NSF Award
2309920
Geophysical imaging has contributed to much of our understanding of the Earth and its geologic, tectonic, and volcanic history. However, most geophysical imaging utilizes single data types rather than using multiple data types jointly, resulting in images that can be incompatible with other data. Such incompatibilities can lead to misleading or erroneous inferences about the Earth and the underlying geophysical processes. This project investigates a method for allowing seamless usage of both gravity and seismic data jointly by leveraging new artificial intelligence tools that have physics based constraints. The method promises to be easily and efficiently applied to many different geophysical imaging problems and therefore potentially improve our understanding of Earth’s tectonic history. In other contexts, the enhanced images may help determine how to best respond to natural hazards like earthquakes and volcanic eruptions. This work also helps connect the artificial intelligence and geophysics scientific communities, two communities that would benefit from more interactions with each other. In addition, the project supports graduate and undergraduate students, as well as outreach efforts in the Providence public schools that includes minority and low-income high school students.<br/><br/>Joint inversions that robustly minimize the tradeoffs inherent in using different types of geophysical data are challenging to implement, in part due to the specialized expertise needed for each data type, the unique difficulties in setting up each type of inversion, and the quantity of data and simulations that must be analyzed as part of such studies. Machine learning has made great strides in addressing all three of the above challenges, but it still often remains computationally impractical to implement robustly and this is true of joint inversions of full-waveform seismic and gravity data. This work uses a new machine learning framework called physics informed neural networks (PINNs) to implement joint inversions of such data. The PINN framework leverages an understanding of the underlying physical model to reduce the cost of building a neural network to accurately approximate the wave propagation or Poisson equation governing the relevant data. One test target of the PINN-based joint inversion methodology is using field data from the Los Angeles Basin, where numerous prior in situ geological and structural field data can be used to test whether the joint inversion accomplishes the goal of improving the resolution of true geologic features. <br/><br/>This project is co-funded by the Directorate for Geosciences to support AI/ML advancement in the geosciences.<br/><br/>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.
