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Intrinsic random functions for mitigation of atmospheric effects in terrestrial radar interferometry

Butt, Jemil ; Wieser, Andreas ; Conzett, Stefan

In: Journal of Applied Geodesy, 2017, vol. 11, no. 2, p. 89-98

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    Titre
    • Intrinsic random functions for mitigation of atmospheric effects in terrestrial radar interferometry
    Auteur
    • Butt, Jemil. ETH Zürich, Institute of Geodesy and Photogrammetry, Stefano-Franscini-Platz 5, CH-8093 Zürich, Switzerland
    • Wieser, Andreas. ETH Zürich, Institute of Geodesy and Photogrammetry, Stefano-Franscini-Platz 5, CH-8093 Zürich, Switzerland
    • Conzett, Stefan. Terra Vermessungen AG, Obstgartenstrasse 7, CH-8006 Zürich, Switzerland
    Type de document
    • Postprint
    Langue
    • Inglese
    Publié dans
    • Journal of Applied Geodesy, 2017, vol. 11, no. 2, p. 89-98. De Gruyter
    Autre version électronique
    Identifiant OAI-PMH
    • oai:doc.rero.ch:332303
    Summary
    The benefits of terrestrial radar interferometry (TRI) for deformation monitoring are restricted by the influence of changing meteorological conditions contaminating the potentially highly precise measurements with spurious deformations. This is especially the case when the measurement setup includes long distances between instrument and objects of interest and the topography affecting atmospheric refraction is complex. These situations are typically encountered with geo-monitoring in mountainous regions, e.g. with glaciers, landslides or volcanoes. We propose and explain an approach for the mitigation of atmospheric influences based on the theory of intrinsic random functions of order k (IRF-k) generalizing existing approaches based on ordinary least squares estimation of trend functions. This class of random functions retains convenient computational properties allowing for rigorous statistical inference while still permitting to model stochastic spatial phenomena which are non-stationary in mean and variance. We explore the correspondence between the properties of the IRF-k and the properties of the measurement process. In an exemplary case study, we find that our method reduces the time needed to obtain reliable estimates of glacial movements from 12 h down to 0.5 h compared to simple temporal averaging procedures.