Magnetic resonance sounding (MRS) is one of the technical applications of nuclear magnetic resonance (NMR) used to directly detect and quantify groundwater content. MRS suffers from a low signal-to-noise ratio (SNR) due to the low amplitude of free induction decay (FID) signals and an inability to shield environmental noise. In this article, a time–frequency fully convolutional neural network (TFCN) was proposed to suppress random, harmonic, and spike noise from MRS data. The TFCN parameters were trained with the time–frequency spectrum obtained by the short-time Fourier transform (STFT) of the MRS datasets as the input and the noise-free FID signals as the output. Based on the results of synthetic and field data experiments, the TFCN was compared with existing denoising methods. The results showed that the TFCN extracted the envelope of the FID signals from low-SNR random noise with higher accuracy than other methods. Moreover, the TFCN simultaneously suppressed multiple types of noise and exhibited high computational efficiency.

Underground construction projects such as tunnel construction are at high risk of water-induced disasters. Because this type of disaster poses a serious threat to worker safety and productivity, instruments and methods that can accurately detect the water source are critical. In this study, a water detection instrument that combines Magnetic Resonance Sounding (MRS) and Time-domain Electromagnetic Method (TEM) techniques to yield a joint MRS-TEM interpretation method was developed for narrow underground spaces such as tunnels. Joint modules including a transmitter and receiver were developed based on a dual-purpose and modular design concept to minimize the size and weight of the instrument and consequently facilitate transportation and measurement. Additionally, wireless control and communication technology was implemented to enable inter-module cooperation and simplify instrument wiring, and wireless synchronization was accomplished by implementing a Global Positioning System (GPS)-based timing scheme. The effectiveness and reliability of the instrument were verified via indoor laboratory tests and field measurement signal tests. Furthermore, the practicability of the combined instrument and its interpretation method was verified via a field case performed in a tunnel in Hubei, China.

Magnetic resonance tomography (MRT) has the advantages of direct, quantitative and unique interpretation in the field of groundwater detection. Currently, the inversion of MRT data primarily uses the QT (Q-time) inversion method based on Tikhonov regularization. However, when the heterogeneity of an aquifer is high, and the water content distribution is markedly uneven, this method removes many details in the model and cannot perform uncertainty analysis on the results. To solve these problems, we propose a Bayesian inversion for MRT data based on geostatistics. The method uses previously known geological data, such as drilling, to determine prior information model containing variograms and mixture Gaussian probability distributions for generating many stochastic realizations. Under the Bayesian framework, a modified Markov chain Monte Carlo strategy (MCMC) is used to obtain the posterior probability distributions of subsurface aquifers and hydraulic conductivity, and the results of quantitative uncertainty analysis. By comparing the inversion performance for simulated models, the imaging result of the Bayesian method is found to be markedly more accurate than that of the QT method for subsurface two-dimensional aquifers, particularly in explaining the stochastic model (i.e., the water-bearing model with an uneven distribution of water content). This method can also intuitively quantify the uncertainty of the imaging results, which mitigates the shortcomings of existing inversion methods. This paper also discusses the effects of prior information, number of chains and noise levels on the results, and also validates the effectiveness and practicability of the proposed method using field-measured data.

Wireless power transfer (WPT) is a safe, convenient, and intelligent charging solution for electric vehicles. To address the problem of the susceptibility of transmission efficiency to large uncertainties owing to differences in coil and circuit element processing and actual driving levels, this study proposes the use of adaptive Gaussian process regression (aGPR) for the uncertainty quantification of efficiency. A WPT system efficiency aGPR surrogate model is constructed with a set of selected small-sample data, and the confidence interval and probability density function of the transmission efficiency are predicted. Finally, the reptile search algorithm is used to optimize the structure of the WPT system to improve efficiency.

Surface magnetic resonance sounding (MRS) offers the advantages of direct, quantitative and unique interpretations in the field of groundwater detection. The time-lapse inversion method, with its temporal continuity, has been applied to monitor the time-varying trends of the hydrological parameters of groundwater. However, the ambient noise levels in MRS data fluctuate significantly over time (daily), and the presence of low signal-to-noise ratio (SNR) data can lead to a deterioration in the results of time-lapse inversion. Thus, we propose a new time-lapse inversion of MRS data weighted by noise level estimation in this paper. Noise-weighted time-lapse inversion (NW-TLI) quantifies the reliability of each MRS dataset on the basis of noise estimation residuals and incorporates time-lapse reference weights into the inversion process, thereby ensuring that the hydrological trends are more reasonably constrained by high-SNR data. In synthetic data experiments, we demonstrate that the NW-TLI method effectively mitigates interference from adjacent low-SNR data under various complicated noisy cases. Even with multiple sets of low-SNR MRS data, NW-TLI can provide more accurate hydrological time-varying trends than conventional TLI. Additionally, we assess the impact of the temporal variability of the water-bearing model and the degree of data weighting on the interpretative accuracy and ultimately validate the practicability of the NW-TLI method via field-measured data.

Magnetic resonance sounding (MRS) is a noninvasive geophysical method that allows direct qualitative and quantitative detection of groundwater and it has been used widely to detect shallow groundwater. However, current MRS device structures suffer from tens of milliseconds of dead time due to the effect of coupling between the receiving and transmitting coils, which can lead to early data loss and signal distortion. This is especially true for small multiturn coil structures. To solve this problem, we propose an early free induction decay (FID) signal detection technique with a gradient coil for MRS, which consists of two equidistant receiving coils above and below the transmitting coil. This symmetric distribution eliminates the effect of coil coupling. Based on the proposed technique, a numerical model and a prototype of the principle were constructed. Simulation and experimental results show that the new method effectively suppresses the influence of transceiver coil coupling. The new method based on a gradient coil structure can acquire short relaxation FID signals and the dead time was reduced to 1 ms. The new method provides technical support for efficient and accurate detection of shallow groundwater.