1. Uncertainty Workshop: Sounding attributes
Rob Hare
Manager, Hydrographic Surveys
Canadian Hydrographic Service, Pacific Region
CHC2004 May 24, 2004

2. Abstract
A discussion of attributes on soundings, sources of error and computation of sounding error.
Plus a whole lot more
An error prediction tool along with the characteristics and capabilities of (a few) sounding systems will be reviewed.

3. Data Quality Elements Completeness Logical consistency
Positional accuracy
Has horizontal and vertical components
Sub-elements: absolute accuracy, attribute completeness, shape fidelity, time accuracy, topologic consistency
Temporal accuracy
Thematic accuracy

4. Estimation of Quality metrics
Direct and indirect methods
Require absolute coordinate reference frame (e.g. WGS-84)
Most direct methods impractical
source inter-comparison is an exception
Indirect methods require validation
e.g. deductive error estimation (forward error prediction)

5. Statistical estimators
Measures of central tendency
Sample mean
Median, Mode
Measures of dispersion (1-D and 2-D)
Standard deviation (or variance), RMS
CEP, MSEP, drms
Total Propagated Error (TPE)
CAUTION - many hydrographic measurements are correlated (e.g. H&V components of soundings)

6. Why estimate error/uncertainty?
Preanalysis
Will my system meet specifications?
Do I purchase a new … C/B analysis?
Real-time QA
Am I collecting enough data to meet specifications?
Do I modify my sampling/processing strategy, discard outer beams, increase overlap, take more sound speed profiles, etc.?
Post-mission assessment
Did I meet specifications? Classification - what Order did I achieve?
Provide metadata for informed decision making/risk assessment
Data attribution for integration/validation/comparison of different data sets
Assessment of historic sources
Initialization of CUBE estimator?
Create Source Classification or Reliability Diagrams or ZOCs

7. Part 1 - The VERTICAL

8. Absolute Vertical Accuracy
Estimates of vertical precision
Errors common to all vertical measurements
Errors common to GPS vertical measurements
Depth soundings
Shoal examinations
Drying heights
Elevations and clearances

9. Depth soundings Leadline, sounding pole, rod
Single-beam echosounder - SBES
analogue sounders
digital sounders
Sweep (multi-transducer)
Lidar
Swath (multibeam echosounder - MBES)
Other – e.g. TIBS, wire or bar sweep, diver

10. Sources of error - water levels
gauge measurement precision
method of filtering sea surface waves
timing synchronisation of gauge and measurement
vertical datum precision
spatial extrapolation to the location of the vertical measurement, or in the case of predictions,
quality of constituent set (length/quality of observations)
correction for environmental effects

11. Other sources of vertical error
Draft and squat or settlement
Antenna to transducer offset (GPS)
Datum separation model (GPS)
Sounding measurement
Heave
Sound speed

12. Traditional sounding reduction
D = d + draft – WL
d = r cos (θ+R) cos P
r = range
Θ = beam angle
R = Roll angle
P = Pitch angle
Dynamic draft
Charted
Depth, D
Chart datum
Measured
Depth, d
Tide, WL θ r

13. RTK GPS sounding reduction
D = d + K – Z – M
K = Δx sinP
+ Δy cosP sinR
+ Δz cosP cosR
Dynamic draft
Charted
Depth, D
Chart datum
Measured
Depth, d
Tide, WL θ r
Ellipsoid
GPS RTK, Z
Separation
Model, M
Antenna
Height, K

14. Vertical error propagation

15. SBES error sources depth measurement sound speed correction method
algorithm, frequency, beamwidth, pulse length
sound speed correction method
draft (and squat if applied)
heave (measured)
tides
manual trace reading, resolution, recording and reduction method

16. Lidar error sources Depth measurement
Refraction correction (calibration)
Sea-surface modelling
Tides
Footprint spreading

17. MBES error sources Range and beam angle measurement
detection method (amplitude or phase)
Refraction correction
Dynamic draft
includes squat, settlement, change of trim
Heave (measured and induced)
Tides or water levels
Roll, pitch, heading
Calibration (patch test) offsets
Roll, pitch, heading, sensor latency
Positioning system

18. Alignment errors

19. MBES coordinate systems

20. Mapping depth errors

21. Confidence levels
For a normal distribution, the probabilities of univariate random errors of a single measurement falling within a certain level of error (number of standard deviations, ) are given in the following table.

