SpringerLink
Log in

Multibeam seafloor topography distortion correction based on SVP inversion

  • Original article
  • Published:
Journal of Marine Science and Technology Aims and scope Submit manuscript

Abstract

Multibeam bathymetric system (MBS) has been widely applied in marine exploration activities for providing high-precision seafloor topography. However, the representative error of sound velocity profile (SVP) can degrade the precision of sounding and lead to the distortion of seafloor topography. To resolve this problem, we propose a seafloor topography distortion correction method based on the SVP inversion. It firstly decomposes the SVP using the empirical orthogonal function (EOF), which converts the SVP inversion process into the time coefficient optimization process. The time coefficient is then optimized by the simulated annealing algorithm (SA) based on a cost function, which is constructed by the sounding consistency of the corresponding beams in the overlap** areas of adjacent swaths. Finally, the inverted SVP is calculated by the optimal time coefficient, and then the distortion of seafloor topography can be corrected by the inverted SVP. Experiments conducted on three types of seafloor topography demonstrate that the root mean square values of sounding errors of the proposed method are 39.44%, 34.49% and 62.32% lower than the method based on reconstructed SVP in flat, inclined, and undulating topographies, respectively. Compared with the method based on replaced SVP, these calculated by the proposed method are reduced by 88.38%, 88.08% and 92.82%, respectively. These results suggest that the proposed method can significantly improve the precision of multibeam sounding, and efficiently correct the distortion of the various seafloor topographies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Hellequin L, Boucher JM, Lurton X (2003) Processing of high-frequency multibeam echo sounder data for seafloor characterization. IEEE J Ocean Eng 28(1):78–89

    Article  Google Scholar 

  2. Gaida TC, Mohammadloo TH, Snellen M et al (2020) Map** the seabed and shallow subsurface with multi-frequency multibeam echosounders. Remote Sens 12(1):52

    Article  Google Scholar 

  3. Amiri-Simkooei A, Snellen M, Simons DG (2009) Riverbed sediment classification using multibeam echo-sounder backscatter data. J Acoust Soc Am 126(4):1724–1738

    Article  Google Scholar 

  4. Imren C, Le Pichon X, Rangin C et al (2001) The North Anatolian Fault within the Sea of Marmara: a new interpretation based on multi-channel seismic and multibeam bathymetry data. Earth Planet Sci Lett 186(2):143–158

    Article  Google Scholar 

  5. Tonchia H, Bisquay H (1996) The effect of sound velocity on wide swath multibeam system data. In: OCEANS 96 MTS/IEEE Conference Proceedings, Fort Lauderdale, FL, USA, 1996, pp 969–974

  6. Yang F, Bu X, Ma Y et al (2017) Geometric calibration of multibeam bathymetric data using an improved sound velocity model and laser tie points for BoMMS. Ocean Eng 145:230–236

    Article  Google Scholar 

  7. Jiabiao Li (1999) Multibeam sounding principles survey technologies and data processing methods. Ocean Press, Bei**g

    Google Scholar 

  8. Jianhu Z, **gnan L (2008) Multibeam sounding and image data processing. Wuhan University Press, Wuhan

    Google Scholar 

  9. Wang X, Feng C, Yu J et al (2020) Ways to correct the errors in posture of multibeam sounding resulted from water flow. Mater Sci Eng 780(3):032047

    Google Scholar 

  10. Xu W, Zhang M, Zhang H, et al (2012) Sparse-increment iteration-based sound velocity profile estimation with multibeam bathymetry systems. In: 2012 Oceans, Hampton Roads, VA, USA, 2012, pp 1–5

  11. Linbang He, Jianhu Z, Hongmei Z et al (2015) A precise multibeam sound ray tracking method taking into account the attitude angle. J Harbin Eng Univ 36(01):46–50

    Google Scholar 

  12. Dinn D F, Loncarevic B D, Costello G (1995) The effect of sound velocity errors on multibeam sonar depth precision. In: Challenges of Our Changing Global Environment, San Diego, CA, USA, 1995, pp 1001–1010

  13. Bosheng L, Jiayu L (2010) Hydroacoustics principle. Harbin Engineering University Press, Harbin

    Google Scholar 

  14. Yang F, Li J, Ziyin Wu et al (2008) The methods of high quality post-processing for shallow multibeam data. Acta Geod Cartogr Sin 4:444–450

    Google Scholar 

  15. Jianhu Z (2012) Modern marine surveying and charting. Wuhan University Press, Wuhan

    Google Scholar 

  16. Etter PC (2013) Underwater acoustic modeling and simulation (4th version). CRC Press

    Google Scholar 

  17. Zhiwei Z, **gyang B, Fumin X et al (2018) Inversion of sound velocity profile in multibeam survey based on simulated annealing algorithm. Geomat Inf Sci Wuhan Univ 43(8):1234–1241

    Google Scholar 

  18. Qingliang D, Minxun C, Junhua Z et al (2011) Analysis and processing of transform geography of convex and cave in multibeam sounding system. Hydrogr Surv Chart 31(1):32–35

