Semi-airborne electromagnetic method (SAEM) is an emerging geophysical exploration method, which can be efficiently carried out in complex terrain conditions. SAEM signals are often mixed with various noise interference, which seriously affects the signal quality. However, the amount of SAEM data is extremely large, the number of signals of a single survey line is in the millions, and the existing noise elimination methods usually intercept the signals in segments and then process them, which is inefficient and has strong subjective factors. To address the above problems, this article proposes a recurrent self-coding neural network (RSCN) for one-stop noise cancellation of the whole measurement line signal. The method adopts the gated recurrent unit (GRU) to obtain the full waveform state and adopts the data segmentation technique to obtain the small-scale data and perform the convolutional coding. The state information as well as the coded data is used to extract important features using an attention mechanism and decoded. The self-encoding structure is updated cyclically to realize one-stop noise cancellation for the whole measurement line data. The test results of simulated and measured data show that the method proposed in this article has a good denoising effect on the full survey line SAEM data and is more adaptable to the SAEM data than the existing noise reduction methods.

Low-field nuclear magnetic resonance (NMR) is a widely employed technique in geoscience. However, signal-to-noise ratio (SNR) is always an issue in low-field NMR measurement, which should be carefully addressed to ensure the accuracy of relaxation spectrum reconstruction for subsequent petro-physical interpretation and applications. This paper presents a novel denoising method for low-field NMR measurements utilizing double sparsity dictionary learning (DSDL), which is an unsupervised machine learning approach. The elaborate trained dictionary models could be directly implemented to denoise raw spin echoes with different signal-to-noise ratios (SNRs). After denoising, digital phase-sensitive detection (DPSD) and phase rotation are conducted to obtain the multi-exponential decay signals and fundamental noise signals for subsequent spectrum reconstruction. In this study, numerical simulations is mainly conducted. The pre-set T2 spectrum models are built to derive raw spin echoes with different SNRs through forward modeling, and then are used to train the double sparsity dictionary models. The dictionary models are trained on raw echo datasets with different Gaussian distributed noise and same porosity, and tested on raw echo datasets with same Gaussian noise and different porosities. The echo data before and after denosing are all inverted by using Singular Value Decomposition (SVD) method, which is a non-objective inversion algorithm to avoid the parameter selection like commonly used regularization inversion algorithm. All the inverted results are compared with the forwarding spectrum models. It is demonstrated that the DSDL method could effectively improve the quality of low-field NMR measurements, resulting in accurate relaxation spectrum.

Low-field nuclear magnetic resonance (LF-NMR) technology provides robust technical support for geophysical applications, including reservoir explorationand oil logging analysis. The inversion spectra of LF-NMR signals, particularly in two-dimensional (2D) form, reveal crucial geological information such as porosity, permeability, and other essential geologic parameters. However, the acquisition of geological parameters in geoscience relies on the scientific analysis, interpretation, and division of the inversion results, affecting the accuracy of the detection results. In LF-NMR, geophysical parameters are obtained by interpreting the fluid type and estimating the saturation of the inverted spectrum. Therefore, it is essential to develop corresponding qualitative and quantitative classification strategies, especially in cases involving fluid component aliasing. To address these geophysical issues comprehensively, we have created an improved fuzzy clustering algorithm using the local direction centrality (Fuzzy-CDC) based on 2D LF-NMR parameter spectra to observe the distribution characteristics of saturation for samples with overlapping states. Additionally, a fuzzy membership degree was introduced to enhance qualitative and quantitative abilities in assigning saturations to fluid components. To validate the clustering capability of this approach, we conducted simulation and actual experiments on four-phase fluids and water-gasoline two-phase fluids, respectively, comparing the results with conventional clustering methods. The improved method exhibits superior abilities and provided precise saturation estimation, yielding a relative error of 7.40% in simulation and 11.04% in actual experiments. In conclusion, our research significantly enhanced the analysis capabilities of 2D LF-NMR geophysical parameters while demonstrating the potential for pore fluid assessment and component classification in field geological exploration.

