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Excellence in Research: A Data-Driven Computational Framework for Seismic Detection, Modeling and Prediction

卓越的研究：用于地震探测、建模和预测的数据驱动计算框架

基本信息

批准号：
2101080
负责人：
Mulugeta Dugda
金额：
$ 34.83万
依托单位：
Morgan State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-15 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2101080&HistoricalAwards=false
关键词：
Excellence Research Data Driven Computational

项目摘要

This project is co-funded by the Geophysics (PH) Program and the Historically Black Colleges and Universities - Excellence in Research (HBCU-EiR) Program, along with support from Integrative and Collaborative Education and Research (ICER) funds of the NSF Geosciences Directorate.Embarking on innovative scientific approaches that fully exploit fast-growing datasets across different disciplines is necessary to achieve great scientific discovery. Such necessity stimulates urgent research initiatives across various scientific disciplines. Seismology, being a data-driven science with huge datasets recorded for more than a century, will definitely benefit from the developments of new scalable algorithms that can process such massive data volumes. Having tremendous potential, geophysics and particularly seismology innovation using machine learning and big-data analytics based on multiple seismic datasets has so far been trailing behind. Broader impacts of this project include: (1) launching a new interdisciplinary research in the areas of machine learning, big-data analytics, computational techniques and geophysics in which undergraduate STEM students and graduate students of the research project will be cross-trained to transcend traditional disciplinary boundaries, (2) creation and distribution of big-data analytics machine learning techniques useful for detecting underground nuclear explosions, modeling crustal structure and predicting the spatial distribution of aftershocks following major earthquakes, (3) delivering analytical and computational techniques that have much to offer to the field of seismology and solid-Earth geophysics at large, and (4) imparting research-enriched learning experiences to STEM undergraduate and graduate students through educational activities at Morgan State University and summer research internships at national laboratories.This project focuses on developing a data-driven computational framework for seismic detection, modeling and prediction. Having the training and expertise in geophysics/seismology, machine learning and big-data analytics to explore computational techniques, the investigators of this project will address the challenges in the development of underground nuclear explosion detection methods, predicting the spatial distribution of aftershocks following major earthquakes, and modeling crustal structure. The specific objectives of this research are: (i) to investigate the development of automatic nuclear explosion detection methods utilizing approximate nearest neighbor methods to search large archives along with the integration of template matching and iterative seismic processing framework, (ii) to explore machine learning methods to predict the likelihood that aftershocks would occur in a particular location on a spatial grid based on modeling transfer of elastic energy between regional stress fields and a set of localized faults, and (iii) to examine data analytics algorithms for modeling crustal structure using multiple complex seismic datasets that provide research communities with open source big-data analytics tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目由地球物理学（PH）计划和历史上的黑人学院和大学-卓越研究（HBCU-EiR）计划共同资助，沿着NSF地球科学理事会的综合与合作教育和研究（ICER）基金的支持。为了实现伟大的科学发现，必须采取创新的科学方法，充分利用跨学科快速增长的数据集。这种必要性激发了跨学科的紧急研究计划。地震学是一门数据驱动的科学，拥有超过世纪的庞大数据集，它肯定会受益于能够处理如此庞大数据量的新的可扩展算法的发展。具有巨大潜力的地球物理学，特别是使用机器学习和基于多个地震数据集的大数据分析的地震学创新迄今为止一直落后。该项目的更广泛影响包括：（1）在机器学习、大数据分析、计算技术和物理学领域开展新的跨学科研究，研究项目的本科STEM学生和研究生将交叉培训，以超越传统的学科界限，（2）创建和分发用于探测地下核爆炸的大数据分析机器学习技术，模拟地壳结构和预测大地震后余震的空间分布，（3）提供对地震学和固体地球物理学领域有很大贡献的分析和计算技术，以及（4）通过在摩根州立大学的教育活动和在国家实验室的暑期研究实习，向STEM本科生和研究生传授丰富的研究学习经验。该项目的重点是开发用于地震检测、建模和预测的数据驱动计算框架。该项目的研究人员在地球物理学/地震学、机器学习和大数据分析方面接受过培训，具备探索计算技术的专业知识，他们将应对地下核爆炸探测方法开发方面的挑战，预测大地震后余震的空间分布，并对地壳结构进行建模。这项研究的具体目标是：（i）研究自动核爆炸探测方法的发展，该方法利用近似最近邻方法搜索大型档案，沿着模板匹配和迭代地震处理框架的集成，（二）探索机器学习方法来预测余震发生在空间网格上特定位置的可能性，区域应力场和一组局部断层之间的弹性能量，以及（iii）使用多个复杂的地震数据集，为研究社区提供开源大数据分析工具，检查用于建模地壳结构的数据分析算法。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。