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

该项目由地球物理(PH)计划和历史上的黑人学院和大学-卓越研究计划(HBCU-EIR)共同资助，并得到美国国家科学基金会地球科学总监的综合和合作教育与研究(ICER)基金的支持。充分利用不同学科快速增长的数据集的创新科学方法是实现重大科学发现的必要手段。这种必要性刺激了各种科学学科的迫切研究计划。地震学是一门数据驱动的科学，拥有一个多世纪以来记录的海量数据集，可以处理如此海量的数据量的新的可扩展算法的开发肯定会使地震学受益。具有巨大潜力的地球物理学，特别是地震学创新，使用基于多个地震数据集的机器学习和大数据分析，到目前为止一直落后。该项目的更广泛影响包括：(1)在机器学习、大数据分析、计算技术和地球物理领域启动一项新的跨学科研究，其中STEM本科生和研究项目的研究生将接受交叉培训，以超越传统的学科界限；(2)创建和传播大数据分析机器学习技术，这些技术有助于探测地下核爆炸、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地球物理领域有很大贡献的分析和计算技术，以及(4)通过摩根州立大学的教育活动和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。该项目专注于开发一个用于地震探测、建模和预测的数据驱动的计算框架。通过在地球物理/地震学、机器学习和大数据分析方面的培训和专业知识来探索计算技术，该项目的研究人员将解决地下核爆炸探测方法的开发、预测大地震后余震的空间分布和建立地壳结构模型方面的挑战。这项研究的具体目标是：(I)研究利用近似最近邻方法搜索大型档案以及模板匹配和迭代地震处理框架相结合的自动核爆炸检测方法的发展，(Ii)探索机器学习方法，基于区域应力场和一组局部断层之间的弹性能量传递建模，预测空间网格上特定位置发生余震的可能性，以及(Iii)使用为研究社区提供开源大数据分析工具的多个复杂地震数据集来检验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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.