Recently, surface nuclear magnetic resonance (SNMR) detection work for groundwater has been performed in complex and noisy urban environments as this method directly provides quantitative water content distribution information. However, the weak signal, the high noise and the inconvenience of the large size magnetic source (MS) restrict the development of this work. The application of electrical source (ES) with low topographic requirement enables SNMR measurements in obstacle areas. To further improve the signal strength and depth resolution, we propose configurations based on dual ESs. Three long wires are laid side by side on the surface, and all the ends of each wire are grounded by electrodes. The middle one is used for signal acquisition, and the two on either side are used as dual ESs to implement NMR excitation of groundwater. We have simulated 3D magnetic field, kernel, and signal with an expanded range of effective actions. It has been proved by simulations that the signal amplitude can be increased to more than 10 times that of MS by dual ESs with the coincident current flows (CF-ESs) in deep water modeling over 100 meters. Its theoretically determinable depth is 50m deeper than MS. The CF-ESs consumes only half the pulse intensity to achieve the same amplitude as previous single ES. The new approach shows great potential to expand SNMR measurements in challenging urban settings.

Soil moisture (SM) is a key state variable in terrestrial systems because it controls the exchange of water and energy between the continental surface and the atmosphere. Nuclear magnetic resonance (NMR) technology is widely used for the analysis of porous media in SM due to its unique sensitivity to hydrogen protons. Unlike traditional laboratory NMR systems, unilateral NMR (UNMR) systems allow for the placement of detection targets outside the sensor space, thereby enabling in situ detection capabilities. However, in the existing designs of UNMR sensors, the magnetic field location is typically determined after the sensor has been designed. In this study, a novel UNMR sensor design scheme is proposed based on a simplified analytical model (SAM) to solve this problem. In contrast to conventional practices, this scheme places a primary emphasis on the identification of detection positions as its initial step, followed by the computation of magnet structure parameters. Concurrently, the mechanical design of the proposed UNMR sensor offers a more adaptable approach to regulation. The scheme consists of two components: magnetic field calculation and optimization of structural parameters. Notably, the proposed model exhibits a remarkable enhancement in calculation efficiency, surpassing the baseline by more than 70 times within a single iteration, compared with the traditional analytical model (TAM). The goodness of fit between the measured magnetic field distribution and the optimized results surpasses 0.99, thereby providing additional evidence of the sensor’s effectiveness. In addition, the sensor’s performance is demonstrated through measurements conducted on samples with varying SM content.

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.

UMRS can effectively explore the aquifer information in front of the tunnel face. However, the complex tunnel construction environment will reduce the reliability of the interpretation results. We propose and implement using joint inversion of UMRS-TEM data in tunnel detection using HMC for the first time to solve this problem. Joint inversion can update the UMRS kernel in real-time during the iterative process, obtain accurate aquifer information and resistivity structure, and improve the reliability of the interpretation of inversion. HMC is a MCMC method that uses Hamiltonian dynamics to propose future states in Markov chains. It can explore the target distribution more effectively, and it also has the advantage that MCMC can obtain a posteriori PDF of parameters, we believe that PDF information can effectively judge the accuracy of inversion results. We validated the effectiveness and practicality of the joint inversion using synthetic and observed data, and the experimental results showed its advantages in accuracy and noise resistance compared to a single UMRS inversion. In the field example, the water content PDF of the joint inversion is significantly increased by 31.8% compared with that of the single inversion. We analyzed the correlation between water content, resistivity, and layer interfaces and discovered some correlation laws. HMC improves the efficiency of computational and provides assistance for further research on the influence of parameters on inversion results. Our conclusions can improve the safety of tunnel construction and provide effective technical support for avoiding hydrogeological disasters in tunnels.

Magnetic resonance sounding (MRS) measurements used for detecting the subsurface aquifers commonly suffer from the notoriously low signal-to-noise ratio (SNR). The conventional approaches usually deal with the specific noise components step by step to improve the SNR, but the denoising effect is limited. MRS denoising methods based on neural networks show great potential in recovering the effective signals at low SNRs, but need large amount of high-quality labeled training datasets and have limitations in the application of measured data. And the denoising model trained for specific noise without noise type identification leads to excessive noise cancellation and low effective signal fidelity. To address these issues, we propose an intelligent denoising process by combining the noise detection method using support vector machine (SVM) and the intelligent denoising models for suppressing the specific noise. We first consider the discriminative features from the noisy signal in time and frequency domains for specific types of noise to be identified, so as to construct 3D feature vectors. Second, the noise detection model using SVM with radial basis function (RBF) kernel function is trained on the training samples. And the cross-validation technique is adapted to assess the performance of the noise detectors in the training process. Finally, the combination of the well-trained SVM noise detectors and the Dn-ResUnet models for specific noise is applied to process the noisy MRS data. The results of the simulation and field experiments show that our proposed method provides a one-stop flow for automatic noise identification and intelligent noise removal, which achieves a better denoising performance compared with the existing denoising methods.

