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

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## Abstract

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## 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

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**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