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

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.

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

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)

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)

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

Part 1 - The VERTICAL

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

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

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

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

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

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

Vertical error propagation

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

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

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

Alignment errors

MBES coordinate systems

Mapping depth errors

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.

Depth error estimates

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

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

Part 2 - The HORIZONTAL

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)

Sounding Position Equations

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

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

Mapping position errors

Confidence levels

Position error estimates

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

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

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

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

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

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

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

Sources, products and points of digitization

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

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?

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