Subsurface Objects Research Articles

Multi-frequency GPR data fusion and its application in NDT

Ground-penetrating radar (GPR) is a non-destructive technique that utilizes high-frequency electromagnetic waves to detect and locate subsurface objects and interfaces. Resolution can be improved with an increase of the source frequency; however, this will lead to decreased investigation depth due to the stronger attenuation of the high frequency signal. Thus, there is a trade-off between the resolution and the investigation depth in the single central frequency-based GPR system. To obtain both high resolution and deep penetrating ability simultaneously, we applied multi-frequency GPR data fusion with three algorithms: time-domain fusion with weights/without, frequency-domain fusion with weights. The fusion effect is qualitatively and quantitatively evaluated and compared by the fused radar profile and Laplacian operator, which is a second derivative gradient operator and commonly used in image edge detection by detecting the zero-crossings of image intensity. The results of the numerical simulation and field data showed that the fused profile was able not only to retain the high resolution in the shallow area from the high-frequency antenna but also take advantage of the significant investigation depth of the low-frequency antenna by merging the multi-frequency data into a single profile. Thus, the fused GPR data has the ability to create a single profile with more information and detailed characteristics.

A Geophysical Survey with Magnetic Method for Interpretation of Iron Ore Deposits in the Eastern Nusawungu Coastal, Cilacap Regency, Central Java, Indonesia

Geophysical survey with magnetic method to interpret the iron ore deposits in the Eastern Nusawungu Coastal, Cilacap Regency, Central Java, Indonesia was carried out during six month, i.e. March –August 2017, covering the area in the geographical position of 109.3462° – 109.3718° E and 7.6958° – 7.7098° S. This survey has produced total magnetic field strength data at each measuring point in the research area. The magnetic field strength data which have been obtained, then be processed, corrected, and mapped so that the local magnetic anomaly contour map can be obtained. The local magnetic anomaly contour map shows the distribution of magnetic anomalous sources in the subsurface of research area. The 2D-modeling of magnetic anomalies data has been carried out along the AB trajectory extending on the local magnetic anomaly contour map from the position of A(109.3463°E and 7.7023°S) to B (109.3688°E and 7.7053°S), so that some subsurface anomalous objects is obtained. The modelling results of magnetic anomalies data show that the research area is estimated to have the potential of iron ore deposits. The subsurface rocks deposits containing iron ore are estimated to be located below the AB trajectory with a length about of 164.85 meters, a depth ranging of 1.709 – 31.909 meters, and a magnetic susceptibility value of 0.0122 cgs unit. These rocks are interpreted as sand deposits which coexists with silt and clay containing iron ore grains from the alluvium formation. Further, iron ore is also estimated to be present in the rocks deposits below the AB trajectory which have a depth of 24.405 – 49.809 meters and 3.989 – 11.111 meters, with the magnetic susceptibility values of 0.0093 and 0.0073 cgs units.

A study on ultrasonic echo tomography for non-destructive evaluation of hardened cementitious concrete

Ultrasonic Echo Tomography (UET) technology has been used as a tool to assess the condition of hardened cementitious concrete by detecting possible subsurface anomalies such as honeycombing, delamination, and air-pocket. In this study, the performance of UET was evaluated in a laboratory study and in three field case studies. In the laboratory, a concrete slab specimen was prepared with manmade honeycombing, delamination, and voids. Steel reinforcing bars with different diameters and a nonmetallic pipe were pre-embedded in the concrete slab. The three field case studies involved evaluating structural beams, a reinforced concrete floor slab, and some reinforced concrete walls constructed with normal-weight concrete. Based on the limited information obtained in this study, it was determined that significant honeycombing, cracks, delamination, and backside of the concrete member formed interfaces with the concrete matrix that could significantly reflect ultrasonic waves. These subsurface objects were clearly imaged by UET at a greater depth from the concrete surface of scanning. Steel reinforcing bars and cluster of small voids formed interfaces with the concrete matrix that could reflect a small amount of ultrasonic wave. These subsurface objects could not be imaged due to attenuation if they were located too deep from the concrete surface. For subsurface objects located close to the concrete surface in a porous concrete matrix, they might not be imaged due to the increased attenuation of ultrasonic waves. A gap in the backside reflection could indicate the presence of subsurface objects that could not be observed in a UET image. Needs for future research were proposed in this study.

