CIF21 DIBBs: An Infrastructure for Computer Aided Discovery in Geoscience

CIF21 DIBB：地球科学计算机辅助发现的基础设施

• 批准号: 1442997
• 负责人: Victor Pankratius
• 金额: $ 142.48万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-11-01 至 2019-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1442997&HistoricalAwards=false
• 关键词: CIF21; DIBBs; Infrastructure; Computer; Aided

Next-generation Geoscience needs to handle rapidly growing data volumes from ground-based and space-based sensor networks. As real-world phenomena are mapped to data, the scientific discovery process essentially becomes a search process across multidimensional data sets. The extraction of meaningful discoveries from this sea of data therefore requires highly efficient and scalable machine assistance to enhance human contextual understanding. This is necessary both for testing new hypotheses as well as for the detection of novel events and monitoring for natural hazards.This project develops a computer-aided discovery approach that provides scientists with better support to answer questions such as: What inferences can be drawn from an identified feature? What does a finding mean and how does it fit into the big theoretical picture? Does it contradict or confirm previously established models and findings? How can concepts and ideas be tested effectively? To achieve this, scientists can programmatically express hypothesized Geoscience scenarios, constraints, and model variations. This approach helps delegate the automatic exploration of the combinatorial search space of possible explanations in parallel on a variety of data sets. Furthermore, programmable crawlers can scale the search and discovery of interesting phenomena on cloud-based infrastructures. The computer-aided discovery prototype is evaluated in case studies from Geospace science, including the exploration of structures in space and time using combined GPS, optics, and Geospace radar data.

下一代地球科学需要处理来自地面和天基传感器网络的快速增长的数据量。随着现实世界现象被映射到数据，科学发现过程本质上变成了一个跨多维数据集的搜索过程。因此，从海量数据中提取有意义的发现需要高效和可扩展的机器帮助，以增强人类的上下文理解。这对于测试新的假说以及检测新事件和监测自然灾害都是必要的。这个项目开发了一种计算机辅助发现方法，为科学家提供更好的支持来回答诸如以下问题：可以从已识别的特征中得出什么推论？一项发现意味着什么？它如何符合大的理论图景？它是否与先前建立的模型和发现相矛盾或证实？如何才能有效地检验概念和想法？为了实现这一点，科学家可以通过编程来表达假设的地球科学情景、约束和模型变化。这种方法有助于在各种数据集上并行地委托可能解释的组合搜索空间的自动探索。此外，可编程爬虫可以扩展搜索和发现基于云的基础设施上的有趣现象。计算机辅助发现原型在地球空间科学的案例研究中得到了评估，包括使用组合的GPS、光学和地球空间雷达数据在空间和时间上探索结构。