Authors: Giuseppe Masetti1*, Jean-Marie Augustin2, Xavier Lurton2, Brian R. Calder3 1. CCOM/JHC, University of New Hampshire, Durham, NH, USA, gmasetti@ccom.unh.edu 2. Institut Français de Recherche pour l’Exploitation de la Mer (Ifremer), Brest, France 3. CCOM/JHC, University of New Hampshire, Durham, NH, USA An objective measurement of the bathymetric uncertainty introduced by sonar bottom detection has been proposed (Lurton and Augustin, 2009) to overcome the sonar-specific heuristic solutions proposed by constructors. This approach pairs each sounding with an estimation of sonar detection uncertainty (SDU) based on the width of the signal envelope (amplitude detection) or the noise level of the phase ramp (phase detection), thus capturing the intrinsic quality of the received signal and any applied signal-processing step. Along with the environment characterization and the motion sensor accuracy, the SDU represents a major contributor to the total vertical uncertainty (TVU). As such, the monitoring of the SDU statistics by detection types, acquisition modes, and transmission sectors (when available) provides an effective way to alert the surveyor about ongoing issues in the data collection. It also has potential application in the evaluation of the health status of the sonar - for example, by comparing SDU-derived performance of repeated surveys on the same seafloor area and estimating the uncertainty contributions from environment and motion. Finally, the SDU may be integrated in multiple stages of the data processing workflow, from data pre-filtering to hydrographic uncertainty modeling, up to more advanced applications like hypotheses disambiguation in statistical gridding algorithms (e.g., CUBE). Based on such considerations, we conducted a study to explore possible applications of the estimated SDU values for survey quality control and data processing. The results of the analysis applied to real data – collected using multibeam echosounders from manufacturers who are early adopters of this metric (i.e., Kongsberg Maritime and Teledyne Reson) – provide evidence that SDU is a useful tool for survey monitoring.

1. APPLICATIONS OF SONAR DETECTION UNCERTAINTY
FOR SURVEY QUALITY CONTROL AND DATA PROCESSING
G. MASETTI, J.-M. AUGUSTIN, X. LURTON, B.R. CALDER
OCTOBER 1, ST. JOHN’S, NL, CANADA

2. IN BRIEF
Background Current Future
• Uncertainty
modeling
• IFREMER’s
Quality Factor
• Validation of IQF
implementations
• TPU model
extension
• Next challenges
and coming
tools
2

3. SURVEY DATA QUALITY
▪ Trend toward automation
3
▪ Trend toward automation → OCS/CCOM QC Tools
APP AVAILABLE AT: www.hydroffice.org/qctools/main

4. SURVEY DATA QUALITY
4
▪ Trend toward automation → OCS/CCOM QC Tools
▪ Standard-verification QC/QA procedures based on:
▫ Density/Coverage
▫ Total Propagated Uncertainty

5. SURVEY DATA QUALITY
5
▪ Trend toward automation → OCS/CCOM QC Tools
▪ Standard-verification QC/QA procedures based on:
▫ Density/Coverage
▫ Total Propagated Uncertainty
▪ Hare-Godin-Mayer Model (1995 + later improvements)
▫ Theoretical/empirical modelling of components
▫ From component measures to depth/position
▫ Approximated TPU using variance propagation

6. 6
MBES SOURCES
OF UNCERTAINTY
SENSORSPLATFORMENVIRONMENT CALIBRATION INTEGRATION
SOUND SPEED
TIDES
SEA STATE
…
SETTLEMENT
AND SQUAT
SENSORS
OFFSETS
STATIC DRAFT
…
NAV
MRU
SONAR
…
SONAR/MRU
STATIC PITCH
SONAR/MR
STATIC YAW
SONAR/MRU
STATIC ROLL
…
SONAR/NAV
TIME SYNC
NAV/MRU
TIME SYNC
SONAR/MRU
TIME SYNC
…

