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

这个项目将通过利用面波方法来开发更真实和更概率的横波速度剖面，从而提高我们成像地下的能力。现场定征仍然停留在过去，并继续严重依赖100多年前发展的经验方法，而医疗行业在非侵入性成像领域已经取得了飞跃。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。研究的框架将利用人工智能和额外的波场信息来取代目前开发地下模型所需的用户技能水平。真实的地下模型对于液化触发、场地响应分析、基岩可撕裂性和沉降分析等应用至关重要。此外，该项目的更大影响是通过国际学生交换计划教育学生，并通过发言人办公室为实习工程师提供培训，从而促进非侵入性方法的使用。这项研究的智力价值在于开发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。该算法将专家知识融入到反问题中，并根据实验数据表征所得到的横波速度剖面的不确定性。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，更准确、更逼真和更具概率性的横波速度剖面的开发允许将其纳入基于性能的设计。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。

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.