A Robust Inversion of Induction Logging Responses in Anisotropic Formation Based on Supervised Descent Method

Hao, Peng; Sun, Xiangyang; Nie, Zaiping; Yue, Xizhou; Zhao, Yongpeng

doi:10.1109/lgrs.2021.3078756

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…The imaging process is mainly governed by physical law, with a soft constraint imposed by the learned descent directions. Experiments show that the descent directions trained with simple scatterers can be applied to predict complex targets in inhomogeneous media for various applications, such as geophysical inversion [55]- [58], microwave imaging [21], [59],…”

Section: Learning With Physics Modelsmentioning

confidence: 99%

Physics Embedded Machine Learning for Electromagnetic Data Imaging

Guo¹,

Huang²,

Li³

et al. 2022

Preprint

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Electromagnetic (EM) imaging is widely applied in sensing for security, biomedicine, geophysics, and various industries. It is an ill-posed inverse problem whose solution is usually computationally expensive.Machine learning (ML) techniques and especially deep learning (DL) show potential in fast and accurate imaging. However, the high performance of purely data-driven approaches relies on constructing a training set that is statistically consistent with practical scenarios, which is often not possible in EM imaging tasks. Consequently, generalizability becomes a major concern. On the other hand, physical principles underlie EM phenomena and provide baselines for current imaging techniques. To benefit from prior knowledge in big data and the theoretical constraint of physical laws, physics embedded ML methods for EM imaging have become the focus of a large body of recent work. This article surveys various schemes to incorporate physics in learning-based EM imaging. We first introduce background on EM imaging and basic formulations of the inverse problem. We then focus on three types of strategies combining physics and ML for linear and nonlinear imaging and discuss their advantages and limitations. Finally, we conclude with open challenges and possible ways forward in this fast-developing field. Our aim is to facilitate the study of intelligent EM imaging methods that will be efficient, interpretable and controllable.

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