Automatic Detection of Near-Surface Targets for Unmanned Aerial Vehicle (UAV) Magnetic Survey

Great progress has been made in the integration of Unmanned Aerial Vehicle (UAV) magnetic measurement systems, but the interpretation of UAV magnetic data is facing serious challenges. This paper presents a complete workflow for the detection of the subsurface objects, like Unexploded Ordnance (UXO), by the UAV-borne magnetic survey. The elimination of interference field generated by the drone and an improved Euler deconvolution are emphasized. The quality of UAV magnetic data is limited by the UAV interference field. A compensation method based on the signal correlation is proposed to remove the UAV interference field, which lays the foundation for the subsequent interpretation of UAV magnetic data. An improved Euler deconvolution is developed to estimate the location of underground targets automatically, which is the combination of YOLOv3 (You Only Look Once version 3) and Euler deconvolution. YOLOv3 is a deep convolutional neural network (DCNN)-based image and video detector and it is applied in the context of magnetic survey for the first time, replacing the traditional sliding window. The improved algorithm is more satisfactory for the large-scale UAV-borne magnetic survey because of the simpler and faster workflow, compared with the traditional sliding window (SW)-based Euler method. The field test is conducted and the experimental results show that all procedures in the designed routine is reasonable and effective. The UAV interference field is suppressed significantly with root mean square error 0.5391 nT and the improved Euler deconvolution outperforms the SW Euler deconvolution in terms of positioning accuracy and reducing false targets.

Excasafezone synthesizing expert based ‘on-the-fly’ safety risk heat maps

Excavation work takes place almost continually in most cities around the Western hemisphere. Many cities are already full of infrastructures, buried networks, and street furniture, so excavation work is not without any thread to the operator and surrounding environment. Small construction sites, for example, are often constrained by operating infrastructure on surface level and underground. Although different agencies and network owners have information about the location of the objects that put excavation work at risk, this information is not centralized. Different organizations manage location information of buried cables, unexploded ordnance, and pollution, for example. This significantly complicates the early-stage planning and last minute risk assessment processes because professionals need to manually collect, assess, and integrate data about subsurface objects into a comprehensive risk assessment. To smoothen this process, ExcaSafeZone project, therefore, develops a system that collects location data, defines expert-based rules for safety risk assessment, and that synthesizes this into an open source prototype that visualized safety risks on a heat map.

Comparison of subsurface object recognition by artificial neural networks and correlation method

Background: The problem of searching for subsurface objects has a particular interest for construction, archeology and humanitarian demining. Detection of underground mines with the help of remote sensing devices replaces the traditional procedure of finding explosive objects, as it excludes the presence of a human in the area of possible damage during a charge explosion. Objectives: The aim of the work is to improve the recognition of three-dimensional objects and demonstrate the benefits of using a more informative data set obtained by a special antenna system with four receiving antennas. In addition, it is necessary to compare the effectiveness of artificial intelligence and the method of cross-correlation for recognition by subsurface radar, taking into account the additive noise of different levels present in practice. Materials and methods: The electrodynamic problem was solved by the finite difference time domain (FDTD) method. An artificial neural network (ANN) is trained on ideal signals to detect the features of the field that will be found in noisy data to determine to the position of the object. Cross-correlation also involves the use of an array of ideal signals, which will be correlated with noisy real signals. Results: The optimal and effective ANN structure for work with the received signals is created. It was tested for noise immunity. The recognition problem was also solved by the classical method of cross-correlation, and the influence of noise of different levels on its responses was studied. In addition, a comparison of the efficiency of their recognition using 1 and 4 sensors was made. Conclusions: For subsurface survey problems, a deep neural networks with at least three hidden layers of neurons should be used. This is due to the complexity and multidimensionality of the processes taking place in the surveyed space. It has been shown that artificial intelligence and cross-correlation techniques perform the object recognition well, and it is difficult to identify the best among them. Both approaches showed good noise immunity. The use of a larger data set of four receivers has a positive effect on the recognition results.

