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

该项目由地球物理学（PH）计划和传统黑人大学卓越研究（HBCU-EiR）计划共同资助，并得到美国国家科学基金会地球科学理事会综合合作教育与研究（ICER）基金的支持。采用创新的科学方法，充分利用不同学科快速增长的数据集，是实现重大科学发现的必要条件。这种需求刺激了各个科学学科的紧急研究计划。地震学作为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，肯定会受益于能够处理如此海量数据的新型可扩展算法的发展。利用机器学习和基于多个地震数据集的大数据分析技术进行的地球物理学和地震学创新具有巨大的潜力，但迄今为止一直落后。本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学等领域开展新的跨学科研究，使STEM本科生和研究项目的研究生交叉训练，以超越传统的学科界限；(2)创建和推广用于探测地下核爆炸的大数据分析机器学习技术；(3)为地震学和固体地球物理领域提供分析和计算技术；(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，为STEM本科生和研究生提供丰富的研究学习经验。该项目的重点是开发一个数据驱动的计算框架，用于地震探测、建模和预测。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发展中的挑战，预测大地震后余震的空间分布，以及建模地壳结构。本研究的具体目标是：(i)研究利用近似最近邻方法搜索大型档案的自动核爆炸检测方法的发展，以及模板匹配和迭代地震处理框架的整合；（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弹性能量的建模传递，预测在空间网格上特定位置发生余震的可能性；（iii）研究使用多个复杂地震数据集建模地壳结构的数据分析算法，为研究社区提供开源大数据分析工具。该奖项反映了美国国家科学基金会的法定使命，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。

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.