SVD based automated dike monitoring system using DTS data

The detection of water leakages in dikes using distributed temperature sensors is an interesting prospect due to the commercial viability of these optical fiber based sensors. The acquired temperature data, being not directly interpretable, requires intervention of advanced signal processing techniques. In this work, we propose a system for the identification of singularities such as existing dike structures and water leakages. The distances where singularities exist show temperature variations over the course of a day which are different from the nonsingular zones. The different nonsingular zones though show a similar temperature variation trend. The proposed system estimates this reference trend as the most coherent component of the Singular Value Decomposition applied on daily data. The corresponding SVD residue subspace thus represents the deviation from the reference subspace and thus contains information on singularities. The L₂ norm of this residue is a good discrimination measure for identification of these singularities.