Sediment classification usingsub-bottom profiler

Mohamed Saleh

Outline

• Introduction

i. Why classification

ii. Subbottom profiler

iii. Dataset description

iv. Classification methods

• High frequency classification

• Low frequency classification

• Conclusions and recommendations

Why classification?Applications:

• Offshore projects need seabed maps and information about sediment type

Examples:

• Offshore engineering

− Harbours

− Oil platforms

− Wind farms

• Dredging

(dredged material are primarily related to the type of dredging equipment)

• Morphological studies

(develop sediment transport models to predict changes of the morphology of the riverbeds and seabeds)

Classification methods

• Grab samples- Costly and time consuming surveys

• Acoustic Remote Sensing- Cost effective

- High spatial coverage

- Non destructive analysis Box core

Seafloor acquisition techniques

SBES,SBP,SEISMICSingle beam echo sounder (SBES)

- Operating frequency : +/- 100kHz

- Observation zone: seabed surface

- Applications : fundamental sensor for

seafloor mapping

Marine seismic (impulsive, sparkers, boomers)

- Operating frequency : 5000Hz – 50 Hz

- Observation zone: deeper than 100m up to several kilometers

- Applications : geophysical mapping (oil and gas exploration)

Sub-bottom profiler

- Operating frequency : 1kHz – 30kHz

- Observation zones : near surface (1-100m)

- Applications : offshore engineering projects

Non-linear acoustics

Working principle:Emit 2 or more +/-100kHz (Primary)to produce 15,10, 5 kHz (Secondary)

F = |105-100| = 5kHz

Advantages:Compact dimension r ~25cm that can emit low

frequencies.

High horizontal resolution due to the very narrow aperture angle ~ ±1.8° for all frequencies

(very narrow comparing to SBES 30 °).

High vertical resolution ~ 5-10 cm due to very short pulse length.

Beam width

Sub-bottom profiler

Two sources interference

Dataset- The data was acquired in the Baltic sea near Rostock-Germany in 2007.

- The image shows a set of amplitude pressures that corresponds to layers contrast

area1

area3

area2

area4

Dataset

High frequency: to determine accurate water depth.

(High frequency can not penetrate into deep layer due to high sediment attenuations)

Low frequency: to observe the seabed layers.

Area1, 100kHz Area1, 5kHz

Signal anatomy (High freq)

The received signal is a composite surface and volume

backscatter

Soft sediment echo- Low amplitude- Large component of volume

scatter

Rough sediment echo- High amplitude- Large component of surface

backscatter

Trailing edge due to scattering and reflections from Subbottom

Leading edge due to initial reflection from seabed

Classification approaches (Phenomenological, Model based)

• Phenomenological

Extract features of received echo (time spread, energy, skewness) that allows to discriminate between different sediment classes.

Principle

1 - Correct for echoes for depth effect.

2 - Extract features from the corrected echoes.

3 - PCA (combination of all extracted features).

4 - Clustering the sets of principle components corresponds to number of sediments types.

Model basedModel based

Employ a physical model that predicts the received echo shape.

Principle

1 – For each ping, a signal is modeled

2 – Input parameters:

Transmitted signal (pulse duration, power etc..)

Transmission loss

Directivity

Sediment backscatter

3 – Search for the mean grain size that maximize the match between modeled and observed echo.

High frequency analysis

Post processing

- Filtering

- Stacking

- Alignment

Classification (Model based)

- SONAR equation

- matching process

Classification results

Stacking

The stacking process is essential to:

- Remove the heave variability to isolate the seabed type.

- Decrease the envelope stochastic variability to improve the matching process.

- Suppress the ping to ping variability by stacking consecutive echoes allows the sediment information in the echo shape and spectral nature to express it self.

Waterfall plot

Stacking

Ensemble size has a tradeoff between the spatial resolution and ensemble variance.

Ensemble size is indicated through the correlation matrix that measures the degree similarity of the received echoes.

Correlation matrix

Result: Ensemble size =15 signals

Alignment

The alignment is done with respect to a temporal feature.

