Development of geospatial clustering methods for broadband seismic-facies analysis

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Seismic data acquisition and geospatial information collection are common practice in current engineering andresource exploration fields. The dynamic nature of today's industry requires new technology and ways ofconducting seismic and geomatics survey continuously, effective and efficient seismic data processing isimportant to industry user groups. In this project, we will focus on the development of efficient geospatialclustering methods and a system prototype to identify groups of similar seismic facies to represent variability insubsurface content. The project will provide the industrial partner Symroc with a better understanding andtechnology of how to efficiently identify seismic facies from a wide range of frequency seismic data. Thisproject will be coordinated and finished by the team of Dr. Xin Wang at University of Calgary withcollaborators from the industrial partner Symroc. The project offers innovative approaches and distinctivesolutions that can lead to a more systematic and data-driven procedure for semismic data analysis, visualizationand interpretation. It will provide Symroc better software solution to process seimsmic data. The project offersinnovative approaches and distinctive solutions that can lead to more energy-efficient, low cost and reliableseismic data collection, integration, analytics and visualization process. The developed methods and system canpotentially be used to help natural earthquake studies and researches, resource industry operations, livemonitoring, and other real time environmental monitoring. This project is aligned with Federal, Provincial andthe University of Calgary strategic priorities. Additionally, the expertise and research experience providesunique training opportunities for HQP.