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 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.