due to terrain effects
• Simplified cable routing with fewer constraints related to steep gradients

However, depth variability across the site still requires:

• Consistent normalization of mooring configurations (addressed in morie_mooring)
• Alignment of anchor design with local water depth conditions (addressed in morie_anchor)
• Consideration of cable touchdown zones under varying depth profiles (addessed in morie_cable)

This bathymetric characterization defines the geometric boundary conditions for all downstream engineering modules.

Seabed Characterization

Initial Mapping

Figure 3 – Initial soil classification used for validation of processing steps.

EMODnet Classification Alignment

Figure 4 – EMODnet Folk 7 classification legend.

The EMODnet seabed dataset provides sediment classification at multiple levels of resolution:

• Folk-16 → highly detailed classification including mixed sediment types
• Folk-7 → intermediate engineering-relevant grouping
• Folk-5 → simplified classification for large-scale screening

In this workflow, the Folk-7 classification is selected as a balance between:

• Spatial resolution (soil horizontal variability)
• Engineering interpretability
• Compatibility with anchor and cable design models

This level preserves key distinctions (e.g., sand vs mud vs coarse material) while avoiding excessive fragmentation of sediment classes.

Engineering Mapping

Figure 5 – Soil classification aligned with EMODnet standards.

The processed map shows a predominance of sandy sediments across the selected Celtic Sea region, with localized variations including:

• Finer materials (mud-dominated zones)
• Coarser sediments and transitional layers

Engineering Significance

Seabed classification supports:

• Anchor concept screening (e.g., suction piles vs driven solutions)
• Soil-structure interaction assumptions (strength, stiffness, friction)
• Cable burial feasibility and protection requirements

From an engineering perspective, the dominance of sand suggests:

• Favorable conditions for predictable installation behavior
• Strong dependence on relative density and friction angle
• Suitability for drag-embedded or driven anchor concepts, depending on depth and variability

At the same time, localized heterogeneity highlights the need for:

• Site-specific soil reconstruction (addressed in morie_soil)
• Robust design envelopes for mixed conditions

Regional Context Verification

Figure 6 – Regional seabed conditions across the Celtic Sea.

Local-to-Regional Validation

Figure 7 – Verification of lease-scale results within regional context.

Engineering Significance

Ensures:

• Spatial accuracy
• Classification consistency
• Traceability to source datasets

Outputs Generated

For each lease area:

• Bathymetry grids (.txt)
• Soil classification grids (.txt)
• Spatial plots (.png)
• Structured CSV summaries

These outputs are directly usable in downstream engineering workflows.

Engineering Applications

The outputs support:

• Lease-to-lease comparison
• Anchor concept screening
• Mooring system feasibility
• Cable routing strategy
• Installation planning

This transforms raw geospatial data into engineering decision inputs.

Relationship to Other Morie Study Cases

This study is the entry point of the Morie Analytics workflow.

Feeds into:

• morie_layout → spatial layout optimization
• morie_soil → localized soil modeling
• morie_mooring → depth-informed system geometry
• morie_anchor → preliminary anchor feasibility
• morie_cable → seabed-aware routing constraints

It provides the baseline environmental context for all downstream modules.

Why It Matters Commercially

• Reduces reliance on manual GIS workflows
• Enables rapid multi-site screening
• Improves consistency across projects
• Accelerates early-stage decision making
• Provides scalable data infrastructure for developers

This is where data becomes engineering leverage.

Aspects to Improve

• Incorporation of higher-resolution bathymetry datasets
• Integration of geotechnical CPT data
• Inclusion of metocean conditions
• Automated constraint mapping (exclusion zones, cables, shipping)

These extensions would move the workflow closer to FEED-level site characterization.

Design Philosophy

This study reflects the Morie Analytics approach:

• Physics-informed
• Modular
• Traceable
• Engineering-focused
• Scalable

How to Run

1. Place datasets in celtic_sea_share/

2. Install dependencies:

   • numpy
   • matplotlib
   • geopandas
3. Execute:

python morie_site.py