Feature-preserving interpolation and filtering of environmental time series. (October 2015)

• Record Type: Journal Article
• Authors: Mariethoz, Gregoire; Linde, Niklas; Jougnot, Damien; Rezaee, Hassan
• Is Part Of: Environmental modelling & software. Volume 72(2015:Oct.)
• Page Start: 71
• Page End: 76
• Publication Date: 2015-10
• Subjects: Gap-filling -- Geophysics -- Geostatistics -- Interferences -- Multiple-point -- Uncertainty; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.07.001
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: We propose a method for filling gaps and removing interferences in time series for applications involving continuous monitoring of environmental variables. The approach is non-parametric and based on an iterative pattern-matching between the affected and the valid parts of the time series. It considers several variables jointly in the pattern matching process and allows preserving linear or non-linear dependences between variables. The uncertainty in the reconstructed time series is quantified through multiple realizations. The method is tested on self-potential data that are affected by strong interferences as well as data gaps, and the results show that our approach allows reproducing the spectral features of the original signal. Even in the presence of intense signal perturbations, it significantly improves the signal and corrects bias introduced by asymmetrical interferences. Potential applications are wide-ranging, including geophysics, meteorology and hydrology. Highlights: A time series processing method that repairs both gaps and interferences. Preserves dependencies between multiple variables. Quantifies the uncertainty of the reconstructed signal.