A physics-based neural network approach for geophysical inversions

用于地球物理反演的基于物理的神经网络方法

• 批准号: 2309920
• 负责人: Victor Tsai
• 金额: $ 43.54万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2309920&HistoricalAwards=false
• 关键词: physics; based; neural; network; approach

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.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. This project is co-funded by the Directorate for Geosciences to support AI/ML advancement in the geosciences.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.

地球物理成像为我们的大部分地质和火山历史造成了贡献，而不是共同使用多种数据类型，从而导致图像与数据不相容。通过利用新的基于人工工具的约束，可以使地球物理过程允许使用重力和地震数据其他环境，增强的图像可能会掌舵地震和火山爆发等自然危害的最佳反应随着普罗维登斯公立学校的宣传努力，由于每种数据类型数据和模拟的专业专家，在PA RT中，在使用不同类型的地球物理数据方面可将折衷的折衷降至最低，因此在PAR RT中有挑战性的折衷方案，而这些数据类型和模拟的模拟，随着每种数据类型和模拟的专业专家，随着每个数据类型的数据和低收入的折衷，作为此类研究的一部分，它通常在计算上仍然是不切实际的。 Pinn框架利用对基础物理模型的理解来降低构建神经网络的成本，以准确近似于洛杉矶流域的Wisson方程。现场数据可以联合倒置实现真正的地质特征的解决方案，以支持AI/ML Advancementes。此奖项反映了NSF'SF的法定任务，并使用基金会的知识分子优点和更广泛的影响标准。