22. Depth error estimates

23. Additional vertical corrections
sounding datum adjustments
metric conversion
may include generation of metric contours
sound speed corrections

24. Potential limitations
heave estimation (manual)
phase lag or latency
sound speed changes
manual trace reading
vertical display resolution
stepped vertical datum zones
shoal biasing
datum separation models

25. Part 2 - The HORIZONTAL

26. Absolute Horizontal Accuracy
Estimates of horizontal precision
Field sheet processes
Soundings
Shoal examinations
Other data types
Heights, elevations, clearances
Navigational Aids
Shoreline
Bathymetric contours
Cartographic processes (including digitizing)

27. Sounding Position Equations

28. Estimates of precision
Rigorous - error ellipses
HDOP
MSEP
CEP
CSE
drms

29. Errors in Multibeam soundings
GPS or POS/MV position error
transducer - antenna offsets
sounder measurement error
range, beam angle, detection method
refraction correction error
transducer shape, orientation, roll-modulation
roll, pitch and heading error
latency between systems
GPS, sounder, VRU

30. Mapping position errors

31. Confidence levels

32. Position error estimates

33. Field Sheet processes Horizontal datum Manual processes Data types
Materials and construction
Horizontal control
Station plotting
Data types
Soundings
Shoal examinations, drying heights
Contours
Navigational aids
Shoreline (natural and man-made)
Seafloor samples
Topography, elevations, clearances
These are scale-dependent errors

34. Soundings - sources of error
Positioning system/method
Sounding system
including offsets in space and time
Plotting
Inking
Digitizing

35. Positioning systems/methods
Manual/Optical
Sextant (eccentric circle LOP)
Subtense (eccentric circle LOP)
Range poles (straight-line LOP)
Azimuth (straight-line LOP)
Electronic positioning systems (EPS)
Range-bearing (hybrid - circle and line)
Two-range or range-range (later multi-range) - concentric circle LOP
Hyperbolic (2 and later multi-hyperbola) LOP
Transit satellite (Doppler) - spherical LOP
GPS (spherical LOP)
DGPS
OTF

36. Plotting, Inking, Digitizing errors
3-arm protractor
lattices
collector registration
fixes and ‘tweeners’
Inking
on collector
on field sheet
registration
Digitizing
digitizer resolution
registration (rms of fit on HCP) - transformation used
datum shift
projection
These are scale-dependent errors

37. Systematic error sources (safety biasing)
Generalization
Line smoothing
Symbolization
Feature displacement
soundings
bottom samples (more for clarity than safety)

38. Drafting (from final compilation manuscript)
Control points
Linework (shoreline)
Soundings
Symbols
Contours
Bottom samples

39. Digitizing Registration/rectification method
Digitizer precision, resolution
Optical centre recognition
Line-following ability
QC tolerances (chart specifications)

40. Sources, products and points of digitization

41. Data inter-comparisons: validating error prediction assumptions
Methods
compare same data set at different process stages
difference DTM (soundings)
inter-comparison of point features (shoal examinations, drying heights)
linework comparisons (HWL, LWL, contours)
validate inference methods (expected vs. actual)
Limitations
small statistical sample
is it the same feature?
separation of depth from position error

42. Quality Implementations
Data collection - ASCII, Simrad, Hypack, NMEA
Data processing - HIPS (Quality flag, coverage, standard deviation), CUBE (stochastic surface, etc.)
Data storage - Caris ASCII, GSF, SDS, …
Paper Charts - Source Classification/Reliability Diagrams, Explanations in Sailing Directions/Pilots
ENC - S-57 objects and meta-objects
(M_QUAL, M_ACCY, CATZOC, CATQUA, QUASOU, TECSOU, POSACC)
Raster Charts?

43. Summary
Depth and position errors can be estimated using forward error prediction
These estimates can be validated by inter-comparison of data sets
Estimates can be used:
To make decisions regarding equipment selection and purchase
To determine if specifications can be/have been met
To adapt your sampling strategy
As an input to statistical processing algorithms
These are but two of many Quality/Uncertainty Measures