7. 7
MBES SOURCES
OF UNCERTAINTY
SENSORSPLATFORMENVIRONMENT CALIBRATION INTEGRATION
SOUND SPEED
TIDES
SEA STATE
…
SETTLEMENT
AND SQUAT
SENSORS
OFFSETS
STATIC DRAFT
…
NAV
MRU
SONAR
…
SONAR/MRU
STATIC PITCH
SONAR/MR
STATIC YAW
SONAR/MRU
STATIC ROLL
…
SONAR/NAV
TIME SYNC
NAV/MRU
TIME SYNC
SONAR/MRU
TIME SYNC
…

8. SMARTMAP
8
WEB GIS ACCESSIBLE AT: www.hydroffice.org/smartmap/
▪ RTOFS +
WOA13
▪ Seven days
Predictions
▪ Past analyses

9. 9
MBES SOURCES
OF UNCERTAINTY
SENSORSPLATFORMENVIRONMENT CALIBRATION INTEGRATION
SOUND SPEED
TIDES
SEA STATE
…
SETTLEMENT
AND SQUAT
SENSORS
OFFSETS
STATIC DRAFT
…
NAV
MRU
SONAR
…
SONAR/MRU
STATIC PITCH
SONAR/MR
STATIC YAW
SONAR/MRU
STATIC ROLL
…
SONAR/NAV
TIME SYNC
NAV/MRU
TIME SYNC
SONAR/MRU
TIME SYNC
…
SONAR

10. 10
MBES SOURCES
OF UNCERTAINTY
SENSORSPLATFORMENVIRONMENT CALIBRATION INTEGRATION
SONAR
ANGLE
RANGE
…

11. (𝑦, 𝑧)
BATHYMETRIC DETECTION
▪ angle 𝜃
▪ range 𝑅 =
𝑐𝑡
2
▪ 𝑦 = 𝑅 × sin 𝜃
▪ 𝑧 = 𝑅 × cos 𝜃
𝜃
𝑅

12. BATHYMETRIC DETECTION
▪ angle 𝜃
▪ range 𝑅 =
𝑐𝑡
2
▪ 𝑦 = 𝑅 × sin 𝜃
▪ 𝑧 = 𝑅 × cos 𝜃
▪ MBES
▫ 𝜃 fixed
▫ 𝑡 measured
▫ amplitude
▫ phase
▪ ISSS
▫ 𝑡 fixed
▫ 𝜃 measured
▫ phase

13. IFREMER’S QUALITY FACTOR (IQF)
▪ A bathymetry quality estimator:
▪ Proposed to the community in 2010
▪ Implemented by a few manufacturers
(e.g., Kongsberg, Reson)
▪ Easy conversion to the more intuitive
Sonar Detection Uncertainty:
▫ Meters [m]
▫ Percentage of water depth [%WD]
𝐼𝑄𝐹 = log10
𝑧
𝛿 𝑧
= log10
𝑡
𝛿𝑡

14. WHY ANOTHER QUALITY ESTIMATOR?
14
▪ IQF provides a better approach:
▫ Objective determination
▫ Quantitative evaluation of the detection
▫ Manufacturer independent
▪ Existing estimators for bathymetric detection:
▫ Obscure definitions
▫ Only indicative of the signal quality
▫ Valid for a particular system or family

15. BATHYMETRIC DETECTION
15
▪ Amplitude Processing
▪ Phase Processing
𝑡
𝐴
𝑡 𝐷
𝑡
ΔΦ ΔΦ = 0
⟹ Center of Gravity
⟹ Zero-crossing of phase-ramp

16. PHASE PROCESSING
▪ Uncertainty increases with noise
16
𝛿ΔΦ
𝛿 𝜃 𝛿𝑡
𝛿 𝑧
𝑧
= tan 𝜃 × 𝛿 𝜃
𝛿 𝑧
𝑧
=
𝛿𝑡
𝑡

17. PHASE PROCESSING
▪ Improvement by N-averaging
17
𝛿ΔΦ = 1
𝑁−1 𝑑
+ 𝑁
2 𝑁−1 𝑁−2 𝑑2
▪ For a fluctuating signal:

18. PHASE PROCESSING
▪ Improvement by N-averaging
18
𝛿𝑧
𝑧
∝ 𝛿ΔΦ
𝑵
∝
1
𝑁
▪ For a fluctuating signal and large N:
▪ Dilemma: ⇑ 𝑁
⇑ accuracy
⇓ resolution

