Magnetic Resonance Sounding Signal Research Articles

Evaluating the applicability of magnetic resonance sounding for groundwater exploration at selected locations in Aswan, Egypt

ABSTRACT Magnetic resonance sounding (MRS) was performed to evaluate its applicability for characterising groundwater at selected locations in Aswan Governorate, Egypt. The MRS mainly detects a weak but noticeable alternating magnetic field as MRS signals when the proton spin in groundwater produces non-magnetic moments and rotates around the geomagnetic field, indicating the detection of groundwater. The MRS system was first correlated at a piezometer with a known water table depth (~80 m). Subsequently, three MRS sounding stations with unknown water tables were tested at Qism Aswan at ~ 18 km from the piezometer using NUMIS Auto magnetic resonance sounding system following the MRS site common practices. The MRS data were subjected to adaptive notch filtering to remove spiky noise and potential power line harmonics, and finally enhance the MRS signals at the selected stations. The water depth (~75–95 m) estimated from the MRS data around the piezometer largely coincides with the actual depth (~80 m) at the piezometer itself. A transient electromagnetic (TEM) profile performed near the measured MRS stations indicates no water content. Unlike the MRS data for NNL-1, the data for NNL-2 and NNL-3 match well with TEM data at shallow depths. The evaluation generally indicates that the MRS method is applicable for groundwater exploration at the selected piezometer and the other three stations. However, the method necessarily requires additional denoising efforts to comprehensively characterise groundwater occurrence and aquifer parameters.

Research on the Detection Method of MRS Signal Initial Amplitude Based on Chaotic Detection System

When using magnetic resonance sounding (MRS) method for groundwater, the detection accuracy is often affected by electromagnetic noise. The chaos method can achieve signal extraction at a lower signal-to-noise ratio. However, this method has many problems when used to obtain MRS signals. Aiming at the attenuation characteristics of MRS signals, in this paper we propose a pre-segmentation method of MRS signals to improve the accuracy of chaos detection. In addition, a chaotic detection system model suitable for MRS signal recognition was constructed, and a calculation method for center-covered was used to improve the efficiency of determining the chaotic state. Through a large amount of simulation data, we have verified that this method can reliably extract MRS signals with a signal-to-noise ratio (SNR) of -30 dB, which provides technical support for the MRS method to work with a relatively high electromagnetic noise.

Harmonic Elimination and Magnetic Resonance Sounding Signal Extraction Based on Matching Pursuit Algorithm

Magnetic resonance sounding (MRS) is a non-invasive, direct, and quantitative geophysical method for detecting groundwater, and has been widely used in groundwater survey, water resource assessment, and disaster water source forecasting. However, the MRS signal is weak (nV level) and highly susceptible to environmental noise, such as random noise and power-line harmonics, resulting in reduced quality of received data. Achieving reliable extraction of MRS signals under strong noise is difficult. To solve this problem, we propose a matching pursuit algorithm based on sparse decomposition theory for data noise suppression and MRS signal extraction. In accordance with the characteristics of the signal and noise, an oscillating atomic library is constructed as a sparse dictionary to realize signal sparse decomposition. A two-step denoising strategy is proposed to reconstruct the power-line harmonics and then extract the MRS signal. We simulated synthetic data with different signal-to-noise ratios (SNRs), relaxation times, and Larmor frequencies. Our results show that the proposed algorithm can effectively remove power-line harmonics and reduce random noise. SNR is significantly improved by up to 35.6 dB after denoising. The effectiveness and superiority of the proposed algorithm are further verified by the measured data and through comparison with the singular spectrum analysis algorithm and harmonic modeling cancellation algorithm.

Harmonic noise cancellation of magnetic resonance sounding signal based on wavelet modulus maximum

Magnetic resonance sounding (MRS) signals are often severely distorted by noise, primarily the power-line harmonics which originates from anthropogenic interferences. Noise cancellation is a particularly important issue for the application of the MRS method. In order to reduce the harmonic peaks as well as the noise floor between the 50 Hz harmonics effectively, a method based on the wavelet modulus maximum is proposed. The method is based on marking the local modulus maximum points in the wavelet transform coefficients and removed the modulus maximum contributed by noise for MRS signal reconstruction. The optimum wavelet basis for noise cancelling is determined through simulations with synthetic signals. The accuracy and stability of the proposed method are investigated using synthetic signals embedded in both artificial and field-recorded noise. In addition, the results of the test on field data show the proposed method can effectively improve the fitting accuracy of the signal parameters and obtain stable inversion results.

