In Partial Fulfillment of the Requirements for the Degree of
Master of Science
Will defend her thesis
The development of seismic imaging technology has substantially improved the exploration of subsurface deposits of crude oil, natural gas and minerals. Recent advances in data capture, processing power and storage capabilities have enabled us to analyze large volumes of seismic data. In our study we report on the implementation of machine learning and data mining techniques for analysis of seismic data to reveal salt deposits underneath the soil. Several seismic attributes have been extracted from these datasets. Using information gain, the best six attributes have been selected for further classification. Finally we compared the results obtained using different clustering techniques.
Date: Wednesday, July 23, 2014
Time: 11:00 AM
Place: HBSB 350
Faculty, students, and the general public are invited.
Advisor: Prof. Ricardo Vilalta