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Tracking tracer motion in a 4‐D electrical resistivity tomography experiment
Author(s) -
Ward W. O. C.,
Wilkinson P. B.,
Chambers J. E.,
Nilsson H.,
Kuras O.,
Bai L.
Publication year - 2016
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2015wr017958
Subject(s) - tracer , tracking (education) , kalman filter , electrical resistivity tomography , trajectory , computer vision , artificial intelligence , noise (video) , tomography , computer science , electrical resistivity and conductivity , image (mathematics) , engineering , physics , optics , psychology , pedagogy , astronomy , nuclear physics , electrical engineering
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.