PINNtomo：使用物理信息神经网络的Seismic tomography
PINNtomo: Seismic tomography using physics-informed neural networks
摘要：
利用透射数据的地震走时层析成像被广泛用于从全球到局部尺度的地球内部成像。在地震成像中，它用于获得后续深度偏移或全波形反演的速度模型。此外，井间层析成像已成功应用于各种应用，包括矿物勘探、储层监测以及CO2注入和封存。传统的层析成像技术受到许多限制，包括使用对波传播的物理不可知的平滑正则化器。在这里，我们提出了一种新的断层扫描方法来解决这些挑战，使用科学机器学习领域的发展。使用地震台站观测到的地震走时覆盖部分计算模型，我们训练神经网络近似的走时因子和速度场，受物理通知正则化形成的因子程函方程。这使我们能够更好地补偿不适定性的层析成像问题相比，传统的方法和结果在一些其他有吸引力的功能，包括计算效率。我们通过地面地震和跨孔几何形状的合成测试显示了所提出的方法的有效性及其能力。与传统技术相反，我们发现所提出的方法的性能是不可知的初始速度模型的选择。
英文摘要：
Seismic traveltime tomography using transmission data is widely used to image the Earth's interior from global to local scales. In seismic imaging, it is used to obtain velocity models for subsequent depth-migration or full-waveform inversion. In addition, cross-hole tomography has been successfully applied for a variety of applications, including mineral exploration, reservoir monitoring, and CO2 injection and sequestration. Conventional tomography techniques suffer from a number of limitations, including the use of a smoothing regularizer that is agnostic to the physics of wave propagation. Here, we propose a novel tomography method to address these challenges using developments in the field of scientific machine learning. Using seismic traveltimes observed at seismic stations covering part of the computational model, we train neural networks to approximate the traveltime factor and the velocity fields, subject to the physics-informed regularizer formed by the factored eikonal equation. This allows us to better compensate for the ill-posedness of the tomography problem compared to conventional methods and results in a number of other attractive features, including computational efficiency. We show the efficacy of the proposed method and its capabilities through synthetic tests for surface seismic and cross-hole geometries. Contrary to conventional techniques, we find the performance of the proposed method to be agnostic to the choice of the initial velocity model.
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