Detection and classification of landmines using UWB antenna system and ANN analysis

Background: The problem of detecting underground objects is found in many areas of human activity in the modern world, for example, a quick survey of the territory for the presence of underground utilities for earthworks, finding the location of grounding structures, cable breakage or short circuit, remote sensing for detecting and mapping of archaeological objects. The issue of humanitarian demining in Donetsk and Lugansk regions is also important in Ukraine. The latest ground surveying devices, such as ultrawideband subsurface radar, have already come to the aid for military sappers in developed countries to make the demining process safer. Objectives: The goal of this work is to improve the recognition of subsurface objects by using an artificial neural network (ANN) for signal processing, to test the influence of interference in signals coming from ultrawideband antenna system on the reliability of determining the object in the observation area, its type and distance to subsurface radar. Materials and methods: In this work, the ANN method is used to recognize the hidden objects by ultrawideband subsurface radar. The process of electromagnetic field propagation is simulated by finite time difference method (FDTD). Neural network testing is performed by adding Gaussian noise of different levels in the input signal. Simulation of the problem is performed 1000 times to exclude the randomness of recognition for different realizations of a noise. Results: Histograms of objects recognition for two types of mines and six types of cans were obtained. A large set of false objects for neural network training gave good results in the detection of antipersonnel mines, which was reflected in the excellent stability of determining the position and type of object, even in the presence of interference with a high signal-to-noise ratio. Conclusions: The problem of subsurface survey can be solved by using a fully connected neural network with five hidden layers of neurons. It has been determined that the use of artificial intelligence gives good results in the recognition of underground objects, if a high-quality learning data set for ANN will be prepared. Satisfactory stability of noisy signal operation is shown, which gives prospects for further testing of the developed method in application to a subsurface radar in the conditions of a real experiment.

Distributed Sensor Networks Based Shallow Subsurface Imaging and Infrastructure Monitoring

Distributed sensor networks can be used as passive seismic sensors to image and monitor subsurface and underground activities. Passive seismic surface-wave imaging adopts background ambient sounds from a far-field energy source. Because high frequency components decay a lot between the neighboring stations, conventional sparse sensor networks cannot image small-scale and shallow objects. In this article, we propose to use local seismic spatial autocorrelation coefficients, obtained by the combinations of independent dense sensor network measurements and pre-processed readings of its neighbor(s), to perform real-time collaborative imaging of the shallow subsurface objects. First, we derive the high-frequency spectral coefficient based shallow subsurface imaging method. Then, we apply the proposed approach to image a shallowly buried pipeline and obtain promising results. Furthermore, based on a time-lapse manner, the water leakage from the buried pipeline can also be detected using distributed computations between sensors. Comparisons and analysis of field deployments are made to validate the effectiveness and performance of the proposed method.

Separating and imaging diffractions of seismic waves in the full-azimuth dip-angle domain

Abstract Seismic diffractions are ideal carriers of information on small-scale, discontinuous objects and can therefore be used to detect these geologic objects. However, recognizing diffractions is difficult because specular reflection with strong energy masks the weak diffraction. In this study, we propose a diffraction separation and imaging method based on a Mahalanobis-based and phase-based attenuation function used to modify the Kirchhoff migration formula in the full-azimuth dip-angle domain. In this domain, reflections are restricted to within the first Fresnel zone and are distributed in the vicinity of the stationary point, while diffractions are located across a wide range of azimuth and dip angles. Synthetic and field data applications suggest that this new method can effectively separate and image diffractions. The results also demonstrate the efficiency of the new method in clarifying subsurface small-scale objects, which can provide finer information about these structures for seismic interpretation.

