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.

The joint inversion of gravity and magnetic data can reduce the nonuniqueness problem of potential field data inversion. We propose a gravity and magnetic joint inversion method based on deep learning (DL) combined with measurement data constraint. The framework obtains the gravity and magnetic dataset required for network training by randomly generating the underground structural consistency model and then inputs the dataset into the network for training. Moreover, we add constraints to the measurement data in the training of the network, that is, fitting the data anomalies obtained by the inversion model through forward calculation with the real anomalies, which makes the network more consistent with geophysical theory. In the test phase, the trained network can obtain the inversion results rapidly, and the inversion results of the testing dataset show that this method can obtain better results when applied to the joint inversion of gravity and magnetic fields than the conventional regularized inversion and cross-gradient joint inversion methods. In addition, our method can also distinguish the anomaly conditions in the case of structural inconsistency. Furthermore, we apply this method to actual gravity and magnetic data of Gonghe Basin, Qinghai Province, China, and predict the distribution of dry hot rock related to geothermal resources.

Three-dimensional inversion of gravity and magnetic data can determine the geometric location of anomalous subsurface bodies and quantitatively calculate the size and spatial distribution of the target body’s physical parameters, providing a foundation for subsequent geological interpretation. The Tikhonov regularization method typically controls the model space in gravity and magnetic data processing to reduce the non-uniqueness of inversion problems. However, various regularization forms and the change in weighting coefficient yield generally different inversion results, and the depth resolution of the potential field data is insufficient. This paper proposes an inversion method with random projection to obtain accurate distribution results for physical property parameters. This method can derive physical property parameters by solving inversion problems involving well-posed equations without Tikhonov regularization constraints or constraint terms of the depth weighting function. The original ill-posed equations are transformed into stably solvable low-dimensional well-posed equations by random projection many times, and the high-dimensional inversion solutions are obtained by averaging the solutions of multiple low-dimensional equations. The inversion accuracy and the proposed method resolution are evaluated using theoretical model tests. In addition, this method can constrain the prior geological information such as horizon, tilt Angle and block into the projection process, and realize the joint inversion of gravity and magnetic data and prior geological information, so as to obtain more reliable physical property inversion results. The random projection inversion method is then applied to the magnetic data in Gonghe Basin, Qinghai Province, and the spatial distribution range of deep hot dry rocks is delineated, providing direction for the continued exploration of geothermal resources in this area.

Three-dimensional gravity inversion is an effective method for restoring underground density distribution from gravity anomaly data. Conventional regularization inversion has good data fitting, but its inversion model has insufficient model fitting capabilities due to its low-depth resolution. Although data-driven deep learning-based gravity inversion results significantly improve depth resolution and physical property distribution, it is difficult to ensure the data fitting of the inversion results. Accordingly, this study proposes a three-dimensional gravity data inversion based on enhanced dual U-Net framework (EdU-Net) to solve the above problems, making the inversion results have good model and data fitting performance. The proposed EdU-Net consists of two parts: first, training a large generalization pre-trained network Net I, and then quickly generating an enhanced Net II for the target data through fine-tuning. Additionally, this study adds forward-fitting constraints in the framework’s loss function to reduce the problem of large data-fitting errors in traditional data-driven deep learning inversion. The trained Net II inversion result has better model and data fitting accuracy than Net I. Moreover, by comparing the inversion results of synthetic models, this study demonstrates that the EdU-Net method performs better than traditional deep learning. Finally, this method is applied to the measured data of the Gonghe Basin in Qinghai Province, China, and provides a reasonable explanation for the distribution of hot dry rocks.

