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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 895061, 8 pages
http://dx.doi.org/10.1155/2013/895061
Research Article

Optimal Modeling and Filtering of Stochastic Time Series for Geoscience Applications

Department of Geomatics Engineering, Pacific Institute for the Mathematical Sciences, University of Calgary, Calgary, AB, Canada T2N 1N4

Received 8 February 2013; Accepted 23 April 2013

Academic Editor: Gradimir Milovanovic

Copyright © 2013 J. A. Rod Blais. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Sequences of observations or measurements are often modeled as realizations of stochastic processes with some stationary properties in the first and second moments. However in practice, the noise biases and variances are likely to be different for different epochs in time or regions in space, and hence such stationarity assumptions are often questionable. In the case of strict stationarity with equally spaced data, the Wiener-Hopf equations can readily be solved with fast Fourier transforms (FFTs) with optimal computational efficiency. In more general contexts, covariance matrices can also be diagonalized using the Karhunen-Loève transforms (KLTs), or more generally using empirical orthogonal and biorthogonal expansions, which are unfortunately much more demanding in terms of computational efforts. In cases with increment stationarity, the mathematical modeling can be modified and generalized covariances can be used with some computational advantages. The general nonlinear solution methodology is also briefly overviewed with the practical limitations. These different formulations are discussed with special emphasis on the spectral properties of covariance matrices and illustrated with some numerical examples. General recommendations are included for practical geoscience applications.