The purpose of this research is to investigate the potential of applying concepts from ma- chine learning, such as pattern recognition and matching, to detect climatic signals in ice core data. The main components of this project are the development of a pattern language for expressing relationships between chemical signals over time, a method of tokenizing ice core chemistry data into an easily manageable form, a method of matching specific instances of climatic signals to a specific pattern string, and a method to recognize and evaluate patterns within ice core chemistry data. While there are weaknesses in each of these components, this research serves as a successful proof of concept for the feasibility of applying machine learning techniques to ice core analysis.
Dunn, Nathan, "Pattern Recognition and Matching in Ice Core Data" (2015). Honors College. 224.