基于U-Net的一维大地电磁反演方法
One-Dimensional Magnetotelluric Data Inversion Based on U-Net

大地电磁法在地球物理勘探中扮演着关键的角色，能够通过地表观测数据反演地出下电性结构的分布。然而，传统的大地电磁反演方法存在一些明显的缺点，例如对初始模型的依赖性以及反演过程中较高的计算消耗。为了解决这些问题，文章提出了一种基于深度学习的反演方法。该方法利用神经网络出色的非线性拟合能力，借助构建的大量样本集，实现了对大地电磁数据的一维演。这种方法不仅能够克服传统反演方法的缺陷，还能够通过大规模样本的学习提高反演的准确性和效率。在网络训练完成后，本文对提出的方法进行了详细验证，证明了方法的可靠性和稳健性。
The magnetotelluric method plays a key role in geophysical exploration, allowing for the inversion of subsurface electrical structures based on surface observation data. However, traditional magnetotelluric inversion methods have some obvious drawbacks, such as reliance on initial models and high computational costs. To address these issues, this paper proposes a deep learning based inversion method. This method utilizes the excellent nonlinear fitting capability of neural networks and, with the aid of a large sample set, achieves one-dimensional inversion of magnetotelluric data. This approach not only overcomes the shortcomings of traditional inversion methods but also improves inversion accuracy and efficiency through learning from a large number of samples. After completing network training, this paper conducts detailed validation of the proposed method, demonstrating its reliability and robustness.