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: $ 4.66万
• 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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=668167
• 关键词: Development; geospatial; clustering; methods; broadband

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.

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。当今工业的动态特性要求不断地进行地震和地质信息调查的新技术和方法，有效和高效的地震数据处理对工业用户群体来说是重要的。在这个项目中，我们将专注于开发高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。该项目将使工业合作伙伴Symroc更好地了解如何从各种频率的地震数据中有效地识别地震相，并提供更好的技术。这个 ** 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，可以导致一个更系统和数据驱动的程序，语义数据分析，可视化 ** 和解释。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地震数据收集，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、现场监测和其他真实的实时环境监测。该项目与联邦，省和 ** 卡尔加里大学的战略优先事项保持一致。此外，专业知识和研究经验为HQP提供了独特的培训机会。