Tsunami lead wave reconstruction based on noisy sea surface height measurements

Kegen Yu*

*Corresponding author for this work

Research output: Contribution to journalConference paper

2 Citations (Scopus)
3 Downloads (Pure)

Abstract

This paper presents a Tsunami lead wave reconstruction method using noisy sea surface height (SSH) measurements such as observed by a satellite-carried GNSS reflectometry (GNSS-R) sensor. It is proposed to utilize wavelet theory to mitigate the strong noise in the GNSS-R based SSH measurements. Through extracting the noise components by high-pass filters at decomposition stage and shrinking the noise by thresholding prior to reconstruction, the noise is greatly reduced. Real Tsunami data based simulation results demonstrate that in presence of SSH measurement error of standard deviation 50 cm the accuracy in terms of root mean square error (RMSE) of the lead wave height (true value 145.5 cm) and wavelength (true value 592.0 km) estimation is 21.5 cm and 56.2 km, respectively. The results also show that the proposed wavelet based method considerably outperforms the Kalman filter based method on average. The results demonstrate that the proposed wave reconstruction approach has the potential for Tsunami detection and parameter estimation to assist in achieving reliable Tsunami warning.

Original languageEnglish
Pages (from-to)525-528
Number of pages4
JournalThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
VolumeXLI-B1
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventXXIII ISPRS Congress - Prague, Czech Republic
Duration: 12 Jul 201619 Jul 2016
Conference number: 23rd

Bibliographical note

Copyright the Author(s) 2016. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • Tsunami lead wave reconstruction
  • Wavelet theory
  • Sea surface height measurement
  • GNSS reflectometry

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