Contribution of Electromagnetic Shielding and the Bloch–Siegert Effect to Magnetic-Resonance Sounding

Abstract —Calibration experiments of magnetic-resonance sounding (MRS) were carried out on the ice of the Ob’ Reservoir. For the first time, a comparative quantitative analysis of the contribution of electromagnetic shielding and the Bloch–Siegert effect to the MRS signal was carried out, and the asymmetry of this contribution when detuning from the resonant frequency was investigated. The results of theoretical calculations without regard to the Bloch–Siegert effect differ about three times from the experimental data at the maximum intensity of the radio frequency pulse for the experimental data. At the same time, when taking into account the interference of electromagnetic shielding and the Bloch–Siegert effect, the results of calculating the amplitude and phase of the signal best approximate the field experimental data. Based on the good agreement between the experiment and the theoretical calculations taking into account the interference of electromagnetic shielding and the Bloch–Siegert effect, it is demonstrated that this model should be used to solve the direct and inverse problems of MRS of underground aquifers. The results obtained allow us to calibrate the MRS method and use it not only as an indicator of underground water but also as a means of quantitative measurement. The necessity of taking into account the Bloch–Siegert effect in real field conditions is demonstrated by the example of MRS studies in the Ebro River valley (Spain). When taking into account only electromagnetic shielding (without regard to the Bloch–Siegert effect), the signal amplitude differs nearly twice at the maximum pulse intensity for experimental data, and the signal phase differs 12 times at the maximum pulse intensity. Therefore, in previously published works, approximation of the experimental data using the model without regard to the Bloch–Siegert effect was made by adding layers at depths close to the maximum for this type of antenna (approximately 50 m). In reality, however, these layers do not exist; they appear only as an artifact of the model without regard to the Bloch–Siegert effect. The best approximation of the experimental data can be achieved by taking into account the interference of electromagnetic shielding and the Bloch–Siegert effect.

Removal of a series of spikes from magnetic resonance sounding signal by combining empirical mode decomposition and wavelet thresholding.

The magnetic resonance sounding (MRS) signal typically suffers from low signal-to-noise ratio (SNR). The MRS signal is severely distorted by noise, primarily harmonic and spiky noise. In terms of despiking, wavelet thresholding (WT) reconstructs the distorted content of the MRS signal, following isolation and elimination of the spiky sequence. However, WT cannot restore the MRS signal content completely when a series of spikes occurs within a given period of time. To solve this problem, a combined method of empirical mode decomposition (EMD) and WT for the removal of a series of spikes is proposed. EMD is first applied to decompose the noisy signal into several different components. The spikes that occur within a period of time are separated, the components without spikes are retained, and the components containing spiky events are selected and further processed by WT. After successively computing the wavelet coefficients of the selected components, the coefficients related to the spikes are isolated by threshold processing, and the subsequent wavelet reconstruction yields the sequence with the spikes removed. Finally, the denoised signal is obtained by adding the processed and retained components. The simulations on synthetic signals corrupted by artificial and real noise show that the proposed method improves the SNR with an accompanying improvement in the retrieval of the signal parameters. Moreover, the comparison results of the proposed and the WT methods suggest that the combined method efficiently removes a series of spikes.

Deep Learning for Denoising: An Attempt to Recover the Effective Magnetic Resonance Sounding Signal in the Presence of High Level Noise

Magnetic resonance sounding (MRS) measurements commonly suffer from a notably low signal-to-noise ratio (SNR). In recent years, many denoising methods have been developed, which have demonstrated the useful capability to improve the SNR. However, when MRS measurements are implemented on the sites with high noise levels, i.e., the human living environment, the conventional methods are helpless to recover the effective MRS signal, which is submerged in extensive environmental noise. In addition, the conventional methods that depend on signal models and the corresponding prior assumptions commonly rely on manual experience, which brings obstacles to the automation and efficiency of signal processing. To solve the above problems, we attempt to apply an intelligent denoising framework with a novel neural network as the basic tool for deep learning, called Dn-ResUnet in this article. The network extracts the features of the MRS signal through the encoder and decoder layers of the Dn-ResUnet structure, in which residual learning is adopted to accelerate the training process and improve denoising performance. Once the training is completed, the deep learning realizes adaptive denoising with no need for 1) prior assumptions of the MRS signal and noise; 2) optimal filter parameter tuning; or 3) expensive time cost. The comparison experiments demonstrate that the Dn-ResUnet model provides superior noise cancellation performance, especially it can replace the conventional methods to recover the effective MRS signals in noise levels down to an SNR of −30 dB. In addition, the noise attenuation tests are performed on synthetic and real data. The results show that the framework of deep learning yields a convincing performance in MRS signal denoising.