    Google Scholar 

  19. Snellen M, Siemes K, Simons DG (2009) A model-based method for reducing the sound speed induced errors in multibeam echo-sounder bathymetric measurements. In: OCEANS 2009-EUROPE, Bremen, Germany, pp 1–7

  20. Zhao J, Yan J, Zhang H et al (2014) A new method for weakening the combined effect of residual errors on multibeam bathymetric data. Mar Geophys Res 35(4):379–394

    Article  Google Scholar 

  21. Sun W, Bao J, Liu M et al (2016) Sound velocity profile (SVP) inversion through correcting the terrain distortion. Int Hydrogr Rev 13:7–16

    Google Scholar 

  22. C. Didier, E. Jaouad, G. Gaspard, et al (2019) Real-time correction of sound refraction errors in bathymetric measurements using multiswath multibeam echosounder. In: OCEANS 2019—Marseille, Marseille, France, 2019, pp 1–7

  23. Mohammadloo TH, Snellen M, Renoud W et al (2019) Correcting multibeam echosounder bathymetric measurements for errors induced by inaccurate water column sound speeds. IEEE Access 7:122052–122068

    Article  Google Scholar 

  24. Yuanbi X, Zhencan P, **gyang B et al (2020) Sounding velocity integrated error correction method of multibeam data based on Kalman filtering. Geomat Inf Sci Wuhan Univ 45(9):146–153

    Google Scholar 

  25. Chen C, Ma Y, Liu Y (2018) Reconstructing Sound speed profiles worldwide with Sea surface data. Appl Ocean Res 77:26–33

    Article  Google Scholar 

  26. Yangfan L, Zhenjie W, Shuang Z (2020) Layered-EOFs based adaptive reconstruction of sound velocity profile in multibeam sounding. Tech Acoust 39(3):372–378

    Google Scholar 

  27. Mafarja MM, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302–312

    Article  Google Scholar 

  28. Han X, Dong Y, Yue L et al (2019) State transition simulated annealing algorithm for discrete-continuous optimization problems. IEEE Access 7(44):391–403

    Google Scholar 

  29. Chadwell CD, Sweeney AD (2010) Acoustic ray-trace equations for seafloor geodesy. Mar Geod 33(2):164–186

    Article  Google Scholar 

  30. ** Lu, Shaofeng B, Motao H et al (2012) An improved method for calculation average sound speed in constant gradient sound ray tracking technology. Geomat Inf Sci Wuhan Univ 37(5):590–593

    Google Scholar 

  31. Yang HQ, He QR, Qi S et al (2012) Method of sound ray refraction correction for underwater target location. J Henan Normal Univ 40:36–39

    Google Scholar 

  32. Ben-Ameur W (2004) Computing the initial temperature of simulated annealing. Comput Optim Appl 29(3):369–385

    Article  MathSciNet  Google Scholar 

  33. Triki E, Collette Y, Siarry P (2005) A theoretical study on the behavior of simulated annealing leading to a new cooling schedule. Eur J Oper Res 166(1):77–92

    Article  MathSciNet  Google Scholar 

  34. Cohn H, Fielding M (1999) Simulated annealing: searching for an optimal temperature schedule. SIAM J Optim 9(3):779–802

    Article  MathSciNet  Google Scholar 

  35. Zhiwei Z, **gyang B, Jianju L (2017) Order selection of EOF expression of sound velocity profile in multibeam surveys. Hydrogr Surv Chart 5:20–24

    Google Scholar 

  36. Fengying W (2007) Modern climate statistics diagnosis and prediction technology (2nd version). Meteorological Press, Bei**g

    Google Scholar 

  37. Xu H, **ao Z, Chen K et al (2019) Spatial and temporal distribution, chemical characteristics, and sources of ambient particulate matter in the Bei**g-Tian**-Hebei region. Sci Total Environ 658:280–293

    Article  Google Scholar 

  38. Sun W, Bao J, Liu M et al (2016) Inversion of sound velocity profiles by correcting the terrain distortion. Geomat Inf Sci Wuhan Univ 41(3):348–355

    Google Scholar 

  39. Geng X, Zielinski A (1999) Precise multibeam acoustic bathymetry. Mar Geod 22(3):157–167

    Article  Google Scholar 

Download references

Acknowledgements

The study is funded by National Key Research and Development Program of China (2020YFB0505800 and 2020YFB0505804), National Natural Science Foundation of China (41931076, 41874032 and 41731069), Open foundation of State Key Laboratory of Geo-information Engineering (SKLGIE2019-Z-2-2).

Author information

Authors and Affiliations

  1. Institute of Space Science, Shandong University, Weihai, China

    Yangfan Liu, Tianhe Xu, Junting Wang & Dapeng Mu

Authors
  1. Yangfan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tianhe Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Junting Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dapeng Mu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tianhe Xu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Y., Xu, T., Wang, J. et al. Multibeam seafloor topography distortion correction based on SVP inversion. J Mar Sci Technol 27, 467–481 (2022). https://doi.org/10.1007/s00773-021-00845-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00773-021-00845-7

Keywords

Search

Navigation