19. PHASE PROCESSING ⟹ IQF
▪ The time accuracy relates to:
▫ Phase fluctuation (𝛿ΔΦ)
▫ Phase slope (𝑆)
19
𝐼𝑄𝐹Φ = log10
𝑡 𝐷
𝛿𝑡 𝐷
= log10
𝑡 𝐷 × 𝑆 × 𝑁
𝛿ΔΦ
𝑡
ΔΦ
𝛿𝑡 𝐷
𝛿ΔΦ
𝑡 𝐷
𝑆

20. AMPLITUDE PROCESSING
▪ Center of gravity over N samples
20
𝛿𝑡 𝐷
= 4−𝜋
𝜋 × 𝑁 2𝑁−1
6 𝑁−1 𝑓𝑠
2 ≅ 0.305 𝑁
𝑓𝑠
▪ E.g., for a square-envelope signal:
𝑡
𝐴
𝑡 𝐷
𝑁

21. AMPLITUDE PROCESSING ⟹ IQF
▪ The time accuracy relates to:
▫ Sampling Period (Ts)
▫ Signal Shape Factor (B)
21
𝐼𝑄𝐹𝐴 = log10
𝑡 𝐷
𝛿𝑡 𝐷
= log10
𝑡 𝐷
𝐵𝑇𝑠 𝑁
𝑡
𝐴
𝑡 𝐷
𝑁

22. CURRENT
Validation and TPU model extension
22

23. IQF SIMULATION
▪ Function of:
▫ Head shape
▫ TX Frequency
▫ Sampling Frequency
▫ Phase-Ramp Length (p-r)
▫ Height Value (H)
▫ Pulse Length (T)
▫ Signal-to-Noise Ratio (SNR)
▫ ….
23

24. IQF SIMULATION
▪ Function of:
▫ Head shape
▫ TX Frequency
▫ Sampling Frequency
▫ Phase-Ramp Length (p-r)
▫ Height Value (H)
▫ Pulse Length (T)
▫ Signal-to-Noise Ratio (SNR)
▫ ….
24

25. IQF SIMULATION
▪ Function of:
▫ Head shape
▫ TX Frequency
▫ Sampling Frequency
▫ Phase-Ramp Length (p-r)
▫ Height Value (H)
▫ Pulse Length (T)
▫ Signal-to-Noise Ratio (SNR)
▫ ….
25

26. IQF SIMULATION
▪ Results match common intuition
▫ ⇑ 𝑆𝑁𝑅 ⟼ ⇑ 𝐼𝑄𝐹
▫ ⇑ 𝑇 ⟼ ⇓ 𝐼𝑄𝐹
▪ 𝐼𝑄𝐹Φ:
▫ Better than 𝐼𝑄𝐹𝐴 (except nadir)
▫ Optimal at oblique incidence
▪ 𝐼𝑄𝐹𝐴:
▫ Higher at nadir
▪ Critical Points:
▫ Swath ends
▫ 𝐼𝑄𝐹Φ-𝐼𝑄𝐹𝐴 junctions
26

27. IQF WITH REAL DATA (2009)
▪ Works by Ladroit and Calder (2009)
▪ July 2008 – Reson Seabat 7111 – RV Pourquoi pas?
27

28. IQF WITH REAL DATA (2009)
▪ Good correlation with hand-cleaned data, but …
▪ 𝐼𝑄𝐹 only deals with sonar/signal part of the sonar uncertainty!
▪ 𝐼𝑄𝐹 cannot:
▫ Detect phase ambiguities
▫ Account for specular return or interferences
▫ … 28

29. IQF WITH REAL DATA (2018)
▪ More stable manufacturer implementations
▪ Datasets for a variety of environments/models
29