Simulation of Magnetic Resonance Sounding With a Delayed Adiabatic Pulse

Magnetic resonance sounding (MRS) is used to identify groundwater by exciting and measuring the nuclear magnetic resonance of hydrogen nuclei in subsurface aquifers. However, the MRS response is particularly weak and has a low signal-to-noise ratio (SNR); therefore, signal enhancement methods, such as adiabatic pulses, are essential for MRS applications with strong interference. The key challenges of utilizing this novel method are to increase the MRS signal response and sensitivity for field measurements. In this letter, we report an improved transmitting mode of a commonly used adiabatic half-passage (AHP) pulse by combining this pulse with an earlier fixed frequency off-resonant pulse of short duration, thereby increasing the theoretical MRS sensitivity. Simulation results demonstrate that the novel approach could be used to further improve the signal responses, i.e., in terms of the SNR and potential resolution, for MRS exploration of high-noise environments.

An On-Site Harmonic Noise Cancellation Antenna With a Multinode Loop for Magnetic Resonance Sounding Measurement

Magnetic resonance sounding (MRS) is a geophysical method to detect aquifers directly in the earth’s magnetic field, which offers the benefit to evaluate groundwater qualitatively and quantitatively. Due to the influence of powerline harmonic noise, the reliability of obtaining the weak MRS signal (10–9 V) is severely limited with a square antenna or a conventional <xref ref-type="fig" rid="fig8" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">figure-8</xref> antenna. To realize the high signal-to-noise ratio (SNR) measurement of groundwater, we first propose a multinode antenna consisting of four <xref ref-type="fig" rid="fig8" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">figure-8</xref> antennas connected around nodes, which has better geometric symmetry and antiphase magnetic dipoles to cancel the magnetic fluxes caused by noise. The simulation results show that the noise suppression effect of the new antenna is better than that of the square antenna and <xref ref-type="fig" rid="fig8" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">figure-8</xref> antenna and is independent of the layout angle. We successfully applied the multinode antenna to test the noise-canceling ability in the urban environment and the detection of field underground aquifers, which verified the superiority of the new antenna in improving the efficiency and the SNR. To be specific, the adoption of this antenna can increase the detection efficiency by 4–8 times and improve the SNR by 10 dB with the same sensitivity compared with the conventional antennas. The utilization of on-site noise suppression characteristic in multinode antennas provides a novel idea for MRS with high SNR, especially in areas with severe noise interference.

Random noise suppression of magnetic resonance sounding signal based on modified short-time Fourier transform

Magnetic resonance sounding (MRS) has the advantage of detecting groundwater content directly without drilling, but the signal-to-noise ratio (SNR) is extremely low which limits the application of the method. Most of the current researches focus on eliminating spikes and powerline harmonic noise in the MRS signal, whereas the influence of random noise cannot be ignored even though it is difficult to suppress due to the irregularity. The common method to eliminate MRS random noise is stacking which requires extensive measurement repetition at the cost of detection efficiency, and it is insufficient when employed in a high-level noise surrounding. To solve this problem, we propose a modified short-time Fourier transform(MSTFT) method, in which used is the short-time Fourier transform on the analytical signal instead of the real-valued signal to obtain the high-precision time-frequency distribution of MRS signal, followed by extracting the time-frequency domain peak amplitude and peak phase to reconstruct the signal and suppress the random noise. The performance of the proposed method is tested on synthetic envelope signals and field data. The using of the MSTFT method to handle a single recording can suppress the random noise and extract MRS signals when SNR is more than –17.21 dB. Compared with the stacking method, the MSTFT achieves an 27.88dB increase of SNR and more accurate parameter estimation. The findings of this study lay a good foundation for obtaining exact groundwater distribution by utilizing magnetic resonance sounding.