Two alignment methods were tested:

- Minimum threshold.(preserves signal properties)

- Maximum alignment(preserves signal integrity)

Echo modelAPL Backscatter model

(angular domain)

+ =

Modeled backscatter (time domain)

Numerical integration over ∆R

Soft sediment

Coarse sediment

Matching process

Iterative loop :- Select the geoacoustic parameters of sediment Mz

- Model the echo envelope of the selected Mz

- Compare function

- Select Mz that corresponds to the minimum E/S

Results (1)

E/S

The classification results using alignment 10%:

1-High model/measurement matching degree for the soft and medium sediments.

2-Low model/matching degree for coarse sediments

3-Good classification consistency with the general description of the area properties except for area4.

Classification result, alignment 10%

Soft

Moderate

Rough

Results (2)

The classification results using alignment 50%:

1-Moderate model/measurement matching degree for the soft sediments.

2-Good model/matching degree for medium and coarse sediments

3-Good classification consistency with the general description of area3 and 4.

4- Poor classification consistency for area1 and 2.

E/S

Classification result, alignment 50%

Soft

Moderate

Rough

E/S

Classification results using alignment 100%:

1-Very poor model/measurement matching degree for the soft sediments.

2-Moderate model/matching degree for medium and coarse sediments

3-No classification consistency with the general description of all areas

Results (3)

Classification result, alignment 100%

Soft

Moderate

Rough

Summary

� soft and medium

� medium

and coarse

Conclusions

Low frequency analysis

The SBES model does not account for Sublayer

interactions.

Signal anatomy (Low freq)

Low frequency echo

- Discrete reflections at contrast layers.

- The short pulse length and very narrow aperture angle ensures to obtain high reflected energies with low backscatter components.

Leading edge due to initial reflection from seabed

Subbottom reflections from moderate contrast

layers

Subbottom reflections from low contrast layers

High reflections from highly contrasted layers

Backscatters

Energy model

15kHz echo

- The received energy ERX is related to the transmitted energy ETX via:

Assumption:- Backscatter is negligible compared to the coherent reflection.

- The aim here is to infer the sediment typefrom its reflection coefficient by comparing it to the modelled reflection coefficients.

1- Surface classification

Energy modelEnergy comparison

1-Reflected energies are correlated with the sediment type.

2-Energy magnitudes are different (scale factor needed).

3- Incomplete energy profile for the SBES model due to the narrow aperture angle (we are missing the backscatter.

4- High energy fluctuations from area4 due to high surface roughness.Energy scaling

1- Five random samples were selected to compute the scale factor C.

2- The reflection coefficients are estimated for the 17 traces using one scale factor via:

3- The estimated reflection coefficients are inverted to the corresponding mean grain size via:

Surface classification

Extended energy model

- Energy model at water sediment interface

- Extended energy model for sub layers

Sample window

Reflection coefficient versus depth is only valid when the secondary reflection is not located within the first sampling window.

The hypothesis here is that if the spectrum of the second sample window is the same or less than the power spectrum of the first sample window then we are at the same sediment layer.

Sample window = 1 pulse width:Very short sample window has low spectral resolution which makes the comparison difficult.

Sample window = 4 pulse width:Long sample window captures secondary reflections. The power spectrum of the second sample is larger than the first sample window.

Sample window

Large sample window unbalances the energy model by over or underestimating the reflection coefficients at deep layers.

Proper sample window equal to twice the transmitted pulse.

Model comparison

Energy model (Simons et al)- stacking is applied on the signal envelope.- more sensible to propagation error.- attenuation is applied on the nominal transmitted frequency.

- reflections are highly correlated to the observed energies.

Energy model (freq-domain)- stacking is applied on the pressure signal- no propagation error due to the nature of the computational algorithm.- attenuation is applied on the observed frequency band.- reflections are less correlated to the observed energies.

Conclusion and recommendations

High frequency

• Calibration of the source level is important to increase the certainty of the matching process.

• Alignment threshold is a crucial factor on classification result.

• Seabed classification using the echo shape is highly influenced by the noise.

• Matching echo features can be tested for the classification such as (amplitude, rise slope, pulse duration) .

•These features are expected to be less influenced by the noise level.

Conclusion and recommendations

Low frequency

• Calibration of source level

• The energy model can be improved by accounting additional physical phenomena such as signal interference and backscatters.

• Error propagation limits the low frequency classification.

Thank you for your attention!