Surface magnetic resonance sounding (MRS) offers the advantages of direct, quantitative, and unique interpretations in the field of groundwater detection. The time-lapse inversion (TLI) 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 TLI. Thus, we propose a new TLI of MRS data weighted by noise-level estimation in this article. Noise-weighted TLI (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.

The ground-airborne frequency-domain electromagnetic method (GAFDEM) is a geophysical technique designed for the efficient exploration of resistivity imaging in areas characterized by complex terrain. Conventional frequency-domain transformation methods are limited in accurately capturing the amplitude and phase of time-varying electromagnetic responses due to various noise and interference. As a result, the resistivity imaging results may be inaccurate and even exhibit false anomalies. To address this issue, we propose a novel orthogonal recursive least squares (ORLS) method. ORLS employs orthogonal signals as reference inputs and incorporates the adaptive filtering algorithm RLS, which enables real-time estimation of the amplitudes and phases of time-varying electromagnetic signals at multiple frequencies. The ORLS method overcomes the limitations of conventional frequency-domain transformation methods, such as the signal stationarity requirement and restricted frequency resolution. By ensuring real-time processing, ORLS enhances the accuracy of parameter estimation while maintaining efficiency. By simulating multifrequency signals and noisy data with different signal-to-noise ratios (SNRs), the effectiveness and stability of the ORLS algorithm are verified. Furthermore, the simulated and measured results are compared with frequency-domain analysis methods such as Fourier transform, which indicate that compared to frequency-domain analysis methods, the ORLS method reduces the average root mean square error (RMSE) of amplitude and phase by 57.79% and 85.97%, respectively, with similar estimation errors for each frequency component and no differences between frequencies. Moreover, the phase results of ORLS are easy to unwrap. Therefore, the GAFDEM data processed by the ORLS method hold significant importance in achieving high-precision underground resistivity imaging.

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.

When using magnetic resonance tomography (MRT) for imaging 2-D or 3-D water-bearing structures in a subsurface, the transmitting frequency must be the same as the Larmor frequency. Due to the inhomogeneity and noise interference in a geomagnetic field, it is difficult to determine the precise Larmor frequency using a magnetometer, resulting in unknown frequency offsets and inaccurate estimations of water content and relaxation time ($T_{2}^{*}$). To solve the 2-D MRT imaging problem in the case of an unknown frequency offset, a frequency cycling method is proposed in this article. This method takes the estimated Larmor frequency as the center, uses two frequencies with the same offset for transmitting, then combines the acquired MRT signals to obtain frequency-cycled data, and finally uses the off-resonance kernel function for inversion. Based on MRT forward modeling and QT inversion, we conduct synthetic data experiments on a complex model with three water-bearing structures and test the 2-D imaging results of the frequency-cycled data. The results show that the water content and$T_{2}^{*}$distribution obtained by the inversion of the frequency-cycled data can accurately reflect the water-bearing structure, which is better than the results of the assumed on-resonance case. In addition, the phase correction method presented in this article significantly improves the accuracy of 2-D MRT estimated aquifer properties under low resistivity conditions. Finally, the validity and accuracy of the frequency cycling method are verified by comparing the inversion results of the data with known drilling data collected in field measurements.

Surface nuclear magnetic resonance (sNMR) can directly and quantitatively detect groundwater, but its application in urban environments faces problems, such as low signal-to-noise ratios (SNRs) and difficulties in laying the coils. This study presents a rotational sNMR measurement scheme to accurately image a frozen urban lake. Through synthetic data experiments, we first demonstrate that sNMR data measured with six rotations can accurately invert underground water-bearing structures. Even when the environmental noise is high, this scheme can reflect the distribution of the water content in a frozen lake. Moreover, due to the small coil size, the inversion result is less affected by the underground resistivity. In field experiments, a large amount of high-quality sNMR data with average SNRs up to 12.8 dB were obtained from a high-noise environment using three reference coils. The 2-D distributions of the water content in the ice, water, and mud layers of the frozen lake were determined using the data measured from six rotations. The water content in the lake was found to be approximately equal to 1 m3/m3. Although there are still some problems with the measurements, such as inaccurate relaxation times and low resolutions in deep areas, further improvements in sNMR and the rotational detection scheme can facilitate the application of this approach to urban groundwater detection.

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.