Magnetic resonance sounding for the estimate of hydrogeologic characteristics of chalk aquifers (North of France)

The hydrogeological characteristics are required for investigating the modelling processes. The transmissivity and hydraulic conductivity values which are obtained from the inverse methods and pumping tests are not representative in the northern part of France where the major aquifer is composed of the chalk deposits. In addition, these methods are costly and time-consuming. In the north of the Paris Basin and by using the magnetic resonance sounding (MRS), the chalk aquifer reveals the vertical distribution of the porosity, the hydraulic conductivity, and the transmissivity. The decay constants of the MRS signal which have been identified from the MRS analysis are considered parameters which control the permeability and the transmissivity values. Five MRS surveys were realized and performed by the electrical resistivity tomography (ERT) aiming to characterize the lithology variation and the thickness of the hydrogeological units. This comparison shows the power of MRS methods of the chalk aquifer in the characterization of the hydrogeological system in the north of France. The water content which is deduced from the MRS highlights a heterogeneous distribution in the aquifer and the water content can reach 35%. Results which are deduced from the pumping test and the MRS are consistent. Indeed, the transmissivity values vary generally between 10−3 and 10−2 m2/s. This comparison shows the utility of the multidisciplinary approaches in the characterization of the chalk system in the north of France.

Electromagnetic Noise Suppression of Magnetic Resonance Sounding Combined with Data Acquisition and Multi-Frame Spectral Subtraction in the Frequency Domain

As an advanced groundwater detection method, magnetic resonance sounding (MRS) has received more and more attention. However, the biggest challenge is that MRS measurements always suffer with a bad signal-to-noise ratio (SNR). Aiming at the problem of noise interference in MRS measurement, we propose a novel noise-suppression approach based on the combination of data acquisition and multi-frame spectral subtraction (DA-MFSS). The pure ambient noise from the measurement area is first collected by the receiving coil, and then the noisy MRS signal is recorded following the pulse moments transmitting. The procedure of the pure noise and the noisy MRS signal acquisition will be repeated several times. Then, the pure noise and the noisy signal are averaged to preliminarily suppress the noise. Secondly, the averaged pure noise and the noisy signal are divided into multiple frames. The framed signal is transformed into the frequency domain and the spectral subtraction method is applied to further suppress the electromagnetic noise embedded in the noisy MRS signal. Finally, the de-noised signal is recovered by the overlap-add method and inverse Fourier transformation. The approach was examined by numerical simulation and field measurements. After applying the proposed approach, the SNR of the MRS data was improved by 16.89 dB and both the random noise and the harmonic noise were well suppressed.

Magnetic resonance sounding measurements as posterior information to condition hydrological model parameters: Application to a hard-rock headwater catchment

In headwater catchment, the calibration of hydrological models is complex due to the scarcity of data in mountainous areas. Here, an innovative methodology is developed to condition hydrological model parameters by using magnetic resonance sounding (MRS) measurements in combination with stream flow rate data. MRS has the specificity in the various geophysical imaging techniques of being mainly sensitive to the vertical distribution of water content among the subsurface. In a way very similar to hydraulic head observations, these local distributions of water content may serve as information in a hydrological model to pattern subsurface flow by seeking model parameters. Simulations are run with different sets of parameters of a hydrological model. Each simulation provides as an output a 4-D map (3-D spatial plus time) of the vertical water content distributions over the whole catchment and their fluctuations over time. This output is then used to simulate the MRS signal that would be produced by the estimated water content. The simulated MRS signal is compared to measured MRS data to determine which hydrological simulations (which model parameters) are close to observations. The approach is applied on a hard-rock headwater catchment housing a very shallow and thin aquifer where an MRS survey covers the whole studied site. Hydraulic parameters of an integrated hydrological model of the catchment are spatially distributed by zones with uniform values, the prior delineation of the zones being guided by pedological studies. As MRS measurements supply local but spatially distributed information, the method conditions the various zones on their parameter values in a much better way than the classical (in headwater catchments) measure of the stream flow rate at the outlet of the system. Finally, hydrological simulation and time-dependent MRS forward calculations can help identifying possible locations for MRS stations to monitor the transient behavior of the hydrological state of the catchment.

