Marine Geophysical Research

, Volume 38, Issue 3, pp 291–301 | Cite as

Automated cleaning and uncertainty attribution of archival bathymetry based on a priori knowledge

  • Rodney Wade Ladner
  • Paul Elmore
  • A. Louise Perkins
  • Brian Bourgeois
  • Will Avera
Original Research Paper


Hydrographic offices hold large valuable historic bathymetric data sets, many of which were collected using older generation survey systems that contain little or no metadata and/or uncertainty estimates. These bathymetric data sets generally contain large outlier (errant) data points to clean, yet standard practice does not include rigorous automated procedures for systematic cleaning of these historical data sets and their subsequent conversion into reusable data formats. In this paper, we propose an automated method for this task. We utilize statistically diverse threshold tests, including a robust least trimmed squared method, to clean the data. We use LOESS weighted regression residuals together with a Student-t distribution to attribute uncertainty for each retained sounding; the resulting uncertainty values compare favorably with native estimates of uncertainty from co-located data sets which we use to estimate a point-wise goodness-of-fit measure. Storing a cleansed validated data set augmented with uncertainty in a re-usable format provides the details of this analysis for subsequent users. Our test results indicate that the method significantly improves the quality of the data set while concurrently providing confidence interval estimates and point-wise goodness-of-fit estimates as referenced to current hydrographic practices.


Hydrography Bathymetry Historical data Data cleaning 



We would like to thank the Office of Naval Research for sponsoring this research under the Worldwide High-Resolution Bathymetry project of the Naval Research Laboratory’s Base Program.


