# Seafloor Characterization Using Multibeam Echosounder Backscatter Data: Methodology and Results in the North Sea

^{1}

^{2}

^{3}

^{4}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Least Squares Cubic Spline Approximation (LS-CSA)

#### 2.1. Background and Objectives

#### 2.2. Principle of LS-CSA

## 3. Seafloor Characterization Using Proposed Method

#### 3.1. Application of Bayesian Method

#### 3.2. Estimation of Angular Calibration Curve

#### 3.3. Model Inversion Using Optimization Method

## 4. Application to MBES Dataset in Brown Bank

#### 4.1. Study Area and Data Description

#### 4.2. Bayes Classification Results

#### 4.3. Calibration of MBES

#### 4.4. Estimating Geoacoustic Parameters

## 5. Results and Discussion

#### 5.1. Acoustic Classes vs. Geoacoustic Parameters

#### 5.2. Grab Sample Ground-Truthing

#### 5.3. Importance of Inversion for Three Parameters

#### 5.4. Brown Bank Sediment Composition

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Diesing, M.; Mitchell, P.; Stephens, D. Image-based seabed classification: What can we learn from terrestrial remote sensing? ICES J. Mar. Sci.
**2016**, 73, 2425–2441. [Google Scholar] [CrossRef] - Diesing, M.; Thorsnes, T. Mapping of cold-water coral carbonate mounds based on geomorphometric features: An object-based approach. Geosciences
**2018**, 8, 34. [Google Scholar] [CrossRef] - Marsh, I.; Brown, C. Neural network classification of multibeam backscatter and bathymetry data from Stanton Bank (Area IV). Appl. Acoust.
**2009**, 70, 1269–1276. [Google Scholar] [CrossRef] - Ojeda, G.Y.; Gayes, P.T.; Van Dolah, R.F.; Schwab, W.C. Spatially quantitative seafloor habitat mapping: Example from the northern South Carolina inner continental shelf. Estuar. Coast. Shelf Sci.
**2004**, 59, 399–416. [Google Scholar] [CrossRef] - Clarke, J.H.; Danforth, B.; Valentine, P. Areal seabed classification using backscatter angular response at 95 kHz. In Proceedings of the SACLANTCEN Conf on High Frequency Acoustics in Shallow Water, Lerici, Italy, 30 June–4 July 1997; pp. 243–250. [Google Scholar]
- Fonseca, L.; Mayer, L. Remote estimation of surficial seafloor properties through the application angular range analysis to multibeam sonar data. Mar. Geophys. Res.
**2007**, 28, 119–126. [Google Scholar] [CrossRef] - Lamarche, G.; Lurton, X.; Verdier, A.-L.; Augustin, J.-M. Quantitative characterisation of seafloor substrate and bedforms using advanced processing of multibeam backscatter—Application to Cook Strait, New Zealand. Cont. Shelf Res.
**2011**, 31, S93–S109. [Google Scholar] [CrossRef] - Hamilton, L.J. Clustering of cumulative grain size distribution curves for shallow-marine samples with software program CLARA. Aust. J. Earth Sci.
**2007**, 54, 503–519. [Google Scholar] [CrossRef] - Hamilton, L.J.; Parnum, I. Acoustic seabed segmentation from direct statistical clustering of entire multibeam sonar backscatter curves. Cont. Shelf Res.
**2011**, 31, 138–148. [Google Scholar] [CrossRef] - Brown, C.J.; Smith, S.J.; Lawton, P.; Anderson, J.T. Benthic habitat mapping: A review of progress towards improved understanding of the spatial ecology of the seafloor using acoustic techniques. Estuar. Coast. Shelf Sci.