As one of the most important geophysical methods for detecting the lunar underground structure of the Moon, the lunar penetrating radar (LPR) is applied to the Chang’e-4 (CE-4) mission to explore the Von Kármán crater on the far side of the Moon. With the Yutu-2 rover continues to move towards the Zhinyu crater at the west, radar will likely detect the fractures created by the impact that formed the Zhinyu Crater. Therefore, the establishment of a comprehensive lunar subsurface structure model with fracture, random media and fractal terrain is crucial for the LPR data processing and the understanding of the lunar geological impact process. In addition, the method that can image the radar data with fractures well has great significance to the interpretation of the LPR data. In this paper, we establish a comprehensive lunar subsurface structure model firstly, the considering factors including fractures, random media, fractal terrain, and the radar response are calculated through forward modeling. Secondly, according to the high resolution and the sensitive to inhomogeneities of LPR data, we design a set of processing flow including plane-wave destruction, velocity analysis based on the focusing analysis method and migration imaging based on the velocity continuation. Finally, the results including simulation data and Antarctic fracture data are used to verify the effectiveness of imaging method.

Three upcoming Martian missions will deploy a ground-penetrating radar (GPR) to reveal the fine-resolution subsurface structure and dielectric properties of materials beneath the surface. Numerical forward simulations of radar echo using a model of the near-surface structure at the landing site can provide a valuable reference for processing and interpretation of future radar data collected on Mars. In this study, based on the geological information of the Jezero crater, a detailed stratigraphic model of the near-surface structure is derived, which includes several key features, for example, the randomness of the medium, terrain, and cracks. To identify correctly the reflections of subsurface interfaces and fractures from the radar image, a$v(z$) f-k migration is carried out, the performance of which is evaluated using the GPR data obtained near Antarctic Zhongshan Station since the electrical properties of Antarctic glaciers and Martian materials are to some extent comparable. The results in this work show that compared with common migration algorithm, the$v(z$) f-k method not only improves the clarity of radar image but also provides the permittivity profiles to infer the composition of the substrate, leading to a better understanding of Martian near-surface geology.

China’s Tianwen-1 probe carrying the Zhurong rover has successfully landed on the southern Utopia Planitia of Mars. The Zhurong rover is first equipped with a full-polarimetric Mars Rover Penetrating Radar (FP-RoPeR) system, aiming to map the fine structure and potential water-ice distribution of Martian regolith. However, the ground experiment on earth indicates that the RoPeR data is severely affected by noises, clutters, and signal misalignment. More importantly, an imbalance between the data from two cross-polarized channels is observed which is considered to be the interference of the rover. The interference prevents the accurate assessment and analysis of field FP-RoPeR data and may even become the traps in the interpretations of future radar data from Mars. In this article, we firstly analyze the interference in detail and achieve the suppressions of noises, clutters, and signal misalignment; subsequently, a polarized orientation calibration method is proposed to calibrate the imbalance of cross-polarized channels. Finally, we introduce a field experiment at the Ulanhada volcanic geopark in Inner Mongolia Autonomous Region, China. Based on the field RoPeR data and the proposed processing methods, we propose three types of data processing strategies for different aims and present how to use field FP-RoPeR data to analyze subsurface structures and properties.

The lunar penetrating radar (LPR) carried on the Chang E-3 Yutu Rover observations at 500 MHz successfully reveals the thickness and geological structure of the shallow lunar regolith. Previous work used the calculated permittivity and the migration image to estimate the layer interface and local target. In this letter, a least-squares Kirchhoff migration (LSM) algorithm is presented to reduce the migration artifacts (i.e., recording footprints) due to incomplete data. Both synthetic and CE-3 LPR data are used to test the effectiveness of the LSM method. The numerical results show that LSM can achieve a much superior image quality than Kirchhoff migration and frequency-wavenumber (F-K) migration results. Based on the LSM result of CE-3 LPR data, we can find evident strata reflection and continuous distinct layers in the shallow lunar subsurface below the CE-3 site. It provides a more reliable and robust way to interpret the lunar regolith structure.