Parametric Method for Searching Subsurface Objects Based on the Interaction of Seismic and Electromagnetic Waves

The physical basis of the method for detecting subsurface objects in the earth is described on the basis of excitation of the Rayleigh shock seismic waves along the earth-air interface and registration by the radar method of the Doppler phase shift arising under the action of seismic impacts of vibrations of the search object body. The energy dependencies for the radar channel are estimated, the binding parameters of the seismic emitter and the radio channel are given. The results of experimental work and mathematical processing of the data are presented

Effective combination of microgravimetry and geoelectrical methods in the detection of subsurface cavities in archaeological prospection – selected case-studies from Slovakia

Abstract This contribution is focused on a common utilization of microgravimetry (very precise and detailed gravimetry) and geoeletrical methods (ground penetrating radar and electric resistivity tomography) in the detection of subsurface cavities in non-destructive archaeological prospection. Both methods can separately detect such kind of subsurface objects, but their complementary and at the same time an eliminating aspect can be very helpful in the interpretation of archaeogeophysical datasets. These properties were shown in various published case-studies. Here we present some more typical examples. Beside this, we present here for a first time an application of the electric resistivity tomography in the interior of a building (a church) in Slovakia. We also demonstrate an example with an extremely small acquisition step in microgravity as a trial for the detection of cavities with very small dimensions – in this case small separated spaces for coffins as a part of the detected crypt (so called columbarium). Unfortunately, these cavities were too small to be reliably detected by the microgravity method. We have tried the well-known 3D Euler deconvolution method to obtain usable depth estimates from the acquired anomalous gravity field. Results from this method were in the majority of cases plausible (sometimes little bit too shallow), when compared with the results from the ground penetrating radar. In one selected example, the 3D Euler solutions were too deep and in the present stage of study we cannot well explain this situation. In general, all presented results support an important role of common combination of several geophysical methods, when searching for subsurface cavities in non-destructive archaeological prospection.

Georadiolocation Survey Method For Solving Various Construction Tasks

The article reveals the significance of georadiolocation survey (GPR survey), along with the competent approach to its application, the following issues have been considered: those ones related to processing and interpretation of data, identification of various subsurface objects, option of automated processing when working on large-dimensioned or extensive sites, as well as the outlook of the situation related to the legal basis in terms of application of this method has been presented. It has been specially noted that the detailed degree of processing and interpretation varies depending on the quality of the field materials, tasks to be solved and availability of the actual reference points and information. Considerable experience in the field of processing of the materials obtained from georadiolocation survey proves that the unified approaches to interpretation could be allowed only after studying the characteristics of each specific object of survey and developing the environment model, taking into consideration those features.

Shape classification of ground penetrating radar using discrete wavelet transform and principle component analysis

Ground penetrating radar is one of the promising non-destructive investigation for shallow subsurface exploration in locating buried utilities. However, interpreting hyperbolic signature of buried objects in GPR images remains a challenging task since the GPR signals are easily corrupted by environmental noise and cause misinterpretation of the size and geometry of subsurface object from the GPR raw profile. Therefore, this paper proposes Discrete Wavelet Transform (DWT) and principal component analysis (PCA) to classify geometry of buried object using k-nearest neighbour. (k-NN). The GPR images firstly being pre-processed. Then, the GPR images are decomposed using DWT into four sub-bands which are LL (Low-Low), LH (Low-High), HL (High-Low) and HH (High-High). The sub-bands LL or a coarse approximation coefficients was extracted as DWT features in order to classify the shape of buried objects. Since DWT features do contain high dimensional data, thus PCA is used to reduce the dimensional features from higher to lower space by linear transformation. The new projected features were then classify using k-NN classifier into four shapes which is cubic, cylinder, disc and sphere. A series of experiments have been conducted on extracted DWT and PCA features from hyperbolic signature of buried objects having different shapes. Based on the results, the proposed method had achieved the average recognition rate of 99.41%.