**2011**, 92, 502–520. [Google Scholar] [CrossRef] - Brown, C.J.; Todd, B.J.; Kostylev, V.E.; Pickrill, R.A. Image-based classification of multibeam sonar backscatter data for objective surficial sediment mapping of Georges Bank, Canada. Cont. Shelf Res.
**2011**, 31, S110–S119. [Google Scholar] [CrossRef] - Eleftherakis, D.; Amiri-Simkooei, A.; Snellen, M.; Simons, D.G. Improving riverbed sediment classification using backscatter and depth residual features of multi-beam echo-sounder systems. J. Acoust. Soc. Am.
**2012**, 131, 3710–3725. [Google Scholar] [CrossRef] [PubMed] - Snellen, M.; Gaida, T.C.; Koop, L.; Alevizos, E.; Simons, D.G. Performance of Multibeam Echosounder Backscatter-Based Classification for Monitoring Sediment Distributions Using Multitemporal Large-Scale Ocean Data Sets. IEEE J. Ocean. Eng.
**2018**, 1–14. [Google Scholar] [CrossRef] - Misiuk, B.; Diesing, M.; Aitken, A.; Brown, C.J.; Edinger, E.N.; Bell, T. A spatially explicit comparison of quantitative and categorical modeling approaches for mapping seabed sediments using random forest. Geosciences
**2019**, 9, 254. [Google Scholar] [CrossRef] - Stephens, D.; Diesing, M. Towards quantitative spatial models of seabed sediment composition. PLoS ONE
**2015**, 10, e0142502. [Google Scholar] [CrossRef] [PubMed] - Brown, C.J.; Beaudoin, J.; Brissette, M.; Gazzola, V. Multispectral multibeam echo sounder backscatter as a tool for improved seafloor characterization. Geosciences
**2019**, 9, 126. [Google Scholar] [CrossRef] - Buscombe, D.; Grams, P.E. Probabilistic substrate classification with multispectral acoustic backscatter: A comparison of discriminative and generative models. Geosciences
**2018**, 8, 395. [Google Scholar] [CrossRef] - Gaida, T.C.; Tengku Ali, T.A.; Snellen, M.; Amiri-Simkooei, A.R.; Van Dijk, T.A.G.P.; Simons, D.G. A multispectral Bayesian classification method for increased acoustic discrimination of seabed sediments using multi-frequency multibeam backscatter data. Geosciences
**2018**, 8, 455. [Google Scholar] [CrossRef] - Simons, D.G.; Snellen, M. A Bayesian approach to seafloor classification using multi-beam echo-sounder backscatter data. Appl. Acoust.
**2009**, 70, 1258–1268. [Google Scholar] [CrossRef] - Amiri-Simkooei, A.; Snellen, M.; Simons, D.G. Riverbed sediment classification using multi-beam echo-sounder backscatter data. J. Acoust. Soc. Am.
**2009**, 126, 1724–1738. [Google Scholar] [CrossRef] - Jackson, D.R.; Winebrenner, D.P.; Ishimaru, A. Application of the composite roughness model to high-frequency bottom backscattering. J. Acoust. Soc. Am.
**1986**, 79, 1410–1422. [Google Scholar] [CrossRef] - Fonseca, L.; Brown, C.; Calder, B.; Mayer, L.; Rzhanov, Y. Angular range analysis of acoustic themes from Stanton Banks Ireland: A link between visual interpretation and multibeam echosounder angular signatures. Appl. Acoust.
**2009**, 70, 1298–1304. [Google Scholar] [CrossRef] - Santos, R.; Rodrigues, A.; Quartau, R. Acoustic remote characterization of seabed sediments using the Angular Range Analysis technique: The inlet channel of Tagus River estuary (Portugal). Mar. Geol.
