用生成先验提升海洋电磁数据反演精度，避免黑箱模型。
Feature-based Inversion of 2.5D Controlled Source Electromagnetic Data using Generative Priors
- 通过变分自编码器学习导电率分布先验知识
- 反演中结合高斯牛顿法与先验投影，提升成像精度
- 适用于不同测线配置，适合地质建模与资源勘探
本研究探讨基于特征的2.5维可控源海洋电磁（mCSEM）数据反演方法，采用有限差分法（FDM）模拟水平电偶极子（HED）激发响应。不同于直接用神经网络黑箱拟合逆映射，本文采用即插即用策略，仅使用变分自编码器（VAE）学习导电率分布的先验信息。反演过程中，利用高斯牛顿法迭代更新导电率模型，同时通过投影到已学习的VAE解码器空间施加先验约束。该框架保持对数据残差的显式控制，并可灵活适配不同调查配置。数值实验与实际数据测试表明，该方法有效融合先验信息，显著提高重建精度，并展现出良好泛化性能。
原文摘要 · Abstract (English)
In this study, we investigate feature-based 2.5D controlled source marine electromagnetic (mCSEM) data inversion using generative priors. Two-and-half dimensional modeling using finite difference method (FDM) is adopted to compute the response of horizontal electric dipole (HED) excitation. Rather than using a neural network to approximate the entire inverse mapping in a black-box manner, we adopt a plug-andplay strategy in which a variational autoencoder (VAE) is used solely to learn prior information on conductivity distributions. During the inversion process, the conductivity model is iteratively updated using the Gauss Newton method, while the model space is constrained by projections onto the learned VAE decoder. This framework preserves explicit control over data misfit and enables flexible adaptation to different survey configurations. Numerical and field experiments demonstrate that the proposed approach effectively incorporates prior information, improves reconstruction accuracy, and exhibits good generalization performance.
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