Random noise suppression and parameter estimation for Magnetic Resonance Sounding signal based on maximum likelihood estimation

Magnetic resonance sounding (MRS) is a promising geophysical method for direct detection and quantification of groundwater. However, the application of MRS is considerably limited due to its vulnerability to electromagnetic interference. In this paper, the statistical method of maximum likelihood estimation (MLE) is introduced to estimate nuclear magnetic resonance (NMR) parameters and suppress random noise in MRS. Using synthetic NMR signals and noise models, we analyse the factors that influence the denoising effect of MLE. The results show that MLE denoising can be effective even when the random noise deviates from a Gaussian distribution. A multi-exponential NMR signal may reduce the accuracy of MLE-based parameter estimation if a bad initial guess is used, but it has little effect on the suppression of random noise. Harmonic noise can reduce the effect of MLE, while impulsive noise has little effect on MLE. In addition, increasing the recording time or frequency increases the amount of data of the MRS signal, thus improving the effectiveness of the MLE method. Field examples show that this method can effectively suppress random noise interference with less stacking compared with traditional methods, thus greatly shortening the working time of MRS and improving its working efficiency.

Time-lapse magnetic resonance sounding measurements for numerical modeling of water flow in variably saturated media

We presented an innovative hydrogeophysical approach that allows numerical modeling of water flow in a variably saturated media. In our model, we approximated the subsurface by horizontally stratified porous media. The model output was a time varying water content profile. Then, we compared the water content provided by the model with the water content measurements carried out using the time-lapse Magnetic Resonance Sounding (MRS) method. Each MRS sounding provided a water content profile in the unsaturated zone down to twenty meters. The time shift between the profiles corresponded to the time lapse between individual MRS soundings. We minimized the discrepancy between the observed and the modeled MRS signals by varying hydraulic parameters of soil layers in the water flow model. For measuring and processing MRS data, we used NUMIS MRS instrument and SAMOVAR software. We carried out water flow modeling with HYDRUS-1D software. This paper reports our results and summarizes the limitations of the MRS method applied to water content measurements in the unsaturated zone.

A Theoretical Study of Underground Magnetic Resonance Sounding for the Advanced Detection of Water Influxes in Tunnels

Based on surface magnetic resonance sounding (MRS), a relationship is proposed to express the MRS response signal with a vertical coil for the MRS method for a whole underground space model. Firstly, the declination and inclination characteristics of the Earth's magnetic field and the coil normal angle are studied by deriving the angle rotation matrix. Surprisingly, the results indicate that the MRS signal can be effectively improved by changing the normal direction of the coil perpendicular to that of the Earth's magnetic field. The advanced detection distance of the underground MRS method is closely related to the exciting pulse moment and the receiving sensitivity. Hence, a larger pulse moment and high receiving sensitivity correspond to a longer advanced detection distance. However, the limited transmitting moment will reduce the advanced detection distance. In the research coincident loop with the coil 2 m by 2 m square is employed. In order to overcome the noises with 1 nV and 100 nV level, the turns of loop should be 100 turns and 400 turns, respectively. Finally, the numerical simulation results verify the feasibility of underground whole MRS theory and resolution analysis method for the advanced detection of water-inrush disasters in mines and tunnels.

Numerical simulation and analyze of magnetic resonance sounding with adiabatic pulse for groundwater exploration

Magnetic resonance sounding (MRS) technology used to detect groundwater directly and quantificationally, which owns the advantages of rich information and low cost. In these years, it has shown significant potential applications in hydrological detections. Considering the traditional measurements with the geomagnetic field, the nano-valt MRS signals are easy to suppress to environmental noise. As one of the MRS signal enhancement methods, the adiabatic pulse was quite popular recently. It is transmitted with variable frequency and amplitude pulse satisfying the adiabatic condition, which can enhance the signal amplitude and signal-to-noise ratio several times. However, there are only a few reports about this method, especially its geophysical modeling. Thus, we introduce the calculating progress of transverse magnetization and kernel function for this method. By employing the interpolation, the computation cost of forward modeling is obviously reduced. Moreover, several different settings of adiabatic transmitting factors are also proceeded to obtain certain results. In conclusion, a pulse moment with 80 ms transmitting duration and 30 quality factor produces a maximum signal amplitude that is enhanced 16.56 times for deep areas. The research in this paper could provide powerful support for MRS method used in noisy environments.

