CAREER: Advancing the Development of Realistic and Probabilistic Shear Wave Velocity Ground Profiles Using Advanced Inversion Strategies

职业：利用先进的反演策略推进现实和概率横波速度地面剖面的开发

• 批准号: 1943113
• 负责人: Clinton Wood
• 金额: $ 50万
• 依托单位: University of Arkansas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1943113&HistoricalAwards=false
• 关键词: CAREER; Advancing; Development; Realistic; Probabilistic

This Faculty Early Career Development Program (CAREER) grant will advance our ability to image below the ground surface by utilizing surface wave methods to develop more realistic and probabilistic shear wave velocity (Vs) profiles. As advanced as the tools of daily society have become, the world of in-situ site characterization still remains mired in the past and continues to rely heavily on empirical approaches, which were developed over 100 years ago, while the medical industry has made leaps forward in the field of non-invasive imaging. As the profession moves forward, the advancement of non-invasive methods is critical to meeting the challenges of tomorrow in a cost-effective manner. As a step toward this goal, this project plans to advance our ability to develop realistic and probabilistic subsurface models through advanced inversion schemes. These schemes will harness artificial intelligence and additional wavefield information to replace a level of user skill now required to develop these subsurface models. These realistic subsurface models are critical to utilizing parameters, such as shear wave velocity, in applications including liquefaction triggering, site response analysis, bedrock rippability, and settlement analyses. In addition, the educational impacts of the project center on promoting the use of non-invasive methods by (1) inspiring future engineers to embrace new technologies through engineering summer outreach programs, (2) educating students through an international student exchange program, and (3) providing training to practicing engineers through a speakers bureau. The intellectual merit of this research lies in the development of state-of-the-art surface wave inversion algorithms. These algorithms will incorporate a Bayesian statistical framework into high-level inversion algorithms using machine learning and trans-dimensional Monte Carlo methodologies. The algorithms will incorporate expert knowledge into the inverse problem and characterize the uncertainty of the developed Vs profiles based on the experimental data. The use of Bayesian and machine learning methods will allow uncertainty in the solution to be considered and presented in a more robust way than current approaches. In addition, further understanding of the petrophysical link between multiple data types advances our knowledge of how different data types work together within joint inversion frameworks to constrain the inversion problem. Advances in the inversion framework will produce broader impacts for multiple applications including site response, liquefaction analysis, and infrastructure evaluation. Moreover, the development of more accurate, realistic, and probabilistic Vs profiles allows for the inclusion of resulting Vs profiles into performance-based designs. Lastly, advancements in inversion algorithms and knowledge of petrophysical links are transferable to other non-invasive geophysical methods, which all suffer from non-uniqueness issues.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.

这项学院早期职业发展计划(Career)赠款将通过利用面波方法来开发更真实和更概率的横波速度(Vs)剖面，从而提高我们在地面以下成像的能力。尽管日常社会的工具已经变得先进，但原位现场表征的世界仍然深陷过去，继续严重依赖于100多年前发展起来的经验方法，而医疗行业在非侵入性成像领域取得了飞跃。随着该行业的发展，非侵入性方法的进步对于以具有成本效益的方式迎接明天的挑战至关重要。作为迈向这一目标的一步，该项目计划通过先进的反演方案提高我们开发现实和概率地下模型的能力。这些方案将利用人工智能和额外的波场信息来取代目前开发这些地下模型所需的用户技能水平。这些真实的地下模型对于在液化触发、场地响应分析、基岩可撕裂性和沉降分析等应用中利用剪切波速等参数至关重要。此外，该项目的教育影响主要集中在通过以下方式促进非侵入性方法的使用：(1)通过工程暑期推广计划激励未来工程师接受新技术，(2)通过国际学生交换计划教育学生，以及(3)通过演讲者局为实习工程师提供培训。这项研究的智力价值在于发展了最先进的面波反演算法。这些算法将把贝叶斯统计框架纳入使用机器学习和跨维蒙特卡罗方法的高级反演算法中。该算法将把专家知识融入到反问题中，并基于实验数据来表征所开发的VS剖面的不确定性。贝叶斯和机器学习方法的使用将使解决方案中的不确定性得以考虑，并以比当前方法更稳健的方式提出。此外，进一步了解多种数据类型之间的岩石物理联系有助于我们了解不同数据类型如何在联合反演框架内协同工作以约束反演问题。反演框架的进展将对包括场地响应、液化分析和基础设施评估在内的多种应用产生更广泛的影响。此外，开发更准确、更真实和更概率的VS配置文件允许将结果VS配置文件包括到基于性能的设计中。最后，反演算法的进步和岩石物理联系的知识可以转移到其他非侵入性地球物理方法，这些方法都存在非唯一性问题。这一奖项反映了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。