Seabed intelligence at scale for the blue economy
Hydrography has long been defined by depth.
From nautical charting to navigation safety, bathymetry remains the primary deliverable.
But many blue economy activities – offshore wind, subsea cables, marine spatial planning – demand more than depth; they need to know what the seabed is made of.
Backscatter, which is routinely collected alongside bathymetry, holds this answer – yet it remains underutilized.
In my view, this is no longer a technical oversight.
It is a missed strategic opportunity.
The global blue economy is valued at approximately US$2.5 trillion annually and continues to expand rapidly.
Offshore wind capacity alone is projected to reach hundreds of gigawatts in the coming decade, while subsea infrastructure – telecommunications cables, power interconnectors and carbon capture systems – is being deployed at unprecedented rates.
Each of these developments depends on reliable seabed characterization.
Yet traditional methods such as grab sampling, coring and video inspection remain inherently limited in spatial coverage.
They provide valuable point data, but cannot scale to meet modern demands.
Backscatter offers a different path.
Acquired as part of routine hydrographic surveys, it provides continuous seabed information across entire survey areas without additional acquisition time.
The data already exists.
The real challenge lies in how effectively it is processed, interpreted and integrated into decision-making workflows.
Hydrography beyond depth
Hydrographic surveying has traditionally focused on accurate depth measurement to support safe navigation, guided by S-44 International Hydrographic Organization (IHO) standards for hydrographic surveys.
Multibeam echosounding (MBES) has greatly advanced this objective, enabling high-resolution mapping of seafloor morphology.
However, MBES systems inherently collect more than depth; they record both bathymetry and backscatter simultaneously.
While bathymetry defines the geometry of the seabed, backscatter provides insight into its composition and physical properties.
As hydrography evolves to support broader applications, it is increasingly necessary to treat backscatter data not as a secondary output, but as a core hydrographic dataset.
What bathymetry alone cannot reveal
Backscatter represents the strength of acoustic energy returned from the seabed following sonar interaction.
Its response is influenced by seabed composition (mud, sand, gravel, rock), surface roughness and heterogeneity, acoustic frequency, incidence angle and environmental conditions.
Operational guidance from organizations such as the National Oceanic and Atmospheric Administration (NOAA) shows that areas of similar bathymetry can produce significantly different backscatter signatures.
Whereas bathymetry describes geometry, backscatter reveals composition.
Figure 1: Comparison of bathymetry and backscatter from the same survey area, highlighting how similar depths can produce different acoustic responses due to seabed variability. (Image courtesy: NORBIT Subsea)
There are various reasons why backscatter data is currently underutilized.
First, backscatter quality depends heavily on consistent sonar settings, vessel motion and survey design.
This sensitivity means that variations in acquisition parameters directly affect the reliability of the final mosaic.
Second, the intensity of the returned signal varies with beam incidence angle.
Without proper angular corrections, artifacts may obscure true seabed characteristics.
Last but not least, processing backscatter data is complex.
Unlike bathymetry, backscatter workflows lack full standardization.
Differences in processing methodologies can lead to inconsistencies across datasets, complicating interpretation.
However, these challenges are not limitations of backscatter itself, but indicators of the need for improved workflow discipline and training within hydrographic practice.
From data to insight: a practical workflow perspective
Transforming backscatter into a usable hydrographic product requires a structured and consistent workflow.
In survey operations I have been involved in, bathymetric data is processed using software such as QPS Qimera, CARIS and so on.
Backscatter is refined using tools such as QPS-FMGT to generate normalized mosaics.
Key factors influencing output quality include consistent acquisition parameters, appropriate frequency selection, radiometric and geometric corrections, and robust mosaicking techniques.
In other words, the transition from raw acoustic intensity to interpretable seabed information is not automatic.
It requires both technical expertise and deliberate workflow design.
Figure 2: Conceptual illustration showing variation in backscatter intensity with beam incidence angle.Backscatter is fundamentally a hydrographic dataset, but its value extends across multiple maritime sectors, including:Offshore energy: backscatter supports foundation design, cable routing and scour assessment by identifying sediment type and seabed variability, directly reducing uncertainty and optimizing engineering decisions.
Ports and navigation: it enables precise identification of dredgeable versus non-dredgeable materials, improving efficiency and reducing operational costs.
Subsea infrastructure: backscatter reveals hazards such as rock outcrops, boulders and mobile sediments that are not evident from bathymetry alone.
Environmental management: it provides scalable habitat mapping to support environmental assessments, conservation planning, and regulatory compliance.
These applications highlight how hydrographic data, particularly backscatter, directly supports critical blue economy sectors, transforming hydrography from a navigation-focused discipline into a key enabler of sustainable ocean development.
The future: calibration, data and intelligence
Historically, backscatter has often been used as a qualitative dataset.
However, advances in calibration, angular response analysis and processing workflows are enabling quantitative backscatter products.
When properly calibrated and archived, backscatter becomes a time-series dataset capable of supporting long-term monitoring of seabed change and predictive analysis.
Emerging machine learning techniques are further enhancing its value.
As these models require large, high-quality datasets, organizations that invest in backscatter today will be better positioned to leverage automated seabed classification in the future.
This is particularly critical in data-sparse regions where improved seabed intelligence can directly support national hydrographic development and sustainable ocean use.
Conclusion
Hydrography is no longer solely about measuring depth; it is about understanding the seafloor.
Backscatter is not an optional by-product – it is a critical dataset that enables this transformation.
Promoting hydrography in today’s context means demonstrating its value beyond navigation.
Backscatter provides a clear example of how hydrographic data products support modern maritime operations and the global blue economy.
In view of the expansion of the blue economy and the increasing demands on ocean data, the integration of bathymetry and backscatter will define the future of hydrographic surveying.
The real question is no longer whether to collect backscatter.
It is whether we are ready to use it to its full potential.
Figure 3: Processed backscatter mosaic showing tonal variations associated with different seabed compositions.
(Image courtesy: Geosciences/Craig J. Brown)
(Image courtesy: Geosciences/Craig J. Brown)
Links :
- International Hydrographic Organization (2020), Standards for Hydrographic Surveys (S-44)
- National Oceanic and Atmospheric Administration – Office of Coast Survey, Multibeam Backscatter Resources
- Lamarche, G., et al.
(2011), Guidelines for Backscatter Acquisition and Processing - Brown, C.
J., et al.
(2011), Mapping Benthic Habitats Using Multibeam Backscatter - Buscombe, D.
(2021), Machine Learning for Seabed Classification
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