A Novel Design of a Unilateral Nuclear Magnetic Resonance Sensor for Soil Moisture Detection Based on a Simplified Analytical Model

• Published in: IEEE transactions on geoscience and remote sensing Vol. 62; pp. 1 - 11
• Main Authors: Lin, Tingting, Wang, Hualiang, Li, Zhengping, Zhu, Jinbao
• Format: Journal Article
• Language: English
• Published: New York IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytical model; Analytical models; Computation; Design; Detection; Goodness of fit; Integrated circuit modeling; Iterative methods; Magnetic circuits; Magnetic field; Magnetic field measurement; Magnetic fields; Mathematical models; NMR; Nuclear magnetic resonance; Optimization; Parameters; Porous media; Protons; Sensors; Soil analysis; Soil moisture; soil moisture (SM); Target detection; uniform magnetic field; unilateral nuclear magnetic resonance (UNMR)
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2023.3340224

Soil moisture (SM) is a key state variable in terrestrial systems because it controls the exchange of water and energy between the continental surface and the atmosphere. Nuclear magnetic resonance (NMR) technology is widely used for the analysis of porous media in SM due to its unique sensitivity to hydrogen protons. Unlike traditional laboratory NMR systems, unilateral NMR (UNMR) systems allow for the placement of detection targets outside the sensor space, thereby enabling in situ detection capabilities. However, in the existing designs of UNMR sensors, the magnetic field location is typically determined after the sensor has been designed. In this study, a novel UNMR sensor design scheme is proposed based on a simplified analytical model (SAM) to solve this problem. In contrast to conventional practices, this scheme places a primary emphasis on the identification of detection positions as its initial step, followed by the computation of magnet structure parameters. Concurrently, the mechanical design of the proposed UNMR sensor offers a more adaptable approach to regulation. The scheme consists of two components: magnetic field calculation and optimization of structural parameters. Notably, the proposed model exhibits a remarkable enhancement in calculation efficiency, surpassing the baseline by more than 70 times within a single iteration, compared with the traditional analytical model (TAM). The goodness of fit between the measured magnetic field distribution and the optimized results surpasses 0.99, thereby providing additional evidence of the sensor's effectiveness. In addition, the sensor's performance is demonstrated through measurements conducted on samples with varying SM content.