Development of geospatial clustering methods for broadband seismic-facies analysis

宽带地震相分析的地理空间聚类方法的开发

• 批准号: 514549-2017
• 负责人: Wang, Xin
• 金额: $ 4.05万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=690654
• 关键词: Development; geospatial; clustering; methods; broadband

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.

地震数据采集和地理空间信息收集是当前工程和资源勘探领域的常见做法。当今工业的动态性要求不断地采用新的技术和方法进行地震和地质信息调查，有效和高效的地震数据处理对工业用户群体至关重要。在这个项目中，我们将专注于开发有效的地理空间聚类方法和系统原型，以识别相似的地震相组，以代表地下含量的变化。该项目将为工业合作伙伴Symroc提供更好的理解和技术，帮助其从各种频率的地震数据中有效识别地震相。该项目将由卡尔加里大学的Xin Wang博士团队与工业合作伙伴Symroc的合作者协调和完成。该项目提供了创新的方法和独特的解决方案，可以为语义数据分析，可视化和解释带来更系统和数据驱动的程序。它将为Symroc提供更好的地震数据处理软件解决方案。该项目提供了创新的方法和独特的解决方案，可以实现更节能，更低成本和更可靠的数据收集，集成，分析和可视化过程。所开发的方法和系统可用于自然地震研究、资源工业运营、生活监测和其他真实的实时环境监测。该项目符合联邦、省和卡尔加里大学的战略优先事项。此外，专业知识和研究经验为HQP提供了独特的培训机会。