A dual-module machine learning scheme is proposed to reconstruct inhomogeneous scatterers with high contrasts and large electrical dimensions. The first nonlinear mapping module (NMM) is an extreme learning machine (ELM), which is used to convert the measured scattered fields at the receiver arrays into the preliminary images of the scatterers. The second image-enhancing module (IEM) is a convolutional neural network (CNN), which is used to refine further the images from the NMM to obtain high-accuracy pixel-based model parameter distribution in the inversion domain. Compared with the traditional approximate methods such as backpropagation, the NMM-IEM machine learning can produce the preliminary image with a much higher accuracy but the unknown weight matrices of the ELM are only solved once during training. Hence, the IEM connected to the NMM has a simple architecture and can be trained at a rather low cost. The performance of the proposed dual-module NMM-IEM scheme and the conventional variational Born iterative method is compared in terms of inversion of scatterers with different electrical sizes and contrasts. Meanwhile, the NMM-IEM is also assessed for the inversion of scatterers with high contrasts and large electrical dimensions and experimental data. Finally, the NMM-IEM is compared with the CNNs used in the previous works.

中文翻译：

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双模块 NMM-IEM 加工学习用于高对比度和大电气尺寸的非均匀散射体的快速电磁反演

提出了一种双模块机器学习方案来重建具有高对比度和大电尺寸的非均匀散射体。第一个非线性映射模块 (NMM) 是一个极限学习机 (ELM)，用于将在接收器阵列处测量的散射场转换为散射体的初步图像。第二个图像增强模块 (IEM) 是卷积神经网络 (CNN)，用于进一步细化来自 NMM 的图像，以获得反演域中基于像素的高精度模型参数分布。与反向传播等传统的近似方法相比，NMM-IEM 机器学习可以以更高的精度生成初步图像，但 ELM 的未知权重矩阵在训练过程中只求解一次。因此，连接到 NMM 的 IEM 具有简单的架构，并且可以以相当低的成本进行训练。所提出的双模 NMM-IEM 方案和传统的变分 Born 迭代方法的性能在具有不同电尺寸和对比度的散射体的反演方面进行了比较。同时，NMM-IEM 还评估了具有高对比度和大电气尺寸的散射体的反演和实验数据。最后，将 NMM-IEM 与之前工作中使用的 CNN 进行比较。NMM-IEM 还评估了具有高对比度和大电气尺寸和实验数据的散射体的反演。最后，将 NMM-IEM 与之前工作中使用的 CNN 进行比较。NMM-IEM 还评估了具有高对比度和大电气尺寸和实验数据的散射体的反演。最后，将 NMM-IEM 与之前工作中使用的 CNN 进行比较。