Collaborative Research: 3D Ambient Noise Tomography (3D ANT) for Natural Hazards Engineering

合作研究：用于自然灾害工程的 3D 环境噪声断层扫描 (3D ANT)

• 批准号: 1931162
• 负责人: Brady Cox
• 金额: $ 38.79万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931162&HistoricalAwards=false
• 关键词: Collaborative; Research; 3D; Ambient; Noise

Despite significant progress in medical imaging, subsurface imaging for infrastructure engineering lags far behind. For example, many engineering analyses are still based on 1D profiles of the subsurface, or pseudo-2D/3D profiles constructed from several 1D soundings. When true 3D imaging is performed, the depth and resolution of exploration is often limited. While the problem of subsurface imaging is quite complex, the ability to develop rapid, realistic, 3D images of the subsurface, with accompanying engineering properties (e.g., shear modulus), would significantly advance engineering for more resilient and sustainable infrastructure. This research aims to develop a new 3D subsurface imaging method using recordings of ambient noise obtained from a grid of surface sensors. The new 3D Ambient Noise Tomography (3D ANT) method will provide a rapid, non-intrusive, robust way of imaging the subsurface in 3D at m-scales over the top 50- to 100-m of the subsurface. While numerous example applications for accurate and deep 3D subsurface imaging exist within infrastructure engineering, this project will specifically address two needs related to natural hazards: (1) the need for developing realistic 3D subsurface models for use in earthquake ground motion studies, and (2) the need for improved 3D in-situ imaging for anomaly (e.g., void/sinkhole) detection. Furthermore, significant and broad benefits for society, both anticipated and unanticipated, will result from developing deeper, higher-resolution 3D subsurface imaging methods. The ability to look inside the earth and retrieve rapid and reliable models using ambient noise will impact fields as diverse as: natural resource exploration, subsurface hydrology, pure earth science, archeology, underground development, military/security studies, and space/planet exploration. The intellectual merit of this research center around testing the hypothesis that accurate 3D subsurface P- and S-wave velocity models can be extracted at m-scales down to 50- to 100-m depth from surface recordings of ambient noise. To test this hypothesis, the research will develop a novel 3D ANT method and verify the method with numerical simulations and field experiments. The use of ambient noise for 3D subsurface imaging presents inherent challenges related to the uncontrollable frequency content and propagation direction of ambient noise. However, ambient noise is rich in low frequency energy, allowing for deeper imaging than what is currently possible using active-source 3D full waveform inversion (FWI) methods. Hence, 3D ANT, when coupled with active-source 3D FWI, will provide high resolution images to depths presently unobtainable. The 3D ANT method will require collecting ambient noise recordings from a 2D grid of closely spaced surface sensors. The noise recordings will be used to extract experimental correlation functions between every possible pair of sensors. 3D viscoelastic wave equations will then be used to obtain synthetic correlation functions, which will be matched with the experimental ones using a Gauss-Newton FWI approach for extracting 3D subsurface models. Optimization of the 3D ANT algorithm will include parametric studies on field testing configurations (i.e., number and spacing of sensors) and ambient noise characteristics (i.e., frequency content and azimuthal distribution). Ultimately, two well-characterized field sites with ground truth have been selected to test the methodology under real-world conditions. This research will only achieve its broadest impact if we are successful at training students to carry it into the future, disseminating results, and developing tools that practitioners can use in industry. We aim to tackle these challenges while simultaneously broadening the participation of women in natural hazards engineering. Hazards engineering is about transforming how civil infrastructure can be designed and rehabilitated such that communities and individuals are more resilient to the devastating effects of natural hazards, and studies have shown that women are more interested in STEMM fields when they have direct societal impacts. This work will help foster a natural intersection of interest and purpose in a diverse and talented group of future engineers with skills that will be critical for revolutionizing natural hazards engineering, such as coding, analyzing big-data, and supercomputing.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.

尽管医学成像取得了重大进展，但基础设施工程的地下成像却远远落后。 例如，许多工程分析仍然基于地下的 1D 剖面，或基于多个 1D 探测构建的伪 2D/3D 剖面。 当进行真正的 3D 成像时，探索的深度和分辨率通常是有限的。 虽然地下成像问题相当复杂，但开发快速、逼真的地下 3D 图像以及伴随的工程特性（例如剪切模量）的能力将显着推进工程建设，以实现更具弹性和可持续性的基础设施。 本研究旨在利用从表面传感器网格获得的环境噪声记录来开发一种新的 3D 地下成像方法。新的 3D 环境噪声断层扫描 (3D ANT) 方法将提供一种快速、非侵入式、稳健的方法，在地下顶部 50 至 100 米范围内以米级对地下进行 3D 成像。虽然基础设施工程中存在大量精确、深度 3D 地下成像的示例应用程序，但该项目将专门解决与自然灾害相关的两个需求：(1) 需要开发用于地震地面运动研究的真实 3D 地下模型，以及 (2 ) 需要改进 3D 原位成像来检测异常（例如，空洞/天坑）。 此外，开发更深入、更高分辨率的 3D 地下成像方法将为社会带来重大和广泛的利益，无论是预期的还是意料之外的。 利用环境噪声观察地球内部并检索快速可靠模型的能力将影响多个领域，例如：自然资源勘探、地下水文学、纯地球科学、考古学、地下开发、军事/安全研究以及太空/行星探索。这项研究的智力价值集中在测试以下假设：可以从环境噪声的表面记录中提取 50 至 100 米深度的 m 尺度的精确 3D 地下 P 波和 S 波速度模型。 为了验证这一假设，该研究将开发一种新颖的3D ANT方法，并通过数值模拟和现场实验验证该方法。 使用环境噪声进行 3D 地下成像带来了与环境噪声的不可控频率内容和传播方向相关的固有挑战。 然而，环境噪声富含低频能量，与目前使用有源 3D 全波形反演 (FWI) 方法相比，可以实现更深层次的成像。 因此，3D ANT 与有源 3D FWI 结合使用时，将提供目前无法获得的深度的高分辨率图像。 3D ANT 方法需要从紧密间隔的表面传感器的 2D 网格收集环境噪声记录。 噪声记录将用于提取每对可能的传感器之间的实验相关函数。然后，3D 粘弹性波动方程将用于获得合成相关函数，该函数将与使用高斯-牛顿 FWI 方法提取 3D 地下模型的实验函数进行匹配。 3D ANT 算法的优化将包括对现场测试配置（即传感器的数量和间距）和环境噪声特性（即频率内容和方位角分布）的参数研究。 最终，选择了两个具有真实情况且特征明确的现场站点来在现实条件下测试该方法。 只有我们成功地培训学生将其带入未来、传播研究结果并开发从业者可以在行业中使用的工具，这项研究才能实现最广泛的影响。我们的目标是应对这些挑战，同时扩大妇女对自然灾害工程的参与。 灾害工程旨在改变民用基础设施的设计和修复方式，使社区和个人能够更好地抵御自然灾害的破坏性影响，研究表明，当 STEMM 领域具有直接的社会影响时，女性对它们更感兴趣。 这项工作将有助于培养多元化、才华横溢的未来工程师群体的兴趣和目标的自然交集，这些工程师拥有对彻底改变自然灾害工程至关重要的技能，例如编码、分析大数据和超级计算。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。