Detection of human activities in complex environments such as through wall by ultrawideband radar has many important applications in security, vital rescue, and so on. It is much more difficult to detect vital signs of moving human beings than static ones. In this letter, we build a model for moving targets and apply the time domain finite element method to simulate single-input multiple-outputs (SIMO) radar data. Human respiration is modeled by changing body size and physical parameters. The background removal is performed for radar data. Then, we use the back projection to reconstruct the consecutive target locations, which constitute the moving path, leading to a curve carrying vital signs in the radar image. Since SIMO radar data are multivariate, we use multivariate empirical mode decomposition (MEMD) and fast Fourier transform to separate and extract the respiratory characteristic frequencies. The reconstructed frequency coincides with that in the original model. The result shows that the combination of SIMO radar and MEMD can effectively identify the moving path of the human being behind the wall and extract vital signs.

Lunar penetrating radar (LPR) is an important way to evaluate the geological structure of the subsurface of the moon. The Chang'E-3 has utilized LPR, which is equipped on the lunar rover named Yutu, to obtain the shallow lunar regolith structure in Mare Imbrium. The previous result provides a unique opportunity to map the subsurface structure and vertical distribution of the lunar regolith with high resolution. In order to evaluate the LPR data, the study of lunar regolith media is of great significance for understanding the material composition of the lunar regolith structure. In this letter, we focus on the lunar regolith quantitative random model and parameter inversion with LPR synthetic data. First, based on the Apollo drilling core data, we build the lunar regolith quantitative random model with clipped Gaussian random field theory. It can be used to model the discrete-valued random field with a given correlation structure. Then, we combine radar wave impedance and stochastic inversion methods to carry out LPR data inversion and parameter estimation. The results mostly provide reliable information on the lunar regolith layer structure and local details with high resolution. This letter presents a further research strategy for lunar probe and deep-space detection with LPR.

The sounding depth is one of the most important parameters of Ground Penetrating Radar (GPR), and it is also the main limitation of GPR's applications. A pseudo random coded GPR for deep investigation is described in this paper. It could achieve a sounding depth more than 130 meters. A long Golay sequences and TGA (time-gain amplifier) technology are utilized to increase the SNR of faint echoes from deep layer underground, and enhance the sounding depth. The platform could be either a vehicle or a large airship. Field tests were carried out on Kubuqi desert located in Inner Mongolia, north-west of china, to verify the detection ability of this GPR system. The sounding results agree well with the results from the drilling and resistivity logging.

In this letter, we apply a time and frequency analysis method based on the complete ensemble empirical mode decomposition (CEEMD) method in ground-penetrating radar (GPR) signal processing. It decomposes the GPR signal into a sum of oscillatory components, with guaranteed positive and smoothly varying instantaneous frequencies. The key idea of this method relies on averaging the modes obtained by empirical mode decomposition (EMD) applied to several realizations of Gaussian white noise added to the original signal. It can solve the mode-mixing problem in the EMD method and improve the resolution of ensemble EMD (EEMD) when the signal has a low signal-to-noise ratio. First, we analyze the difference between the basic theory of EMD, EEMD, and CEEMD. Then, we compare the time and frequency analysis with Hilbert–Huang transform to test the results of different methods. The synthetic and real GPR data demonstrate that CEEMD promises higher spectral–spatial resolution than the other two EMD methods in GPR signal denoising and target extraction. Its decomposition is complete, with a numerically negligible error.

In this paper, we combine the multiple-input-multiple-output (MIMO) array antenna technology with a multipolarization component in a ground penetrating radar (GPR) system to improve target detection accuracy. The MIMO technology introduced in previous literature is widely applied in radar and other wireless communication fields. Here, we apply the MIMO technology with a “plane-wave like” (PWL) source that uses array antennas with small spacing to emit a pulse source at the same time in GPR detection. First, we analyze the physical mechanism of the MIMO GPR system with a “PWL” source to improve the target detection resolution. Then, we carry out a numerical simulation with a finite-difference time-domain method in 1-D and 2-D array antennas to compare the imaging results of the MIMO and traditional GPR systems. Finally, the synthetic data MIMO GPR experiment with a step-frequency GPR system is implemented. Compared with the traditional GPR system, our results demonstrate that the MIMO GPR system with a multipolarization detection mode can overcome the influence of target radar cross sections and antenna radiation directions, and improve target detection accuracy effectively. Meanwhile, the synthetic MIMO GPR system also provides a good idea to improve the system performance and reduce system design requirements and the manufacture cost.