30. IQF WITH REAL DATA (2018) - MARIANAS
30

31. IQF WITH REAL DATA (2018) - TOULON
31

32. IQF WITH REAL DATA (2018) - TOULON
32
SECTOR BOUNDARY

33. IQF WITH REAL DATA (2018) - TOULON
33
▪ By using a reverse-engineering method.
FIX FIX

34. IQF WITH REAL DATA (2018) -
34
MARIANAS

35. IQF WITH REAL DATA (2018) - MARIANAS
35
FIX FIX

36. HOW TO R2O? THUMBS (ALPHA)HOW TO R2O?
▪ An app to manage:
▫ The Total Hydrographic Uncertainty Modeling for
Bathymetric Surveys.
▫ A SQLite database of parameters for sonars, sensors,
environments, and hydrographic standards.
▫ The storage/retrieval/comparison of different survey
scenarios and related uncertainty budgets.
▫ The derivation of a Sonar Detection Uncertainty from
collected IQF values.
36

37. 37
HOW TO R2O? THUMBS (ALPHA)

38. 38
HOW TO R2O? THUMBS (ALPHA)

39. 39
HOW TO R2O? THUMBS (ALPHA)

40. 40
HOW TO R2O? THUMBS (ALPHA)

41. 41
HOW TO R2O? THUMBS (ALPHA)

42. FUTURE
Next challenges …
42

43. NEXT CHALLENGES …
▪ Survey Uncertainty Tool (aka, Thumbs)
▪ Real-time Monitoring Tool
▪ CUBE/CHRT Disambiguation Method
43
… and your ideas !!!

44. REFERENCES
• Hare, R.; Godin, A.; Mayer, L. Accuracy estimation of Canadian swath (multibeam) and sweep (multitransducer)
sounding systems. Canadian Hydrographic Service and University of New Brunswick Publication, Fredericton 1995.
• Hare, R.; Eakins, B.; Amante, C. Modelling bathymetric uncertainty. The International Hydrographic Review 2011, 6.
• Masetti, G.; Kelley, J.G.W.; Johnson, P.; Beaudoin, J. A Ray-Tracing Uncertainty Estimation Tool for Ocean
Mapping. IEEE Access 2018, 6, 2136-2144, https://doi.org/10.1109/ACCESS.2017.2781801.
• Lurton, X.; Augustin, J.; Ladroit, Y. Definition of a Quality Factor for MBES bathymetry processing, Advances in
Seafloor Mapping Sonar, Brest, France, 30 November - 1 December 2009, 2009; Brest, France.
• Lurton, X.; Augustin, J. A measurement quality factor for swath bathymetry sounders. IEEE JOE 2010, 35, 852-862.
• Lurton, X.; Ladroit, Y.; Augustin, J. A quality estimator of acoustic sounding detection. IHR 2010, 4.
• Ladroit, Y.; Lurton, X.; Sintès, C.; Augustin, J.; Garello, R. In Definition and application of a quality estimator for
multibeam echosounders, Oceans, 2012; 1-7.
• Ladroit, Y.; Lurton, X.; Sintès, C.; Garello, R. In Maximum likelihood estimator based on Quality Factor for
bathymetric multibeam echosounder, Oceans, 14-19 Oct. 2012, 2012; 1-4.
• Ladroit, Y. Improvement of soundings detection and qualification methods for bathymetric multibeam echosounders.
Télécom Bretagne, Université de Rennes 1, 2012.
• Gutierrez, F.J. Real-time sounding uncertainty estimation in phase measuring bathymetric sonars, Hydro14
Conference, Aberdeen, UK, 28-30 October 2014, Aberdeen, UK.
• Mohammadloo, T.H.; Snellen, M.; Simons, D.G. Multi-beam echo-sounder bathymetric measurements: Implications
of using frequency modulated pulses. JASA 2018, 144, 842-860, 10.1121/1.5050816.

45. THANKS!
Any questions?
You can contact me at: gmasetti@ccom.unh.edu