Random Noise Suppression of Magnetic Resonance Sounding Data with Intensive Sampling Sparse Reconstruction and Kernel Regression Estimation

The magnetic resonance sounding (MRS) method is a non-invasive, efficient and advanced geophysical method for groundwater detection. However, the MRS signal received by the coil sensor is extremely susceptible to electromagnetic noise interference. In MRS data processing, random noise suppression of noisy MRS data is an important research aspect. We propose an approach for intensive sampling sparse reconstruction (ISSR) and kernel regression estimation (KRE) to suppress random noise. The approach is based on variable frequency sampling, numerical integration and statistical signal processing combined with kernel regression estimation. In order to realize the approach, we proposed three specific sparse reconstructions, namely rectangular sparse reconstruction, trapezoidal sparse reconstruction and Simpson sparse reconstruction. To solve the distortion of peaks and valleys after sparse reconstruction, we introduced the KRE to deal with the processed data by the ISSR. Further, the simulation and field experiments demonstrate that the ISSR-KRE approach is a feasible and effective way to suppress random noise. Besides, we find that rectangular sparse reconstruction and trapezoidal sparse reconstruction are superior to Simpson sparse reconstruction in terms of noise suppression effect, and sampling frequency is positively correlated with signal-to-noise improvement ratio (SNIR). In one case of field experiment, the standard deviation of noisy MRS data was reduced from 1200.80 nV to 570.01 nV by the ISSR-KRE approach. The proposed approach provides theoretical support for random noise suppression and contributes to the development of MRS instrument with low power consumption and high efficiency. In the future, we will integrate the approach into MRS instrument and attempt to utilize them to eliminate harmonic noise from power line.

Random Noise Suppression of Magnetic Resonance Sounding Oscillating Signal by Combining Empirical Mode Decomposition and Time-Frequency Peak Filtering

Magnetic resonance sounding (MRS) signals are always corrupted by random noise. Although time-frequency peak filtering (TFPF) has been proven to be an effective method to suppress the random noise, it shows shortcomings when processing the oscillating high-frequency MRS signal at about 2 kHz. In this study, a new method combining empirical mode decomposition (EMD) and TFPF is proposed to overcome the TFPF limitation when processing the MRS oscillating signal. With the help of EMD decomposition characteristics, the random-noise-corrupted MRS oscillating signal is first decomposed into several different components which contain frequencies ranging from the highest to the lowest ones. Then, the components which do not have signal frequency are discarded to bring down the level of random noise. The residual components are further processed by TFPF, respectively, based on the theory of instantaneous frequency estimation and the property of noise accumulation. Finally, the de-noised result is obtained by reconstructing the processed components. The numerical simulations on synthetic signals embedded in both artificial noise and real noise show the combined method can improve the signal-to-noise ratios and reduce the uncertainties of signal parameters. In addition, the combined method is applied following a standard processing scheme in field data, and better results are also obtained.

Design of Meter-Scale Antenna and Signal Detection System for Underground Magnetic Resonance Sounding in Mines

Magnetic resonance sounding (MRS) is a novel geophysical method to detect groundwater directly. By applying this method to underground projects in mines and tunnels, warning information can be provided on water bodies that are hidden in front prior to excavation and thus reduce the risk of casualties and accidents. However, unlike its application to ground surfaces, the application of MRS to underground environments is constrained by the narrow space, quite weak MRS signal, and complex electromagnetic interferences with high intensities in mines. Focusing on the special requirements of underground MRS (UMRS) detection, this study proposes the use of an antenna with different turn numbers, which employs a separated transmitter and receiver. We designed a stationary coil with stable performance parameters and with a side length of 2 m, a matching circuit based on a Q-switch and a multi-stage broad/narrowband mixed filter that can cancel out most electromagnetic noise. In addition, noises in the pass-band are further eliminated by adopting statistical criteria and harmonic modeling and stacking, all of which together allow weak UMRS signals to be reliably detected. Finally, we conducted a field case study of the UMRS measurement in the Wujiagou Mine in Shanxi Province, China, with known water bodies. Our results show that the method proposed in this study can be used to obtain UMRS signals in narrow mine environments, and the inverted hydrological information generally agrees with the actual situation. Thus, we conclude that the UMRS method proposed in this study can be used for predicting hazardous water bodies at a distance of 7–9 m in front of the wall for underground mining projects.