**2018**, 400, 60–75. [Google Scholar] - Collier, J.; Brown, C. Correlation of sidescan backscatter with grain size distribution of surficial seabed sediments. Mar. Geol.
**2005**, 214, 431–449. [Google Scholar] [CrossRef] - APL-UW Model. High-Frequency Ocean Environmental Acoustics Models Handbook; APL-UW Technical Report, No. APL-UW 9407; Williams, K.L., Ed.; Scientific Research: Wuhan, China, 1994; Available online: http://www.dtic.mil/docs/citations/ADB199453 (accessed on 26 June 2019).
- Bartels, R.H.; Beatty, J.C.; Barsky, B.A. Hermite and Cubic Spline Interpolation. In An Introduction to Splines for Use in Computer Graphics and Geometric Modeling; Morgan Kaufmann: San Francisco, CA, USA, 1998; Chapter 3; pp. 9–17. [Google Scholar]
- Chen, W.K. Feedback, Nonlinear, and Distributed Circuits; CRC Press: Boca Raton, FL, USA, 2009; pp. 9–20. ISBN 978-1-4200-5881-9. [Google Scholar]
- Reinsch, C.H. Smoothing by Spline Functions. Numer. Math.
**1976**, 10, 177–183. [Google Scholar] [CrossRef] - Runge, C. Über empirische Funktionen und die Interpolation zwischen äquidistanten Ordinaten. Zeitschrift für Mathematik und Physik
**1901**, 46, 224–243. [Google Scholar] - Burden, R.L.; Faires, J.D.; Reynolds, A.C. Numerical Analysis, 6th ed.; Brooks/Cole: Boston, MA, USA, 1997; pp. 120–121. [Google Scholar]
- Lyche, T. Discrete Cubic Spline Interpolation. BIT
**1976**, 16, 281–290. [Google Scholar] [CrossRef] - Lawson, C.L.; Hanson, R.J. Solving Least Squares Problems; Prentice Hall: Upper Saddle River, NJ, USA, 1974. [Google Scholar]
- Luenberger, D.G. Least-Squares Estimation. Optimization by Vector Space Methods; John Wiley & Sons: Hoboken, NJ, USA, 1997; pp. 78–102. [Google Scholar]
- Teunissen, P.J.G. Adjustment Theory: An Introduction; Series on Mathematical Geodesy and Positioning; Delft University Press: Delft, The Netherlands, 2000. [Google Scholar]
- Zangeneh-Nejad, F.; Amiri-Simkooei, A.R.; Sharifi, M.A.; Asgari, J. Cycle slip detection and repair of undifferenced single-frequency GPS carrier phase observations. GPS Solut.
**2017**, 21, 1593–1603. [Google Scholar] [CrossRef] - Koop, L.; Amiri-Simkooei, A.R.; van der Reijden, K.J.; O’Flynn, S.; Snellen, M.; Simons, D.G. Seafloor classification in a sand wave environment on the Dutch Continental Shelf using multibeam echosounder backscatter data. Geosciences
**2019**, 9, 142. [Google Scholar] [CrossRef] - De Boor, C. A Practical Guide to Splines, rev. ed.; Springer: New York, NY, USA, 2001. [Google Scholar]
- Alevizos, E.; Snellen, M.; Simons, D.G.; Siemes, K.; Greinert, J. Acoustic discrimination of relatively homogeneous fine sediments using Bayesian classification on MBES data. Mar. Geol.
**2015**, 370, 31–42. [Google Scholar] [CrossRef] - Eleftherakis, D.; Snellen, M.; Amiri-Simkooei, A.; Simons, D.G.; Siemes, K. Observations regarding coarse sediment classification based on multi-beam echo-sounder’s backscatter strength and depth residuals in Dutch rivers. J. Acoust. Soc. Am.
**2014**, 135, 3305–3315. [Google Scholar] [CrossRef] - Hamilton, E. Prediction of deep-sea sediment properties: State-of-the-art. In Deep-Sea Sediments, Physical and Mechanical Properties; Inderbitzen, A.L., Ed.; Plenum Press: New York, NY, USA, 1974; pp. 1–43. [Google Scholar]
- Snellen, M.; Simons, D.G. An assessment of the performance of global optimisation methods for geoacoustic inversion. J. Comput. Acoust.