Feasibility of magnetic susceptibility tomography for shallow targets

We present an approach to obtain images of magnetic susceptibility of the subsurface using as data the surface magnetic fields of small dipole sources. The approach relies on measurements taken with the instrument known as EM-38 MK2. This device operates with a pair of coils, one operating as the source and the other as the receiver. Usually four measurements are taken, vertical magnetic dipoles at 0.5 and 1.0 m separations, and horizontal magnetic dipoles at the same two separations. To improve depth discrimination we introduce two new arrays using the same instrument but rotated vertically from its usual horizontal lay out. The different configurations are contrasted by analyzing the subsurface signatures generated by elementary ground contributions, in the way it is currently done for resistivity arrays. We model subsurface objects using continuous variations by assuming that they are a superposition of Hann functions. For the inverse problem the superposition is such that we can model uniform bodies with rounded boundaries. We use quadratic programming for optimizing the fit to the data with only positive magnetic susceptibilities, and standard Tikhonov regularization for stabilizing the models. To deal with the non-uniqueness of the inverse problem we compute the shallowest and deepest models that fit the data. This is accomplished by a simple modification of the standard objective function for regularized optimization. We present simulations with synthetic data to demonstrate the viability of the approach. A case with field data also demonstrates the feasibility of magnetic susceptibility tomography for shallow targets.

INTEGRATION BETWEEN SURFACE AND SUBSURFACE SPATIAL OBJECTS FOR DEVELOPING OMAN 3D SDI BASED ON THE CITYGML STANDARD

Abstract. The paper investigates the capability to integrate the surface and subsurface 3D spatial objects data structure within the 3D spatial data infrastructure (3D SDI) based on the CityGML standards. In fact, a number of countries around the world have started applying the 3D city models for their planning and infrastructure management. While others are still working toward 3D SDI by using CityGML standards. Moreover, most of these initiatives focus on the surface spatial objects with less interest to model subsurface spatial objects. However, dealing with 3D SDI requires both surface and subsurface spatial objects with clear consideration on the issues and challenges in terms of the data structure. On the other hand, the study has used geospatial tools and databases such as FME, PostgreSQL-PostGIS, and 3D City Database to generate the 3D model and to test the capability for integrating the surface and subsurface 3D spatial objects data structure within the 3D SDI. This paper concludes by describing a framework that aims to integrate surface and subsurface 3D geospatial objects data structure in Oman SDI. The authors believe that there are possible solutions based on CityGML standards for surface and subsurface 3D spatial objects. Moreover, solving the issues in data structure can establish a better vision and open new avenues for the 3D SDI.

Improved Clutter Removal in GPR by Robust Nonnegative Matrix Factorization

The clutter encountered in the ground-penetrating radar (GPR) system severely decreases the visibility of subsurface objects, thus highly degrading the performance of the target detection algorithms. This letter presents a new clutter removal method based on nonnegative matrix factorization (NMF). The raw GPR data are represented as the sum of low-rank and sparse matrices, which correspond to the clutter and target components, respectively. The low-rank and sparse decomposition is performed using a robust version of NMF called RNMF. Although similar to the robust principal component analysis (PCA) (RPCA), which is recently widely used in image processing applications as well as in GPR, the proposed method is faster and has enhanced results. The state-of-the-art clutter removal methods, morphological component analysis (MCA), RPCA, besides the conventional PCA, have been included for comparison for both simulated and real data sets. The visual and quantitative results demonstrate that the proposed RNMF method outperforms the others. Moreover, it is 25 times faster than the RPCA for the given regularization parameter values.

