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 and**resource exploration fields. The dynamic nature of today's industry requires new technology and ways of**conducting seismic and geomatics survey continuously, effective and efficient seismic data processing is**important to industry user groups. In this project, we will focus on the development of efficient geospatial**clustering methods and a system prototype to identify groups of similar seismic facies to represent variability in**subsurface content. The project will provide the industrial partner Symroc with a better understanding and**technology of how to efficiently identify seismic facies from a wide range of frequency seismic data. This**project will be coordinated and finished by the team of Dr. Xin Wang at University of Calgary with**collaborators from the industrial partner Symroc. The project offers innovative approaches and distinctive**solutions that can lead to a more systematic and data-driven procedure for semismic data analysis, visualization**and interpretation. It will provide Symroc better software solution to process seimsmic data. The project offers**innovative approaches and distinctive solutions that can lead to more energy-efficient, low cost and reliable**seismic data collection, integration, analytics and visualization process. The developed methods and system can**potentially be used to help natural earthquake studies and researches, resource industry operations, live**monitoring, and other real time environmental monitoring. This project is aligned with Federal, Provincial and**the University of Calgary strategic priorities. Additionally, the expertise and research experience provides**unique training opportunities for HQP.