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来开发更现实和概率的剪切波速度曲线来提高我们对地下成像的能力。过去的原位特征仍然陷入困境，并继续严重依赖100年前发展的经验方法，而医疗行业已经在非侵入性成像领域中飞跃。随着行业的前进，非侵入性方法的进步对于以具有成本效益的方式应对明天的挑战至关重要。为了朝着这一目标迈出一步，该项目计划通过高级反转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其他波场信息来替换现在开发地下模型所需的用户技能。现实的地下模型对于包括液化触发，现场响应分析，基岩纹波和设置分析在内的应用至关重要。此外，通过国际学生交流计划教育学生，并通过演讲者的Bugeau为实践工程师提供培训，寄宿生对促进非侵入性方法的使用影响。这项研究的知识分子在于开发最先进的表面波浪反逆转算法。这些算法将使用机器学习和跨维蒙特卡洛方法将贝叶斯统计框架纳入高级反转算法中。该算法将根据实验数据将专家知识纳入反问题，并表征开发的剪切波速度曲线的不确定性。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比当前方法更强大。此外，对多种数据类型之间的岩石物理联系的进一步理解提高了我们对不同数据类型如何在关节反转框架中共同起作用的知识，以限制反转问题。反转框架的进步将对多个应用程序产生更广泛的影响，包括站点响应，液化分析和基础架构评估。此外，开发更准确，现实和概率的剪切波速度曲线使它们融入了基于性能的设计。最后，反转算法的进步和岩石物理联系的知识被转移到其他非侵入性地球物理方法上，这些方法都遭受了非唯一性问题的困扰。该奖项反映了NSF的法定任务，并被认为是通过使用该基金会的知识分子和更广泛影响的评估来评估Criteria的评估来通过评估来评估的。