scholarly journals A Feasibility Study of 2-D Microwave Thorax Imaging Based on the Supervised Descent Method

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 352
Author(s):  
Haolin Zhang ◽  
Maokun Li ◽  
Fan Yang ◽  
Shenheng Xu ◽  
Yan Yin ◽  
...  
Keyword(s):  
Prior Information ◽  
Structural Information ◽  
Descent Method ◽  
Boundary Integral ◽  
Training Set ◽  
Training Models ◽  
Supervised Descent Method ◽  
One Step ◽  
Human Thorax ◽  
Element Boundary

In this paper, the application of the supervised descent method (SDM) for 2-D microwave thorax imaging is studied. The forward modeling problem is solved by the finite element-boundary integral (FE-BI) method. According to the prior information of human thorax, a 3-ellipse training set is generated offline. Then, the average descent direction between an initial background model and the training models is calculated. Finally, the reconstruction of the testing thorax model is achieved based on the average descent directions online. The feasibility using One-Step SDM for thorax imaging is studied. Numerical results indicate that the structural information of thorax can be reconstructed. It has potential for real-time imaging in future clinical diagnosis.

Application of supervised descent method for 2D magnetotelluric data inversion

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. WA53-WA65
Author(s):  
Rui Guo ◽  
Maokun Li ◽  
Fan Yang ◽  
Shenheng Xu ◽  
Aria Abubakar
Keyword(s):  
Objective Function ◽  
Prior Knowledge ◽  
Descent Method ◽  
Model Reconstruction ◽  
Training Set ◽  
Magnetotelluric Data ◽  
Training Models ◽  
Data Inversion ◽  
Supervised Descent Method ◽  
Descent Directions

The supervised descent method (SDM) is applied to 2D magnetotellurics (MT) data inversion. SDM contains offline training and online prediction. The training set is composed of the models generated according to prior knowledge and the data simulated by MT forward modeling. In the training process, a set of descent directions from an initial model to the training models is learned. In the prediction, model reconstruction is achieved by optimizing an online regularized objective function with a restart scheme, where the learned descent directions and the computed data residual are involved. SDM inversion has the advantages of (1) being more efficient than traditional gradient-descent methods because the computation of local derivatives of the objective function is avoided, (2) incorporating prior uncertain knowledge easier than deterministic inversion approach by generating training models flexibly, and (3) having high generalization ability because the physical modeling can guide the online model reconstruction. Furthermore, a way of designing general training set is introduced, which can be used for training when the prior knowledge is weak. The efficiency and accuracy of this method are validated by two numerical examples. The results indicate that the reconstructed models are consistent with prior information, and the simulated responses agree well with the data. This method also shows good potential to improve the accuracy and efficiency in field MT data inversion.

Application of supervised descent method to transient electromagnetic data inversion

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. E225-E237 ◽  
Author(s):  
Rui Guo ◽  
Maokun Li ◽  
Guangyou Fang ◽  
Fan Yang ◽  
Shenheng Xu ◽  
...  
Keyword(s):  
Prior Information ◽  
Descent Method ◽  
Training Data ◽  
Data Set ◽  
Data Inversion ◽  
Transient Electromagnetic ◽  
Online Computation ◽  
Training Stage ◽  
Highly Nonlinear ◽  
Supervised Descent Method

Inversion plays an important role in transient electromagnetic (TEM) data interpretation. This problem is highly nonlinear and severely ill posed. Gradient-descent methods have been widely used to invert TEM data, and regularization schemes containing prior information are applied to reduce the nonuniqueness and stabilize the inversion. During the inversion, the partial derivatives are repeatedly computed, which is time and memory consuming. Furthermore, regularization schemes can only provide limited prior information. Much prior information from knowledge and experience cannot be directly used in inversion. In this work, we applied the supervised descent method (SDM) to TEM data inversion. This method contains an offline training stage and an online prediction stage. In the training stage, a training data set is generated according to prior information. Then, the average descent direction between a fixed initial model and the training models can be learned by iterative schemes. In the online stage of prediction, the learned descent directions are applied directly into the inversion to update the models. In this manner, one can select models satisfying the data and model misfit. In this study, SDM is applied to model- and pixel-based inversion schemes. Synthetic examples indicate that SDM inversion can not only enhance the accuracy of inversion due to the incorporation of prior information but also largely accelerate the inversion procedure because it avoids the online computation of derivatives.

scholarly journals Phaseless Gauss-Newton Inversion for Microwave Imaging

2020 ◽  
Author(s):  
Chaitanya Narendra ◽  
Puyan Mojabi
Keyword(s):  
Field Data ◽  
Prior Information ◽  
Structural Information ◽  
Microwave Imaging ◽  
The Other ◽  
Total Field ◽  
Field Phase ◽  
Full Data ◽  
L2 Norm ◽  
Phase Data

<p>A phaseless Gauss-Newton inversion (GNI) algorithm is developed for microwave imaging applications. In contrast to full-data microwave imaging inversion that uses complex (magnitude and phase) scattered field data, the proposed phaseless GNI algorithm inverts phaseless (magnitude-only) total field data. This phaseless Gauss-Newton inversion (PGNI) algorithm is augmented with three different forms of regularization, originally developed for complex GNI. First, we use the standard weighted L2 norm total variation multiplicative regularizer which is appropriate when there is no prior information about the object being imaged. We then use two other forms of regularization operators to incorporate prior information about the object being imaged into the PGNI algorithm. The first one, herein referred to as SL-PGNI, incorporates prior information about the expected relative complex permittivity values of the object of interest. The other, referred to as SP-PGNI, incorporates spatial priors (structural information) about the objects being imaged. The use of prior information aims to compensate for the lack of total field phase data. The PGNI, SL-PGNI, and SP-PGNI inversion algorithms are then tested against synthetic and experimental phaseless total field data.</p>

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