  1. Becker JJ, Sandwell DT, WHF Smith, Braud J, Binder B, Jl Depner, Fabre D et al (2009) “Global bathymetry and elevation data at 30 Arc seconds resolution: SRTM30_PLUS”. Mar Geodesy 32(4):355–371. doi: 10.1080/01490410903297766 CrossRefGoogle Scholar
  2. Bourgeois, B, Elmore P, Avera W, Zambo S (2016) “Achieving comparable uncertainty estimates with kalman filters or linear smoothers for bathymetric data”. Geochem Geophys Geosyst 17:2576–2590. doi: 10.1002/2015GC006239 CrossRefGoogle Scholar
  3. Calder BR (2006) “On the uncertainty of archive hydrographic datasets”. IEEE J Oceanic Eng 312:249–265. doi: 10.11109/JOE.2006.872215 CrossRefGoogle Scholar
  4. Calder BR, Garder JV (2008) “U.S. Law of the sea cruise to map the foot of the slope of the Northeast U.S. Atlantic Continental Margin: Leg 6”, University of New Hampshire (UNH), Center for Coastal and Ocean Mapping (CCOM)/Joint Hydrographic Center (JHC). Accessed 10 Mar 2017
  5. Calder B, Mayer LA (2003) “Automatic processing of high-rate, high-density multibeam echosounder data”. Geochem Geophys Geosyst (American Geophysical Union) 4(6):1048. doi: 10.1029/2002GC000486.
  6. Chandler MT, Wessel P (2008) “Improving the quality of marine geophysical track line data: along-track analysis”. J Geophys Res Solid Earth 113(B2):B02102. doi: 10.1029/2007JB005051 CrossRefGoogle Scholar
  7. Cleveland WS (1979) “Robust locally weighted regression and smoothing scatterplots”. J Am Stat Assoc 74:829–836CrossRefGoogle Scholar
  8. Cleveland WS, Devlin SJ (1988) “Locally-weighted regression: an approach to regression analysis by local fitting”. J Am Stat Assoc 83:596–610. doi: 10.2307/2289282 CrossRefGoogle Scholar
  9. Debese N, Moitie R, Seube N (2012) “Multibeam echosounder data cleaning through a hierarchic adaptive and robust local surfacing”. Comput Geosci 46:330–339. doi: 10.1016/j.cageo.2012.01.012 CrossRefGoogle Scholar
  10. Doornik, JA (2011) Robust estimation using least trimmed squares. economic department, Oxford University, Oxford, United Kingdom: Economics Department, University of Oxford.
  11. Draper N, Smith H (1981) Applied regression analysis, 2nd edn. Wiley, New YorkGoogle Scholar
  12. Elmore P, Fabre D, Sawyer R, Ladner R (2012) “Uncertainty estimation of historical bathymetric data from bayesian networks”. Geochem Geophys Geosyst 13 (11). doi: 10.1029/2012GC004144
  13. Free Software Foundation (2015) GSL—GNU scientific library, Version 2.1. Accessed May 15 2016
  14. Galassi M, Davies J, Theiler J, Gough R, Jungman G, Alken P, Booth M, Rossi F (2009) GNU scientific library reference manual, 3rd edn. Network Theory Ltd, LondonGoogle Scholar
  15. Golub GH, Reinsch C (1970) “Singular value decomposition and least squares solutions”. Numer Math 14:403–420. doi: 10.1007/BF02163027 CrossRefGoogle Scholar
  16. Hare R (1995) “Depth and position error budgets for multibeam echosounding”. Int Hydrogr Rev 72:37–69.
  17. Hare R, Eakins B, Amante C (2011) “Modelling bathymetric uncertainty”. Int Hydrogr Rev 6:31–42. Accessed 31 May 2016
  18. IHO (2008) IHO standards for hydrographic surveys: special publication No. 44 Fifth edition. International Hydrographic Bureau, Monaco, p 28Google Scholar
  19. Jakobsson M, Mayer LA, Coakley B, Dowdeswell JA, Forbes S, Fridman B, Hodnesdal H et al. (2012) “The international bathymetric chart of THE arctic ocean (IBCAO) version 3.0.” Geophys Res Lett. doi: 10.1029/2012GL052219
  20. JCGM (2008) “Evaluation of measurement data—Guide to the expression of uncertainty in measurement.” September. Accessed May 31 2016.
  21. Ladner R, Moseley J (2002) “Managing heterogeneous databases to support diverse product requirements”. OCEANS ‘02 MTS/IEEE. IEEE. 895–899. doi: 10.1109/OCEANS.2002.1192086
  22. Lawson CL, Hanson RJ (1995) Solving least squares problems. S.I.A.MGoogle Scholar
  23. Mount DM, Netanyahu NS, Piatko CD, Silverman R, Wu AY (2014) “On the least trimmed squares estimator”. Algorithmica 69:148–183. doi: 10.1007/s00453-012-9721-8 CrossRefGoogle Scholar
  24. Press WH, Teukolsky SA, Vetterling WT, Flannery BP (2007) Numerical recipes—the art of scientific computing. 3rd. Cambridge University Press, New YorkGoogle Scholar
  25. Rousseeuw PJ, Leroy AM (1987) Robust regression and outlier detection. Wiley, New YorkCrossRefGoogle Scholar
  26. Ryan WBF, Carbotte SM, Coplan JO, O’Hara S, Melkonian A, Arko R, Weissel RA et al (2009) “Global multi-resolution topography synthesis.” Geochem Geophys Geosyst. doi: 10.1029/2008gc002332 Google Scholar
  27. Smith WH, Sandwell DT (1997) “Global sea floor topography from satellite altimetry and ship depth soundings.” Science 277. doi: 10.1126/science.277.5334.1956
  28. Taylor BN, Kuyatt CE (1994) “NIST technical note 1297 guidelines for evaluating and expressing the uncertainty of NIST measurement results”. September. Accessed May 31, 2016
  29. Weatherall P, Marks KM, Jakobsson M, Schmitt T, Tani S, Arndt JE, Rovere M, Chayes D, Ferrini V, Wigley R (2015) “A new digital bathymetric model of the world’s oceans”. Earth Space Sci. doi: 10.1002/2015EA000107
  30. Weber JR (1983) “Maps of the arctic basin sea floor: a history of bathymetry and its interpretation”. Arctic 36 (2):121–142.
  31. Wessel P, Chandler MT (2011) “The spatial and temporal distribution of marine geophysical surveys”. Acta Geophys 59(1):55–71. doi: 10.2478/s11600-010-0038-1 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht (outside the USA) 2017

Authors and Affiliations

  1. 1.Hydrographic DepartmentU.S. Naval Oceanographic OfficeStennis Space CenterUSA
  2. 2.Marine Geosciences DivisionU.S. Naval Research LaboratoryStennis Space CenterUSA
  3. 3.Gordon and Jill Bourns College of EngineeringCalifornia Baptist UniversityRiversideUSA

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