Underground magnetic resonance sounding (UMRS) has the problem of low data quantity and low data quality in tunnel detection, a probabilistic statistical method is needed for data inversion. We used Hamiltonian Monte Carlo (HMC) to obtain UMRS inversion results, and we implemented a “tempered” scheme in HMC, in order to obtain higher efficiency and accuracy of inversion. This is the first time tempered HMC (THMC) has been applied to UMRS inversion and tunnel detection. It adds the neglected temperature term into HMC, effectively improving the escape ability, and improving computational efficiency. First, UMRS and THMC methods are briefly introduced in this article. Then, we investigate the relationship between different temperatures and the ability of THMC to jump out of the local optimal and find the temperature range suitable for UMRS inversion. We designed a series of schemes to test the performance of two methods and demonstrate that UMRS inversion using THMC has clear advantages. The inversion results of synthetic data show that THMC has higher efficiency and accuracy than HMC under extreme conditions, such as weak signal and high noise. Finally, we introduce the general situation of the study site and apply the two methods to the observation data inversion. THMC obtains results that are more consistent with the actual situation, which proves that it has strong practicability. We believe that THMC is more suitable for UMRS data inversion than HMC. THMC is helpful in improving the detection accuracy and efficiency of UMRS, ensuring the safety of tunnel construction, and preventing the delay of the construction period.

Magnetic resonance sounding (MRS) is the only technology capable of noninvasive direct detection of subsurface water content. However, MRS often suffers from weak signals (10-9V) and may prohibit application in complex interference environments. To effectively recover the MRS signals regardless of whether they are corrupted by single spike or cascades of spikes, a method based on proximate wavelet coefficient recovery technology is proposed. Compared with the conventional methods, the proposed method improves the measurement efficiency by eliminating the demand for multiple recordings. The accuracy and stability of the proposed method are investigated using simulated signals from complex noise environments and geological conditions. Simulation results show significant improvements in signal-to-noise ratio (SNR) and retrieval of signal parameters, which demonstrate the validity of the proposed method. Moreover, the proposed method is applied on the synthetic signals embedded in noise-only data recorded in the urban environment. The results show that the proposed method can maintain a balance between preserving the signal component and suppressing complex spiky noise. The proposed method is implemented for the field data and the results demonstrate good practicality. The research results provide technical support for rapid detection in complex interference environments.

Magnetic resonance sounding (MRS) is an effective method for groundwater detection due to its direct sensitivity to the protons. However, in the actual measurement, the strong ringing current is always collected, which distorted the data samples in the early time. Different from other noise corrupting the signal, the ringing interference cannot be removed by the de-noising methods in the signal post-processing. The current processing strategy is to delete the distorted data samples, but the fitting error of the initial amplitude will be increased, which reduces the accuracy of the inversion result. Therefore, this article attempts to propose a new post-processing method for ringing noise suppression of MRS signal to address the problem that cannot be solved by the traditional method. Deep learning was applied to verify the idea. The mapping relationship between the signal containing ringing and the ringing noise is established thereby removing the noise to restore the ringing-free signal. The numerical simulations and the field experiments prove that the proposed method based on deep learning may suppress the ringing noise without losing valid information. Compared with the traditional processing strategy, the fitting error of the initial amplitude can be reduced to less than 10% and the root mean square error (RMSE) is less than 0.015 by using the new post-processing method, which significantly improves the accuracy of the measurement results.

Low-field (LF) nuclear magnetic resonance (NMR) technology has been widely used in reservoir identification, NMR logging and other geophysical exploration fields. Due to the advantage of providing abundant parameter information related to fluid (e.g. the longitudinal and transverse relaxation time,T1andT2), it requires the advanced inversion approach for data interpretation from the measured echo data. As the present inversion schemes show weakness, especially in fluid typing and quantitative analysis, we developed a novel imaging scheme based on the classical Butler–Reeds–Dawson (BRD) algorithm frame. Employing the discrepancy principle, the new method aims to improve the selection for regularized parameter for inversion, and further increases the imaging accuracy. To verify the ability and reliability of this method with strong interference, we compare the fluid typing inversion results of simulated data for different noise level between the conventional and improved approach with variable data acquisition wait time and echo spacing. The results indicate that the estimation accuracy ofT1-T2spectra for updated scheme gets about 8 percent improvement even with high-noise environments. To further evaluate the advantage, this algorithm is also tested with real oil-water fluid experimental data, which yields a distribution of fluid properties that matches the sample. In conclusion, the research in this paper shows significant for LF-NMR data interpretation, in particular for charactering pore-fluid and quantifying organic contamination in subsurface sediments in the geological survey.

