The assumption is that only the input errors are stochastic variables with known power spectral density functions (PSDs).
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 solution depends on the ratios of the error PSD and the signal PSD, i.e., the noise-to-signal ratios of the two inputs. The assumption here is that both the input signals and their errors are stochastic variables with known PSDs. 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. Is error propagation possible with heterogeneous data? This paper answers the following three questions:(a)Ĭan data errors be propagated into the results? Typically, they have been used for the efficient evaluation of convolution integrals, utilizing homogeneous, noise-free gridded data. Spectral methods have been a standard tool in physical geodesy applications over the past decade.
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