Ultrawideband (UWB) radar technology has emerged as one of the preferred choices for through-wall detection due to its high range resolution and good penetration. The resolution is a result of high bandwidth of UWB radar and helpful for better separation of multiple targets in complex environment. Detection of human targets through a wall is interesting in many applications. One significant characteristic of human is the periodic motion, such as breathing and limb movement. In this letter, we apply the UWB radar system in through-wall human detection and present the methods based on fast Fourier transform and S transform to detect and identify the human's life characteristic. In particular, we can extract the center frequencies of life signals and locate the position of human targets from experimental data with high accuracy. Compared with other research studies in through-wall detection, this letter is concentrated in the processing and identifying of the life signal under strong clutter. It has a high signal-to-noise ratio and simpler to implement in complex environment detection. We can use the method to search and locate the survivor trapped under the building debris during earthquake, explosion, or fire.

The ultra wideband (UWB) radar has greater advantage in estimate positions and shapes of the target in Through Wall Imaging (TWI). One significant characteristic of human beings is the periodic motion, such as respiration and movement of arms. In this paper, we apply the UWB pulse radar to detect the periodic motion in through-wall detection. We present methods based on the FFT and time-frequency analysis to detect periodic motions characters using the TWI data. In particular, we extract the frequency of the periodic motion, characterize time-frequency features, the size and position of the human being from the TWI images. It is straight forward to use the method in other applications such as earthquake and fire rescue.

We perform a finite-difference time-domain numerical simulation for metal ore detection by borehole radar. The ore-body model is a practical Ni-Cu-Pt one which is a magmatic deposit located in Sudbury, Canada. We design three boreholes along a cross section perpendicular to the geological strike of the formation which is composed of overburden, ore zone, iron formation, peridotite, granite gneiss, and sediments. We analyze the simulated borehole radar profiles and find that some interfaces could be detected. The ore zone is very absorptive to electromagnetic wave. By combined interpretation of the data from several boreholes, the azimuth ambiguity of the borehole radar could be overcome with some a priori geological information, and the geological structure could be delineated clearly.

We perform finite difference time domain (FDTD) numerical simulation for metal ore detection by borehole radar. The ore-body model is adopted from a practical Ni-Cu-Pt ore body which is a magmatic deposit located in Sudbury, Canada. We design three boreholes along a cross-section perpendicular to the geological strike of the formation which is composed with overburden, ore zone, iron formation, peridotite, granite-gneiss, and sediments. We analyzed the simulated borehole radar profiles and found that some interfaces could be detected and some could not, also the ore zone is very absorptive to the wave.

Borehole radar is a special mode of ground penetrating radar. It has several distinguished features from surface radar. For examples, by means of borehole access to deep regions below the surface, the radar sonde can be located relatively close to the anomalies or targets to be measured, this results in more precise targets response than surface measurement. The experimental site is located on the top of a granite hill west of Beijing, China. There are a group of boreholes intersected by many fractures. The measured single-hole reflection data are processed and interpreted. The radial detecting range is more than 30 meters at this site. The subsurface fracture distribution can be imaged very clearly. Many fractures can be "seen", and their distance from borehole and their dip angle can be determined. The azimuth determinations for these fractures are possible in some situations. It is concluded that the borehole radar is an effective tool for subsurface imaging.

Most of the landmine detection techniques by GPR use a monostatic type radar system, which is suitable for detecting landmines buried in shallow soil, which is normally less than 10 cm. The shallow target is difficult to detect by conventional GPR due to its low range resolution. We developed a broadband Vivaldi type antenna, which operates at 1GHz-10GHz. Using this antenna, we made a prototype of a GPR system, which operates at 2GHz-5GHz. By scanning the antenna on a 2D plane, we could obtain clear 3-D images of mine-like targets buried in dry sand. We proposed an array signal processing technique using CMP method, and found it is effective for rejection of the ground surface clutter, even if the ground surface has roughness.