A high precision finite-element forward solver for surface nuclear magnetic resonance incorporating conductivity changes and surface-topography variations

PLoS One. 2022 Mar 17;17(3):e0264235. doi: 10.1371/journal.pone.0264235. eCollection 2022.

Abstract

Surface nuclear magnetic resonance (SNMR) is a geophysical method that can be used directly for detecting groundwater resources, and it has attracted the attention of many scholars. In this paper, we propose a new effective algorithm for numerical modeling of 3D SNMR data for arbitrary topography in a conductive medium. We adopt a total-field algorithm for solving the quasi-static variant of Maxwell's equation and handle a complex-shaped loop source by discretizing the transmitter into electric dipoles, which can be further easily discretized into electric dipoles along the three directions of the Cartesian coordinate system. To solve the 3D SNMR forward-modeling problem quickly and accurately, a new element-integration system based on a new symmetric orthogonal rule is used for calculating the sensitivity (i.e., kernel) functions of all elements. The new rule is based on a special arrangement involving a cubic close-packed lattice structure and is characterized by fast convergence, positive weight, and symmetry. We apply the developed numerical algorithm to SNMR tomography of several typical hydrogeological models. The synthetic results show that higher precision can be achieved with few grids and nodes without increasing the computation time by using the new integration algorithm. In addition, we find that the topography and conductivity can affect the SNMR response, which needs to be considered while interpreting SNMR data.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Electric Conductivity
  • Magnetic Resonance Imaging* / methods
  • Magnetic Resonance Spectroscopy
  • Tomography* / methods

Grants and funding

This research work was supported by the National Natural Science Foundation of China No.42174080. It was also supported by the Natural Science Foundation of Guangxi (No.2020gxnsfaa297079) and the Research Start-up Foundation of Guilin University of Technology (No. RD2100002165) and Guangxi science and technology plan innovation team project (GXNSFGA380004) and the China Postdoctoral Science Foundation funded project (2021MD703820). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.