Surface nuclear magnetic resonance (SNMR) is the only non-invasive geophysical method for direct water detection, and is widely used in groundwater exploration. However, the dead time arising from the coupling effect of the transmitting magnetic field on the receiving coil results in the loss of the high level early free induction decay (FID) signal. To solve this problem, we propose a decoupling method based on symmetrical transmitting coils. A mathematical analysis was conducted to describe the decoupling principle and detection theory based on the novel coil structure. Simulation experiments that performed comparisons with the traditional method showed the potential of the new method for the detection of shallow thin aquifers with short relaxation time signals. Further studies of the detection effects with different coil key structure parameters were conducted and a three-layer coil was developed. Laboratory tests showed that the dead time was shortened to 1 ms, which made it possible to acquire the FID signal earlier.

The surface nuclear magnetic resonance (SNMR) technique is the only method that can directly detect aquiferous layers. By detecting and interpreting multiple parameters such as water content and relaxation time, it can provide rich information related to pore structure and hydraulic conductivity, and directly and effectively predict water source geological hazards such as groundwater outbursts and collapse of cavern areas. The conventional SNMR method has a weak signal, so the prepolarization (PP) SNMR signal enhancement technique has been rapidly developed to improve the adaptability of this method in urban and underground engineering fields. However, the present PP SNMR modeling and data interpretation methods are based on the conventional steady-state background field SNMR theory, and their accuracy is limited. In this article, the researchers construct an instantaneous polarization (IP) model for the accurate interpretation of the PP SNMR response through an in-depth analysis of the active PP field source properties, which not only effectively improves the inversion accuracy of the subsurface aquiferous layer, but also helps to separate the longitudinal relaxation parameters of the aquiferous layer carried by the variable PP field itself. The method is used for the interpretation of experimental data from known urban hydrological sites, and the results are generally consistent with the real situation, which confirms the effectiveness of the method proposed in this article.

Transient electromagnetic (TEM) method is a geophysical technique suitable for efficient detection of urban underground space, which can be used for advance detection of road collapse and underground water inrush accidents. In actual urban geological detection, engineers hope to be able to quickly characterize the underground space in 3-D, so as to assess the potential underground subsidence area or water inrush area risk. However, due to the complexity of urban geology, it is difficult for conventional 3-D imaging techniques to take into account both imaging efficiency and imaging accuracy. To address this issue, this article proposes a fast, high-resolution TEM quasi-3-D imaging strategy suitable for urban geology. We convert the TEM data into pseudoseismic wavelet data to identify the geological correlation of the 3-D spatial survey area, and then impose adaptive spatial constraints on the 3-D imaging. Simulation and application case results show that, compared with conventional imaging techniques, our proposed new strategy can effectively improve imaging resolution while ensuring computational efficiency. The research results provide a feasible technical solution for rapid high-resolution 3-D detection of urban geology.

Machine learning inversion (ML-inversion) has been widely used in the interpretation of geophysical data. However, because the transient electromagnetic (TEM) is not sensitive to the response of the small-size or large-depth abnormal body, there are some problems such as poor imaging accuracy and low-resolution when directly processing raw TEM data with ML method. To solve this problem, we propose to use the pseudo seismic wavelet (PSW) data obtained from TEM wave field transformation to perform TEM ML-inversion. Simulation and example application verify that compared with the TEM data, the PSW data has higher recognition sensitivity to the electrical changes of underground anomalies, and the ML-inversion based on the PSW data can significantly improve the TEM imaging accuracy. Our study demonstrates that the PSW data can be used to achieve TEM ML-inversion, which may provide a new way for the further combination of electromagnetic data processing and ML technology.

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.