Hybrid Polarimetric GPR Calibration and Elongated Object Orientation Estimation

Ground penetrating radar (GPR) has been widely applied to the detection of subsurface elongated targets, such as underground pipes, concrete rebars, and subsurface fractures. The orientation angle of a subsurface elongated target can hardly be delineated by a commercial single-polarization GPR system. In this paper, a hybrid dual-polarimetric GPR system, which consists of a circularly polarized transmitting antenna and two linearly polarized receiving antenna, is employed to detect buried elongated objects. A polarimetric calibration experiment using a gridded trihedral is carried out to correct the imbalances and cross talk between the two receiving channels. A full-polarimetric scattering matrix is extracted from the double-channel GPR signals reflected from a buried elongated object. An improved Alford rotation method is proposed to estimate the orientation angle of the elongated object from the extracted scattering matrix, and its accuracy is validated by a numerical test. A laboratory experiment was further conducted to detect five metal rebar buried in dry sand at different orientation angle relative to the GPR scan direction. The maximum relative error of the estimated angles of the buried rebars in the migrated GPR images is less than 5%. It is concluded that radar polarimetry can provide not only richer information than single-polarization GPR, but also a reliable approach for orientation estimation of a subsurface elongated object.

Application of GPR sounding at the examination of fortification objects on Matua Island, Kuril Islands

Matua Island is of volcanic origin and was formed by Sarychev Peak volcano. The island is a place of a specific anthropogenic landscape. Its structure was substantially changed by fortification constructions and other military objects. Analogues of such a landscape weren't described in scientific literature, thus, perhaps, it may be considered unique for Russia and it merits more detailed and indepth review. Results of ground penetration radar (GPR) survey of soil-pyroclastic cover of the island's southeastern part are presented, which include also an investigation of certain subsurface military objects, the greater part of which is unexplored. It's established that existence of objects, various soil disturbances, downwrappings, anthropogenic or natural faults can be located by some radiophysical indicators — details of the reflected pulse, disturbance of pulse lineups, numerous phase shifts and repeated rereflections. It is shown that elaborated methods and increased power GPR with 50—250 MHz antennas to be applied can effectively solve these tasks on complex multilayer and moist volcanic soils.

Bistatic antenna configurations for air-launched ground penetrating radar

Ground penetrating radar (GPR) is valuable for the detection of subsurface objects with little or no metal content, such as plastics, ceramics, and concrete piping. However, the effects of antenna configuration parameters, such as height and angle, are not well studied for all sensing applications. GPR simulations and laboratory GPR experiments are performed to evaluate the effects of antenna angle and height on the sensitivity of bistatic air-launched GPR, to search for buried nonmetallic objects. The results presented provide guidance for the development of air-launched GPR systems installed on unmanned aerial vehicles for in-flight subsurface scanning of buried targets.

A Machine Learning Based Approach for Automatic Rebar Detection and Quantification of Deterioration in Concrete Bridge Deck Ground Penetrating Radar B-scan Images

Ground penetrating radar (GPR) is a non-destructive method (NDT) for subsurface object identification. Interpretation of GPR data is often done manually by an engineer, which is a time-intensive task and requires moderate to significant level of training. The authors proposed a novel machine learning based processing for automatic interpretation and quantification of concrete bridge deck GPR B-scan images. The proposed method is based on combination of image processing, machine learning (ML) data classification, data filtering, and spatial pattern analysis for quantification of deterioration in concrete bridge decks. For the first time, the authors introduced a dataset of 4,000 B-scan images cropped from real bridge deck GPR field data, named DECKGPRH1.0. The proposed method is tested on bridge deck GPR data collected from three bridges with different NBI (National Bridge Inventory) ratings. The results presented indicate that by implementing a ML based classifier and a fine tuned filter, the proposed approach provides a robust solution for automatic quantification GPR field data.