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

Ward, Wil O.C.; Wilkinson, Paul B.; Chambers, Jon E.; Nilsson, Henrik; Kuras, Oliver; Bai, Li

Authors

Wil O.C. Ward

Paul B. Wilkinson

Jon E. Chambers

Henrik Nilsson

Oliver Kuras

Li Bai



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.

Citation

Ward, W. O., Wilkinson, P. B., Chambers, J. E., Nilsson, H., Kuras, O., & Bai, L. (in press). Tracking tracer motion in a 4-D electrical resistivity tomography experiment. Water Resources Research, 52(5), https://doi.org/10.1002/2015WR017958

Journal Article Type Article
Acceptance Date May 4, 2016
Online Publication Date May 26, 2016
Deposit Date Jun 21, 2016
Publicly Available Date Mar 29, 2024
Journal Water Resources Research
Print ISSN 0043-1397
Electronic ISSN 1944-7973
Publisher American Geophysical Union
Peer Reviewed Peer Reviewed
Volume 52
Issue 5
DOI https://doi.org/10.1002/2015WR017958
Public URL https://nottingham-repository.worktribe.com/output/788643
Publisher URL http://onlinelibrary.wiley.com/doi/10.1002/2015WR017958/abstract;jsessionid=4B021E8E23081329CE83237FB491E2C0.f02t04
Additional Information c2016 American Geophysical Union.

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