Classification and repeatability studies of transient electromagnetic measurements with respect to the development of CO2-monitoring techniques

The mitigation of greenhouse gases, like CO2 is a challenging aspect for our society. A strategy to hamper the constant emission of CO2 is utilizing carbon capture and storage technologies. CO2 is sequestrated in subsurface reservoirs. However, these reservoirs harbor the risk of leakage and appropriate geophysical monitoring methods are needed. A crucial aspect of monitoring is the assignment of measured data to certain events occurring. Especially if changes in the measured data are small, suitable statistical methods are needed. In this thesis, a new statistical workflow based on cluster analysis is proposed to detect similar transient electromagnetic signals. The similarity criteria dynamic time warping, the autoregressive distance, and the normalized root-mean-square distance are investigated and evaluated with respect to the classic Euclidean norm. The optimal number of clusters is determined using the gap statistic and visualized with multidimensional scaling. To validate the clustering results, silhouette values are used. The statistical workflow is applied to a synthetic data set, a long-term monitoring data set and a repeat measurement at a pilot CO2-sequestration site in Brooks, Alberta.

Level thesis:degree_level

thesis.doctoral

Grantor dc:publisher

Technische Universität Bergakademie Freiberg

Year

2020

Author dc:creator

• Bär, Matthias

Contributors dc:contributor

• Spitzer, Klaus
• Schaeben, Helmut
• Tezkan, Bülent

• transient electromagnetics
• CO2-sequestration
• time series analysis
• time series models
• monitoring
• Transient Elektromagnetik
• Zeitreihenanalyse
• Zeitreihenmodelle