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20. Region of Interest. 0. Peak Amplitude. Water column. Seabed substrate. -20. Echo return (dB). -40. Above seabed (m). Below seabed (m). -60. 9.2 m. 5 m. 2.2 m. -80. A. B. 1.3 m. 2.1 m. -100. 0. 50. 100. 150. 200. 250. Distance from sea level (m).

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20

Region of

Interest

0

Peak

Amplitude

Water

column

Seabed

substrate

-20

Echo return (dB)

-40

Above seabed (m)

Below seabed (m)

-60

9.2 m

5 m

2.2 m

-80

A

B

1.3 m

2.1 m

-100

0

50

100

150

200

250

Distance from sea level (m)

Fig. 5 Optimal depth selection from mean adjacent mean-square-err

Fig. 1 Single-beam echo sounder Fig. 2 Transceiver and data capture equipment

Maximal depth

Optimal depth

PCA + k-means

Optimal depth

Maximal depth

Fig. 3 Data processing flowchart Fig. 4 SBES time series

QT local

Best result

Fig. 6 Clustering results from optimal and maximal depths

Seabed classification using SBES data

Peter Hung (NUIM), Seán McLoone (NUIM), Xavier Monteys (GSI)

Feature extraction

Due to the high dimensionality of time series data, the large number of samples collected in sea trips and the heteroscedastic noise contained within, a total of four features are extracted from the raw echo data. The temporal mean and associated standard deviation are standard features that has direct relationships with seabed geology. To convey information about the relationship between adjacent time series, measures of spatial randomness and spatial correlation are proposed.

Optimal depth selection

A novel adjacent mean-square-error metric is used to estimate the optimum time series interval for feature extraction. This is based on the assumption that seabed substrate should produce gradually changing features unless the echo captures additional information, such as sidelobe backscatter and background noise. Determined separately for the above and below time series segments, optimal depth is defined as the point of inflexion or minimum in the following plots.

Background

  • Seabed type clustering

  • Identification of seabed type (such as mud, sand and rock) is of value in many applications including seabed mapping, coastal management and seabed conservation. There are two main types of echo sounders for such purposes, multi-beam (MBES) and single-beam (SBES). The difference between the two types of echo sounders is that while SBES has one transceiver that emits and detects echo time series at normal incidence to the seabed, MBES has multiple transceivers sending sound waves at various angles towards the seabed. Usually, seabed clustering is performed from data collected by MBES during sea survey trips while single-beam echo sounders (SBES) are usually employed for the measurement of bathymetry (depth). However, it is believed that SBES time series data also contains useful information for seabed type analysis due to its ability to achieve deeper penetration of the seabed substrate.

  • Challenges of SBES clustering

    • Influence of environmental factors (salinity, sea water temperature, bathymetry, slope);

    • Characteristics of SBES echo which are frequency-dependent;

    • Operational limitations (automatic gain control, ship movement).

  • Ground truthing

  • All existing clustering approaches require extensive ground truthing (such as grabbing, coring and visual inspection) which are not practical in deep waters. By adopting a statistical approach, the objective is to perform multi-frequency clustering as an alternative to extensive ground truthing prior to label validation. In this study, features from three frequencies (12, 38, 200 kHz) are collected, spatially filtered and normalised before clustering.

Clustering

PCA with k-means is the industry standard for clustering seabed data. However, it is less effective at dealing with non-Gaussian clusters and requires the number of clusters to be specified a priori. In this work extensions of quality thresholding approaches, including local quality thresholding (QT local) and max-separation clustering (MSC) are developed. These have several advantages over k-means, including automatic cluster number determination and robustness to outliers.

Results and future work

  • Clustering on features derived from optimally selected intervals yields more distinctive and continuous (and hence geologically plausible) clusters than if the full time-series intervals are employed.

  • Preliminary results suggest that the best performance is obtained by employing QT local clustering with the optimally selected intervals.

  • Overall, data fusion using multi-frequency SBES data improves information richness and data quality control improves the reliability of clustering results and provides a more geologically realistic interpretation of the survey area.

  • Future work:

    • Improving the computation and memory efficiency of the QT local and MSC clustering algorithms

    • Determination of an optimum feature set from SBES time-series for seabed classification

Methodology

Semi-automatic statistical approach

To achieve consistent and reliable classification, each processing step needs to be carefully assessed. Since SBES data contains less geological information and redundancy for quality assurance compared to MBES data, additional measures need to be taken to ensure the quality of the raw data. The pre-processing stage involves further expert inspection for data integrity, initial ‘cleanup’ to mitigate the effects of systematic deviations of sonar measurements and spatial sampling to improve the signal-to-noise ratio.

Seabed detection

Two approaches are employed. The first assumes the seabed is located at the peak amplitude of each echo time series return. The second method estimates the location of the seabed by spatially smoothing the bathymetry using a second-order Butterworth filter. Utilising both bathymetric approaches allows the detection of ‘bad’ data samples.

Research presented in this poster was funded by a Strategic Research Cluster Grant (07/SRC/I1168) by Science Foundation Ireland under the National Development Plan. The authors gratefully acknowledge this support.


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