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

使用先进的反演策略促进现实和概率横波速度剖面的开发

• 批准号: 2100889
• 负责人: Clinton Wood
• 金额: $ 50.22万
• 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: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2100889&HistoricalAwards=false
• 关键词: Advancing; Development; Realistic; Probabilistic; Shear

This project will advance our ability to image the subsurface by utilizing surface wave methods to develop more realistic and probabilistic shear wave velocity profiles. In situ site characterization still remains mired in the past and continues to rely heavily on empirical approaches 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. The researched framework will harness artificial intelligence and additional wavefield information to replace a level of user skill now required to develop subsurface models. Realistic subsurface models are critical for applications including liquefaction triggering, site response analysis, bedrock rippability, and settlement analyses. In addition, the boarder impacts of the project center on promoting the use of non-invasive methods by educating students through an international student exchange program, and providing training to practicing engineers through a speaker’s 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 shear wave velocity 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 shear wave velocity profiles allows for their inclusion 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.

该项目将通过利用表面波方法开发更真实和概率性的剪切波速度剖面来提高我们对地下成像的能力。现场现场表征仍然在过去陷入困境，并继续严重依赖于 100 多年前开发的经验方法。尽管医疗行业在非侵入性成像领域取得了飞跃，但随着行业的发展，非侵入性方法的进步对于以具有成本效益的方式应对未来的挑战至关重要。目标，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。研究的框架将利用人工智能和额外的波场信息来取代现在开发地下模型所需的用户技能水平。现实的地下模型对于包括液化触发在内的应用至关重要。 、场地响应分析、基岩撕裂性和沉降分析。此外，该项目中心通过国际学生交流计划教育学生，并通过一个项目为执业工程师提供培训，对促进非侵入性方法的使用产生了影响。这项研究的智力价值在于开发最先进的表面波反演算法，这些算法将使用机器学习和跨维算法蒙特卡罗方法将贝叶斯统计框架纳入高级反演算法中。该算法将把专家知识融入到反演问题中，并根据实验数据来表征所开发的剪切波速度剖面的不确定性。贝叶斯和机器学习方法的使用将允许以更多的方式考虑和呈现解决方案中的不确定性。比当前更强大的方式此外，对多种数据之间的岩石物理联系的进一步了解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限制反演问题。反演框架的进步将对包括场地响应在内的多种应用产生更广泛的影响。此外，更准确、更真实和更概率的剪切波速度剖面的开发允许将它们纳入基于性能的设计中。最后，反演算法和岩石物理链接知识的进步可以转移到其他领域。非侵入性地球物理方法，这些方法都存在非唯一性问题。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。