**2008**, 16, 199–223. [Google Scholar] [CrossRef] - Snellen, M.; Siemes, K.; Simons, D.G. Model-based sediment classification using single-beam echosounder signals. J. Acoust. Soc. Am.
**2011**, 129, 2878–2888. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Snellen, M.; Eleftherakis, D.; Amiri-Simkooei, A.R.; Koomans, R.L.; Simons, D.G. An inter-comparison of sediment classification methods based on multi-beam echo-sounder backscatter and sediment natural radioactivity data. J. Acoust. Soc. Am.
**2013**, 134, 959–970. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Knaapen, M.A. Sandbank occurrence on the Dutch continental shelf in the North Sea. Geo-Mar. Lett.
**2009**, 29, 17–24. [Google Scholar] [CrossRef] - Flemming, N.C. The Scope of Strategic Environmental Assessment of North Sea Areas SEA3 and SEA2 in Regard to Prehistoric Archaeological Remains. Report TR 014; Department of Trade and Industry: Hong Kong, China, 2002. [Google Scholar]
- Laban, C. Seabed mapping in the Dutch sector of the North Sea. Sea Technol.
**2006**, 47, 47–51. [Google Scholar] - Ward, I.; Larcombe, P. Determining the preservation rating of submerged archaeology in the post-glacial southern North Sea: A first-order geomorphological approach. Environ. Archaeol.
**2008**, 13, 59–83. [Google Scholar] [CrossRef] - Van Dijk, T.A.; van Dalfsen, J.A.; van Lancker, V.; van Overmeeren, R.A.; van Heteren, S.; Doornenbal, P.J. Benthic habitat variations over tidal ridges, North Sea, the Netherlands. In Seafloor Geomorphology as Benthic Habitat; Elsevier: Amsterdam, The Netherlands, 2012; pp. 241–249. [Google Scholar]
- Amiri-Simkooei, A.R.; Hosseini-Asl, M.; Safari, A. Least squares 2D bi-cubic spline approximation: Theory and applications. Measurement
**2018**, 127, 366–378. [Google Scholar] [CrossRef] - Folk, R.L.; Ward, W.C. Brazos River bar [Texas]; a study in the significance of grain size parameters. J. Sedim. Res.
**1957**, 27, 3–26. [Google Scholar] [CrossRef] - Simons, D.G.; Snellen, M.; Ainslie, M.A. A multivariate correlation analysis of high-frequency bottom backscattering strength measurements with geotechnical parameters. IEEE J. Ocean. Eng.
**2007**, 32, 640–650. [Google Scholar] [CrossRef] - De Swart, H.E.; Yuan, B. Dynamics of offshore tidal sand ridges, A review. Environ. Fluid Mech.
**2018**, 1–25. [Google Scholar] [CrossRef] - Roos, P.C.; Hulscher, S.; Van Der Meer, F.; Van Dijk, T.; Wientjes, I.G.; van den Berg, J. Grain size sorting over offshore sandwaves: Observations and modeling. In Proceedings of the 5th IAHR Symposium on River, Coastal and Estuarine Morphodynamics, Enschede, The Netherlands, 17–21 September 2007. [Google Scholar]
- Walgreen, M.; De Swart, H.E.; Calvete, D. A model for grain-size sorting over tidal sand ridges. Ocean Dyn.
**2004**, 54, 374–384. [Google Scholar] [CrossRef]

