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Volume 58 Supplement 2

Special Issue: The 2004 Great Sumatra Earthquake and Tsunami

Site-specific correlation of GPS height residuals with soil moisture variability using artificial neural networks

Earth, Planets and Space volume 58, pages e5–e8 (2006)Cite this article

Abstract

Historical time series generated from GPS sites reveal significant seasonal variations in the vertical direction. It is well known that continental waters (soil moisture, snow, ground water) mass redistribution is one of the potential contributors to these observed seasonal variations although their actual loading effects on GPS results are least well understood. A number of hydrology model outputs exist with a fair degree of uncertainty. Studies of interrelations between anomalous vertical variations observed at geodetic sites and hydrology model outputs are useful, in particular, as the hydrology models continue to be refined. In this paper, artificial neural networks is proposed for correlating GPS height residuals with the soil moisture variability. Time series from eight sites of the global GPS network are used to correlate with the soil moisture information from the US National Oceanographic and Atmospheric Administration (NOAA) Climate Prediction Center’s land data assimilation system (CPC LDAS). The results show the feasibility of the neural interpretation in terms of the correlation coefficients (∼0.6) and root mean square errors (about 15% of residual range). Other geodetic time series can be used for the same purpose, such as from SLR, VLBI, and absolute gravity.

References

  • Ambrozic, T. and G. Turk, Prediction of subsidence due to underground mining by artificial neural networks, Computers & Geosciences, 29(5), 627–637, 2003.

    Article  Google Scholar 

  • Bose, N. and P. Liang, Neural Network Fundamentals with Graphs, Algorithms and Applications, Tata McGraw-Hill Publication, 478 pp, 1998.

    Google Scholar 

  • Dong, D., P. Fang, Y. Bock, M. K. Cheng, and S. Miyazaki, Anatomy of apparent seasonal variations from GPS-derived site position time series, J. Geophys. Res, 107(B4), doi:10.1029/2001JB000573, 2002.

  • Hammerstrom, D., titWorking with neural networks, IEEE Spectrum, July, 46–53, 1993.

  • Haykin, S., Neural Network: A Comprehensive Foundation, Prentice Hall—Upper Saddle River, New Jersey, 1999.

    Google Scholar 

  • Leandro, R. F., A new technique to TEC regional modelling using a neural network, Personal Communication, 9 pp, 2004.

    Google Scholar 

  • Mathworks, Inc., MATLAB 7.0 (Release 14), www.mathworks.com, 2004.

  • McKinnell, L., A Neural Network based Ionospheric Model for the Bottomside electron density profile over Grahamstown, South Africa, Ph.D. Thesis, Rhodes University, 2002.

    Google Scholar 

  • Rodell, M., et al., The global land data assimilation system, Bull. Am. Meteorol. Soc., 85(3), 381–394, 2004.

    Article  Google Scholar 

  • Takle, E. S., M. Jha, and C. J. Anderson, Hydrological cycle in the upper Mississippi River basin: 20th century simulations by multiple GCMs, Geophys. Res. Lett., 32, L18407, doi:10.1029/2005GL023630, 2005.

  • ]van Dam, T. M., J. M. Wahr, P. C. D. Milly, A. B. Shmakin, G. Blewitt, D. Lavallee, and K. M. Larson, Crustal displacements due to continental water loading, Geophys. Res. Lett., 28, 651–654, 2001.

    Article  Google Scholar 

  • Wasserman, P., Advanced Methods in Neural Computing, Van Nostrand Reinhold, New York, 255 pp, 1993.

    Google Scholar 

  • Wilson, C. R. and J. Chen, The Hydrology Bureau of the Global Geophysical Fluids Center, in IERS Technical Note No. 30, edited by B. Richter et al., Proceedings of IERS Workshop on Combination Research and Global Geophysical Fluids, ISBN 3-89888-877-0, 153–156, 2003.

    Google Scholar 

  • Winter, M. and G. Taylor, Modular neural networks for map-matched GPS positioning, Fourth International Conference on Web Information Systems Engineering Workshops (WISEW’03), 106–111, 2003.

    Google Scholar 

  • Zaletnyik, P., Coordinate transformation with neural networks and with polynomials in Hungary, Personal Communication, 9 pp, 2005.

    Google Scholar 

Download references

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Authors and Affiliations

  1. Geospatial and Earth Monitoring Division, Geoscience Australia, Australia

    Clement Ogaja

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  1. Clement Ogaja
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Correspondence to Clement Ogaja.

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Ogaja, C. Site-specific correlation of GPS height residuals with soil moisture variability using artificial neural networks. Earth Planet Sp 58, e5–e8 (2006). https://doi.org/10.1186/BF03353363

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  • DOI: https://doi.org/10.1186/BF03353363

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