Surface magnetic resonance sounding (SMRS) is a unique geophysical method that can directly track and quantify groundwater using the remote sensing technique. The conventional SMRS used a closed coil as the magnetic field source. In the field measurement, a large-size coil is laid on the ground or even multiple coils are set for array detection. This reduces the detection efficiency and consumes labor inevitably. The current study proposes an electrical source (ES), a new mode for exciting the groundwater. It is a long wire placed on the ground and connected to the Earth by two grounding electrodes. The ES has the advantages of labor-saving, time-saving, and better environmental adaptability. Moreover, the magnetic field generated by the ES can transmit farther than the traditional magnetic source. Based on this, we matched different receivers for the ES and simulated the kernel, the resolution, and the signal with different configurations. The results show that using the long grounding wire as both the electrical transmitter and receiver can obtain higher signal amplitude and better resolution than the traditional magnetic configuration. In addition, it has the ability to break through the detection depth of the conventional method.

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.

Electromagnetic geophysical methods are widely applied in urban shallow exploration, considering their convenience and noninvasiveness. As one of the emerging techniques, polarizing surface nuclear magnetic resonance (SNMR) shows significant potential for water-based target detections. However, problems involving weak signal response and strong noise interference are challenging to avoid. Therefore, numerous studies focused on suppressing the electromagnetic interference and improving the effective electromagnetic control accuracy to achieve high-precision shallow imaging. On this basis, we proposed a new magnetic-field enhancement technology that explored double-polarization coils, instead of the single loop, to improve the applications of polarizing SNMR. By controlling the current for double-polarization coils, the magnetic field, which is twice as strong as the single coil, could be provided. As a result, the sensitivities and signal responses for subsurface detection sensitive areas with the same power consumption were further enhanced. Considering the SNMR research, we also identified that this new configuration with double-polarization coil can effectively improve the resolution of the shallow depth because it compensates for the magnitude of the static magnetic field in the center. Based on the theoretical analysis, we developed the instrument and, for the first time, realized the accurate interpretation of the real ground soil in an urban environment, verifying the effectiveness of the proposed configuration and the reliability of the system.

Surface nuclear magnetic resonance (SNMR) technology is a geophysical method to directly measure the water content and saturated porosity of aquifers by exciting the nuclear magnetic resonance (NMR) phenomenon of hydrogen nuclei in groundwater. With the natural Earth magnetic field B0 as the background detection field, the SNMR signals, which usually exist only within a range of tens of nanovolts, are very likely to be submerged in widespread environmental noise. The prepolarization (PP) method with artificial application of an active field to enhance NMR signals has been gradually applied to the SNMR field, and this method is expected to overcome existing problems and to gain further development in more high-noise detection environments, e.g., urban engineering detection. However, when PP is introduced to SNMR, difficulties in data interpretation focus on the theoretical formula derivation and model construction. In this paper, we studied the state of the detected target under a PP field. Based on currently available measurement processes, we established the corresponding PP-SNMR forward equations and modeling for groundwater. It was proven by simulation and actual measurements that the research in this study can interpret actual field detection data. In addition, compared with the conventional PP-SNMR theory, the proposed improved method can effectively avoid detected aquifer misestimation. The results gained in this study compensate for the shortcomings of the current PP-SNMR theory, which is of significance to the development and application of high-power PP-SNMR technology.

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,

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.

During the excavation of underground tunnels and in ore mining, accidents related to water bursts occasionally occur. As the only technique used for the direct detection of groundwater, the nuclear magnetic resonant (NMR) method has advantages for the detection of disaster-inducing water flows. Unfortunately, the amplitudes of underground NMR (UNMR) signals are in the range of some tens of nanovolts (10-9V) or even picovolts (10-12V), and thus extremely susceptible to environmental noise. By increasing the macromagnetic moment of groundwater, both adiabatic pulses and prepolarization (PP) methods have been employed in surface NMR. However, when using either method, it is difficult to achieve substantial signal enhancements over large volumes. For maximum signal amplitudes, we integrated these two approaches and derived the forward formulas with adiabatic pulses under PP for UNMR. In comparison with existing methods, this new model can achieve high sensitivity and broad responses. (A 6-m antenna attains a 10-5V signal level for a homogeneous subsurface with 0.2 m3/m3water content.) Thus, better resolution could also be provided even in a high-noise place. Overall, the large NMR signals and high resolutions make the combination of adiabatic pulses with PP a valuable approach, which is expected to open up a new application for UNMR.