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.
