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

Ward, W.O.C.; Wilkinson, P.B.; Chambers, J.E.; Nilsson, H.; Kuras, O.; Bai, L.. 2016 Tracking tracer motion in a 4-D electrical resistivity tomography experiment. Water Resources Research, 52 (5). 4078-4094. https://doi.org/10.1002/2015WR017958

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Official URL: http://dx.doi.org/10.1002/2015WR017958

Abstract/Summary

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:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1002/2015WR017958
ISSN:	00431397
Date made live:	09 Aug 2016 14:09 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/514215

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1002/2015WR017958

ISSN:

00431397

Date made live:

09 Aug 2016 14:09 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/514215