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对地球及其地质，构造和火山历史的大部分理解做出了贡献。但是，大多数地球物理成像都使用单个数据类型，而不是共同使用多种数据类型，从而导致图像与其他数据不相容。这种不兼容会导致对地球和基本地球物理过程的误导或错误的推论。该项目研究了一种通过利用具有基于物理约束的新的人工智能工具来允许无缝使用重力和地震数据的方法。该方法有望轻松有效地应用于许多不同的地球物理成像问题，因此有可能提高我们对地球构造历史的理解。在其他情况下，增强的图像可能有助于确定如何最好地应对地震和火山喷发等自然危害。这项工作还有助于连接人工智能和地球物理学科学社区，这两个社区将受益于彼此之间的更多互动。此外，该项目还支持毕业生和本科生，以及包括少数群体和低收入高中生在内的普罗维登斯公立学校的宣传工作。在使用不同类型的地球物理数据中固有的固定固定的固定的折叠构成，在每种数据类型中都需要使用不同类型的数据，这些倒置会构成各种地球物理数据的质疑。作为此类研究的一部分进行了分析。机器学习在解决上述所有三个挑战方面取得了长足的进步，但是强大的实现通常在计算上仍然是不切实际的，这对于全波形地震和重力数据的联合反转是正确的。这项工作使用一个新的机器学习框架，称为物理知情的神经元网络（PINN）来实施此类数据的联合反转。 PINN框架利用对基本物理模型的理解来降低构建神经元网络的成本，以准确近似于波浪传播或泊松方程，以控制相关数据。基于PINN的关节反转方法的一个测试目标是使用来自洛杉矶盆地的现场数据，在该数据中，可以使用许多先前的原位地质和结构场数据来测试联合反转是否实现了改善真正地质特征分辨率的目标。该项目由地球科学局共同资助，以支持地球科学中的AI/ML进步。该奖项反映了NSF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审查标准，认为通过评估被认为是宝贵的支持。