**Figure 1.**Bathymetry of Brown Bank extracted from multibeam echosounder data; yellow circles are positions having grab samples, yellow circles with dots indicates grab samples plus video data.

**Figure 2.**Bayesian classification map along with grab samples based on Folk scheme. Four acoustic classes ranging from lowest backscatters (green) to highest values (red), figure from Koop et al. [36].

**Figure 3.**Calibration curve obtained from 1000 independent runs; results are presented in two frames (top and bottom) each consisting of 500 runs; in each frame the average curve is indicated in blue. Spline function consists of a series of third-order polynomials connected at knots indicated as cyan circles at 5-degree intervals.

**Figure 4.**Two typical examples of optimization problem in which three geoacoustic parameters were searched for, Port side (

**left**), Starboard side (

**right**); indicated in plots are observed backscatter curve (dashed blue line), corrected backscatter curve after applying calibration curve (solid red line), and modeled backscatter curve (solid green line).

**Figure 5.**Inverted geoacoustic parameters of mean grain size ${M}_{z}$ (

**a**), spectral strength ${w}_{2}$ (

**b**), and volume scattering parameters ${\sigma}_{2}$ (

**c**) in Brown Bank, North Sea.

**Figure 6.**Smoothed maps of inverted mean grain size ${M}_{z}$ (

**top**), spectral strength ${w}_{2}$ (

**middle**), and volume scattering parameters ${\sigma}_{2}$ (

**bottom**). Indicated in top frame also mean grain sizes of grab samples based on Folk classification scheme. Dashed line indicates square patches connected to each other using LS-BICSA method (Amiri-Simkooei et al. [49]).

**Figure 7.**Histogram of inverted mean grain size ${M}_{z}$ (

**top**), spectral strength ${w}_{2}$ (

**middle**), and volume scattering parameter ${\sigma}_{2}$ in log scale (

**bottom**) categorized versus Bayesian acoustic classes.

**Figure 8.**Examples of predicted backscatter curves of two grain size values ${M}_{z}=1.5\text{}\varphi $ (

**top**) and ${M}_{z}=2\text{}\varphi $ (

**bottom**) for which spectral strength ${w}_{2}$ and volume scattering parameter ${\sigma}_{2}$ were inverted using optimization method.

**Figure 9.**BGS Folk classification versus USGS Folk classification of grab samples from Brown Bank; numbers indicate number of occurrences for grab samples.

**Figure 10.**Averaged (over three replicates) mean grain size and standard deviation of grab samples versus their corresponding inverted values using optimization method.

**Figure 11.**Smoothed maps of inverted mean grain size ${M}_{z}$ along with Folk classes of grab samples when spectral strength ${w}_{2}$ and volume scattering parameter ${\sigma}_{2}$ were kept fixed to their average values.

**Figure 12.**Zoom-in map of inverted geoacoustic parameters using optimization method applied to mean backscatter curve around grab sample 23 (indicated inside ellipses with green square and ${M}_{z}=4.40\pm 1.40$); (

**a**) mean grain size ${M}_{z}$ for which search was performed only over ${M}_{z}$ (fixing ${w}_{2}$ and ${\sigma}_{2}$ ), (

**b**) mean grain size ${M}_{z}$, and (

**c**) volume scattering parameters ${\sigma}_{2}$ for which search was performed over three parameters ${M}_{z}$, ${w}_{2}$, and ${\sigma}_{2}$.

**Figure 13.**Large scale variations of estimated mean grain size ${M}_{z}$ (

**top**), spectral strength ${w}_{2}$ (

**middle**), and volume scattering parameter ${\sigma}_{2}$ (

**bottom**) over Brown Bank structure in eastward direction (results averaged along north axis).

**Figure 14.**Video data presented as still images from stations 9 and 12 (in troughs) and 3 and 8 (on crest). Indicated in pictures are also two green dots representing a distance of ~20 cm, sand ripples having wavelengths of ~5 cm and other surficial structures/compositions.

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Amiri-Simkooei, A.R.; Koop, L.; van der Reijden, K.J.; Snellen, M.; Simons, D.G.
Seafloor Characterization Using Multibeam Echosounder Backscatter Data: Methodology and Results in the North Sea. *Geosciences* **2019**, *9*, 292.
https://doi.org/10.3390/geosciences9070292

**AMA Style**

Amiri-Simkooei AR, Koop L, van der Reijden KJ, Snellen M, Simons DG.
Seafloor Characterization Using Multibeam Echosounder Backscatter Data: Methodology and Results in the North Sea. *Geosciences*. 2019; 9(7):292.
https://doi.org/10.3390/geosciences9070292

**Chicago/Turabian Style**

Amiri-Simkooei, Alireza R., Leo Koop, Karin J. van der Reijden, Mirjam Snellen, and Dick G. Simons.
2019. "Seafloor Characterization Using Multibeam Echosounder Backscatter Data: Methodology and Results in the North Sea" *Geosciences* 9, no. 7: 292.
https://doi.org/10.3390/geosciences9070292