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

这个教师早期职业发展计划（CAREER）补助金将通过利用表面波方法开发更现实和概率的剪切波速度（Vs）剖面来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，但原位部位表征的世界仍然陷入过去的泥潭，并继续严重依赖100多年前开发的经验方法，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。 作为实现这一目标的一步，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、基岩可裂性和沉降分析等应用中利用剪切波速度等参数至关重要。 此外，该项目中心的教育影响，促进使用非侵入性的方法（1）激励未来的工程师，通过工程夏季推广计划，接受新技术，（2）通过国际学生交流计划教育学生，（3）通过扬声器局提供培训，实践工程师。这项研究的智力价值在于国家的最先进的面波反演算法的发展。这些算法将使用机器学习和跨维蒙特卡罗方法将贝叶斯统计框架纳入高级反演算法。该算法将专家知识纳入反问题，并根据实验数据表征开发的Vs配置文件的不确定性。贝叶斯和机器学习方法的使用将允许解决方案中的不确定性得到考虑，并以比当前方法更稳健的方式呈现。此外，进一步了解多个数据类型之间的岩石物理联系推进我们的知识，不同的数据类型如何在联合反演框架内一起工作，以约束反演问题。反演框架的进步将对多个应用产生更广泛的影响，包括场地响应、液化分析和基础设施评估。 此外，开发更准确、更现实和更概率的Vs剖面允许将所得Vs剖面纳入基于性能的设计中。最后，反演算法和岩石物理联系知识的进步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。该奖项反映了NSF的法定使命，并被认为值得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来支持。

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.