Accelerated Imaging of 2-D Water-Bearing Structures in MRT Data Based on the SVD-UNet

• Published in: IEEE transactions on geoscience and remote sensing Vol. 62; pp. 1 - 13
• Main Authors: Lin, Tingting, Wang, Qingyue, Wang, Yunzhi, Miao, Ruixin, Ren, Chunpeng, Jiang, Chuandong
• Format: Journal Article
• Language: English
• Published: New York The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024
• Subjects: Algorithms; Artificial neural networks; Deep learning; Geology; Geophysical exploration; Imaging; Imaging techniques; Kernel functions; Machine learning; Magnetic resonance; Magnetic resonance imaging; Moisture content; Neural networks; Relaxation time; Singular value decomposition; Spatial distribution; Tomography; Water; Water content
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2024.3414409

Magnetic resonance tomography (MRT) is a geophysical exploration technique that enables the imaging of 2-D or 3-D water-bearing structures, offering distinct advantages, including noninvasiveness, quantifiability, and unique interpretability. Currently, MRT data inversion mainly relies on the Q-time (QT) inversion method. Since this method utilizes the Gauss-Newton iteration to seek the optimal solution, it involves a considerable amount of computational workload, thus consuming a significant amount of time. To overcome this challenge, this study introduces an accelerated imaging method by combining the singular value decomposition (SVD) pseudoinversion algorithm and the deep neural network algorithm. The SVD pseudoinversion algorithm transforms MRT data into a water-bearing feature matrix containing only water content and relaxation time information by introducing a priori forward kernel function. Subsequently, neural network establishes a nonlinear mapping relationship between the water-bearing feature matrix and the spatial distribution of the water content and relaxation time in the subsurface. The SVD pseudoinversion algorithm, by incorporating prior information, mitigates the distribution differences in MRT data caused by geological and measurement parameters. This addresses the limited applicability of deep learning methods under complex geological conditions and multiple measurement schemes. The experimental results demonstrate that the method achieves precise and rapid imaging, while also possessing effectiveness and practicality.