Tracking tracer motion in a 4-D electrical resistivity tomography experiment

Ward, Wil O.C. and Wilkinson, Paul B. and Chambers, Jon E. and Nilsson, Henrik and Kuras, Oliver and Bai, Li (2016) Tracking tracer motion in a 4-D electrical resistivity tomography experiment. Water Resources Research, 52 (5). pp. 4078-4094. ISSN 1944-7973

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Abstract

A new framework for automatically tracking subsurface tracers in electrical resistivity tomography (ERT) monitoring images is presented. Using computer vision and Bayesian inference techniques, in the form of a Kalman filter, the trajectory of a subsurface tracer is monitored by predicting and updating a state model representing its movements. Observations for the Kalman filter are gathered using the maximally stable volumes algorithm, which is used to dynamically threshold local regions of an ERT image sequence to detect the tracer at each time step. The application of the framework to the results of 2-D and 3-D tracer monitoring experiments show that the proposed method is effective for detecting and tracking tracer plumes in ERT images in the presence of noise, without intermediate manual intervention.

Item Type: Article
Additional Information: c2016 American Geophysical Union.
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1002/2015WR017958
Depositing User: Nilsson, Henrik
Date Deposited: 21 Jun 2016 08:05
Last Modified: 27 Nov 2016 06:26
URI: http://eprints.nottingham.ac.uk/id/eprint/34202

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