Tracking tracer motion in a 4-D electrical resistivity tomography experiment
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 updat...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
American Geophysical Union
2016
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| Online Access: | http://eprints.nottingham.ac.uk/34202/ http://eprints.nottingham.ac.uk/34202/ http://eprints.nottingham.ac.uk/34202/ http://eprints.nottingham.ac.uk/34202/1/WardEtAl-WRR2016-TrackingIndependentTracerMomentsInA4DElectricalResistivityTomographyExperiment.pdf |