Collaborative Research: ITR: Software for Interpretation of Cosmogenic Isotope Inventories - Combination of Geology, Modeling, Software Engineering and Artificial Intelligence

合作研究：ITR：解释宇宙成因同位素库存的软件 - 地质学、建模、软件工程和人工智能的结合

• 批准号: 0325812
• 负责人: Elizabeth Bradley
• 金额: $ 92.16万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-15 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0325812&HistoricalAwards=false
• 关键词: Collaborative; Research; ITR; Software; Interpretation

This project aims to improve the accuracy of geological chronologies based on cosmogenic isotope data. It combines research on computer models of the effects of various processes on the rate of accumulation of cosmogenic isotopes in geological formations, the development of a database of data (such as isotope abundances) and environmental parameters useful for cosmogenic isotope dating (such as solar output and the state of the terrestrial magnetic field at different times), and an artificial intelligence (AI) system to support the use of cosmogenic isotope accumulation models in dating. The AI system will act as an expert system guiding the user in the choice of modeling tools and data, and integrating the selected models and data. The combined AI/database/modeling system will be built using a component-based architecture to make it simpler to modify and extend as new processes and data sets are added.Because of the central importance to geological inference of establishing the age of geological structures, this project, if successful should be helpful in a number of problems in research and applied geology. The project will also strengthen the connections between the geological and computer science research communities, both by virtue of the collaboration itself and by demonstrating the potential for exploiting information technology to further geological research.

该项目旨在提高基于宇宙成因同位素数据的地质年代学的准确性。 它结合了对各种过程对地质构造中宇宙成因同位素积累速率的影响的计算机模型的研究，数据库的开发，（如同位素丰度）和可用于宇宙成因同位素定年的环境参数（例如太阳输出和地球磁场在不同时间的状态），和人工智能（AI）系统，以支持在测年中使用宇宙成因同位素积累模型。 人工智能系统将作为一个专家系统，指导用户选择建模工具和数据，并集成选定的模型和数据。 人工智能/数据库/建模组合系统将使用基于组件的体系结构来构建，以便在添加新的过程和数据集时更容易修改和扩展。由于确定地质构造年龄对地质推断的核心重要性，该项目如果成功，将有助于研究和应用地质学中的一些问题。 该项目还将加强地质学和计算机科学研究界之间的联系，既通过合作本身，也通过展示利用信息技术促进地质学研究的潜力。