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 (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.

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.

In the development of urban underground spaces (UUSs), comprehensive exploration of geological conditions is crucial for assessing the structural stability. While geophysical electromagnetic (EM) prospecting plays a pivotal role in this regard, urban settings pose unique challenges due to heightened requirements for measurement efficiency and interpretation precision, compounded by constraints on instrument deployment. To address this issue, this article proposes an innovative approach termed the urban-suitable frequency-domain EM gradient (UFEMG) measurement and quick imaging method. The method involves the measurement of array-type magnetic fields, computation of spatial and frequency gradients, and derivation of magnetic field gradient-based apparent resistivity (MFGBAR). The effectiveness of the method was tested, and the key influencing factors of its application effect were analyzed using a 3-D model. To further validate its practical utility, a UFEMG instrumentation system was designed and implemented. The system integrates multifrequency high-efficiency transmission (MHT) technology to enhance efficiency and vertical resolution during transmission and utilizes a movable and deformable towage receiver array (MDTRA) configuration to ensure efficient magnetic field measurement and precise gradient acquisition, thereby improving lateral resolution. Field surveys conducted at Cultural Square Light Rail Station and Yuhua Park in Changchun, China, demonstrated the effectiveness of the method in detecting subterranean features such as subterranean cavities, artificial ponds, and reservoirs. Comparative analysis with traditional EM prospecting and apparent resistivity imaging results reveals the superior anomaly recognition of the UFEMG method, and the better consistency with known geological information that further affirms its advantages of high efficiency, resolution, and precision. This research demonstrates the significance of the UFEMG method for urban subsurface exploration and provides technical support for enhancing the planning and development of UUS.

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.