Magnetic resonance sounding (MRS) has been applied to underground constructions, such as tunnels and mines, to detect and forewarn groundwater sources hidden in front of the mine face, also named disaster water sources. Disaster water sources are mostly found in 2-D or 3-D structures; as such, their spatial distribution characteristics are difficult to reflect accurately by conventional 1-D MRS results. This letter proposes a method for sector-scanning magnetic resonance tomography (MRT) measurement using rotating antennas for 2-D waterbearing structures, such as water-filling conduits, faults, and goafs to obtain the 2-D distribution image of water content and relaxation time (T2) in front of the face by inversion. Through the numerical simulation of conduits, we analyzed the 2-D sensitivity, resolution, and inversion results of the rotating antennas. The imaging result at a high noise level was improved by increasing the number of antenna rotations. We also determined the effects of 2-D MRT on the faults and goafs and discussed the minimum cross-sectional area and maximum distance for reliable imaging of conduits.2-D,

In tunnel construction, to prevent the occurrence of water inrush, the geological conditions of faults and underground rivers must be determined in advance. As a direct detection method of groundwater, magnetic resonance sounding (MRS) has been applied for the advanced detection of water-related hazards in tunnels and mines recently. However, the results of conventional 1-D MRS cannot correctly reflect the spatial distribution characteristics of complicated water-bearing structures. In this article, we propose a measurement scheme using a rotating coil with separated transmitter and receiver loop configuration (SEP) for the 2-D imaging of water-bearing structures, such as faults and conduits. In this scheme, the receiver coil rotates several times, while the transmitter coil, which is separated from the receiver coil by a certain distance, remains stationary. Moreover, all the observed data participate in the inversion to achieve the 2-D magnetic resonance tomography (MRT). Numerical simulations of water-bearing faults are performed, and we compare and analyze the 2-D sensitivity and inversion results of two measurement schemes for a rotating coil, i.e., an overlapping transmitter and receiver loop configuration (OVE) and SEP. The results showed that the imaging results of the SEP are better than those of the OVE because the OVE imaging has a symmetric artifact. Finally, we discuss the influence of the transceiver distance, the resistivity, and the environmental noise on the imaging results. Moreover, the imaging results of the water-bearing conduit at different locations were obtained to validate the effectiveness of the SEP rotating coil measurement scheme.

Magnetic resonance tomography (MRT) is a technique that is used in the 2-D or 3-D detection and imaging of subsurface water-bearing structures based on the principle of surface nuclear magnetic resonance. Currently, the research and application of 3-D MRT is still limited by low measurement efficiency and image resolution. In this paper, a new loop array layout that consists of a coincident transmitting (Tx) and receiving (Rx) loop and an array of Rx loops is proposed to achieve high-efficiency MRT data acquisition and 3-D imaging. A number of water-bearing structures with various shapes (X, L, + and S models) are simulated based on the forward modeling of separated Tx and Rx loops with arbitrary geometries and topographies. Using the complex QT inversion scheme, images of these structures produced by 3-D MRT with the loop array layout are examined. The numerical simulation experiment shows that in low noise conditions, the water content distribution pattern obtained by inversion can reflect the fine details of the water-bearing structure, and an accurate relaxation time (T2*) is provided. As the noise level increases, 3-D MRT images gradually become blurry. Nevertheless, increasing the number of Rx loop arrays can significantly improve the image resolution. Finally, the feasibility of practical applications of 3-D MRT with the loop array layout and feasible methods of improving measurement are discussed.

A new method for detecting risk of tunnel water-induced disasters using magnetic resonance sounding (MRS) is proposed in this letter. The method utilizes magnetic resonance signals that are generated directly from hydrogen protons to achieve the purpose of detecting risk of tunnel water-induced disasters directly and quantitatively. This letter evaluates the potential of this method based on a systematic study involving forward modeling, numerical experiments, and a large-scale physical model test. The relationship between the magnetic resonance signal response in the tunnel and the position and water content of water-bearing structures is obtained by the forward modeling. In the numerical examples, the inversion results are in agreement with the synthetic model. In the physical model test, the inversion results can accurately locate the water-bearing structure, and the water content linearly decreases with the water level of the water-bearing structure, which verifies the feasibility and effectiveness of predictions made based on the MRS data. These results demonstrate that tunnel MRS can be used to anticipate water-induced disasters.