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【目的】地面核磁共振(SNMR)找水方法是一种极具发展潜力并且探测效果极佳的地下水探测方法，而传统的地面核磁共振工作方法是利用收发共圈装置激发并接收一组信号来对地下水进行探测，为丰富SNMR的工作模式并提高SNMR方法探测地下水的精度。【方法】通过对SNMR横向梯度线圈装置在地下水探测中的信号特征进行模拟分析，并以天门汉江江滩的场地试验进行验证。【结果】发现横向梯度线圈装置的响应信号与含水量、含水层厚度近似呈正相关关系，与含水层深度近似呈负相关关系。对横向梯度线圈差值分析表明，横向梯度线圈的信号差值零值的出现及其信号峰值所对应的脉冲矩大小可定性反映含水层的深浅。同时，将梯度信号差值与原线圈信号进行联合反演，可以有效提高SNMR方法的反演精度。【结论】说明运用SNMR横向梯度线圈装置来提高探测地下水的准确性是行之有效的。
Abstract：[Objective]The surface nuclear magnetic resonance(SNMR) method for groundwater exploration is a highly promising technique with exceptional detection performance. Traditionally, SNMR operates using a co-located transmitter-receiver coil to excite and receive signals for groundwater detection. The objective is to diversify SNMR operational modes and enhance the accuracy of groundwater detection using SNMR method.[Methods]A simulation analysis was conducted on the signal characteristics of the SNMR transverse gradient coil device in groundwater detection, followed by field experiments in the river beach area along the Han River in Tianmen to validate the result.[Results]The response signal of the transverse gradient coil device showed an approximate positive correlation with water content and aquifer thickness, and a negative correlation with aquifer depth. The difference analysis of the transverse gradient coil signals revealed that the occurrence of zero values in the signal difference, as well as the pulse moment magnitude corresponding to the signal peak, can qualitatively indicate the aquifer depth. Additionally, combining the gradient signal difference with the original coil signal for joint inversion can significantly improve the inversion accuracy of the SNMR method.[Conclusion]The findings demonstrate that utilizing the SNMR transverse gradient coil device is an effective approach to improving the accuracy of groundwater detection.
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基本信息:
DOI：10.13928/j.cnki.wrahe.2025.11.010
中图分类号:P641.7
引用信息:
[1]钱伦,潘剑伟,何胜,等.SNMR横向梯度线圈装置对不同含水层的响应信号特征研究[J].水利水电技术(中英文),2025,56(11):127-139.DOI:10.13928/j.cnki.wrahe.2025.11.010.
基金信息:
国家自然科学基金青年基金资助项目(42004122); 贵州省科技计划资助项目([2020]1Y173)