Journal of Geodesy

, Volume 70, Issue 8, pp 470–479 | Cite as

On the use of heterogeneous noisy data in spectral gravity field modeling methods

  • M. G. Sideris


Spectral methods have been a standard tool in physical geodesy applications over the past decade. Typically, they have been used for the efficient evaluation of convolution integrals, utilizing homogeneous, noise-free gridded data. This paper answers the following three questions:
  1. (a)

    Can data errors be propagated into the results?

  2. (b)

    Can heterogeneous data be used?

  3. (c)

    Is error propagation possible with heterogeneous data?


The answer to the above questions is yes and is illustrated for the case of two input data sets and one output.

Firstly, a solution is obtained in the frequency domain using the theory of a two-input, single-output system. The assumption here is that both the input signals and their errors are stochastic variables with known PSDs. The solution depends on the ratios of the error PSD and the signal PSD, i.e., the noise-to-signal ratios of the two inputs. It is shown that, when the two inputs are partially correlated, this solution is equivalent to stepwise collocation.

Secondly, a solution is derived in the frequency domain by a least-squares adjustment of the spectra of the input data. The assumption is that only the input errors are stochastic variables with known power spectral density functions (PSDs). It is shown that the solution depends on the ratio of the noise PSDs.

In both cases, there exists the non-trivial problem of estimating the input noise PSDs, given that we only have available the error variances of the data. An effective but non-rigorous way of overcoming this problem in practice is to approximate the noise PSDs by simple stationary models.


Power Spectral Density Gravity Field Stochastic Variable Heterogeneous Data Spectral Density Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag 1996

Authors and Affiliations

  • M. G. Sideris
    • 1
  1. 1.Department of Geomatics EngineeringThe University of CalgaryCalgary, AlbertaCanada

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