Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article

Degree

Master of Engineering Science

Program

Electrical and Computer Engineering

Collaborative Specialization

Planetary Science and Exploration

Supervisor

Dr. Kenneth McIsaac

2nd Supervisor

Dr. Jayshri Sabarinathan

Co-Supervisor

Abstract

Space weather phenomena is a complex area of research as there are many different variables and signatures that are used to identify the occurrence of solar storms and Interplanetary Coronal Mass Ejections (ICMEs), with inconsistencies between databases and solar storm catalogues. The identification of space weather events is important from a satellite operation point of view, as strong geomagnetic storms can cause orbit perturbations to satellites in low-earth orbit. The Disturbance storm time (Dst) and the Planetary K-index (Kp) are common indices used to identify the occurrence of geomagnetic storms caused by ICMEs, among several other signatures that are not consistent with every storm. Moreover, specific instrumentation is needed for solar storm and space weather phenomena, which can be costly and technically difficult for small and nano-satellite applications. This thesis demonstrates the capability of a new signature for identification and characterization of ICMEs, through the use of satellite accelerometer data from the Gravity Recovery and Climate Experiment (GRACE) satellite, and machine learning techniques. Utilizing pre-existing satellite instrumentation, this research proposes the use of accelerometers for future space weather monitoring applications. Four binary classification algorithms have been explored: Random Forest, Support Vector Machine, Extremely Randomized Trees, and Logistic Regression. It is proposed that a binary classification model can differentiate between a solar storm caused by an ICME versus a period of quiet geomagnetic activity, using only the accelerometer data of a satellite. Of the four architectures, the tree-based machine learning models performed the best, with accuracy scores over 80%.

Summary for Lay Audience

Space weather phenomena is a complex area of research. An eruption of energy on the surface of the Sun sends high-energy particles towards Earth, a signature of the beginning of an event known as an Interplanetary Coronal Mass Ejection (ICME). When these events reach the Earth’s atmosphere, they result in geomagnetic storms, which physically alter the atmosphere around the Earth and the satellites orbiting within it. ICME and geomagnetic storm events are difficult to characterize, as there are many different variables and signatures that are used to identify them, with inconsistencies between databases and storm catalogues. The identification of space weather events is important from a satellite operation point of view, as strong geomagnetic storms can cause the orbit properties of a satellite to change unexpectedly, which could result in collisions with other spacecraft, or unwanted re-entry. The Disturbance Storm-Time (Dst) and the Planetary K-index (Kp) are common indices used to identify the occurrence of geomagnetic storms caused by ICMEs, among several other signatures that are not consistent with every storm. Moreover, specific instrumentation is needed for solar storm and space weather phenomena research, which can be costly and technically difficult for small and nano-satellite applications. This thesis demonstrates the capability of a new signature for identification and characterization of Interplanetary Coronal Mass Ejections, through the use of satellite accelerometer data from the Gravity Recovery and Climate Experiment (GRACE) satellite, and machine learning techniques. Utilizing pre-existing satellite instrumentation and extracting statistical information from what is physically measured by a satellite, this research proposes the use of accelerometers for future space weather monitoring applications. Four binary classification algorithms have been explored: Random Forest, Support Vector Machine, Extremely Randomized Trees, and Logistic Regression. It is proposed that a binary classification model can differentiate between a solar storm caused by an ICME versus a period of quiet geomagnetic activity, using only the accelerometer data of a satellite.

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