Underground Objects Research Articles

Detection and Imaging of Underground Objects for Distinguishing Explosives by Using a Fluxgate Sensor Array

Active and passive techniques are two different techniques with which to detect buried explosives. In practice, the most preferred active method works by broadcasting a signal underground. This signal may stimulate the buried explosive and cause it to explode. It is important to eliminate or minimize this drawback to ensure the safety of the detector operator. In this respect, it is important to increase the studies on the passive detection technique which is not currently used in practice. The aim of this study was to passively detect improvised explosive devices without stimulating them, and to classify underground objects as explosive or non-explosive. A fluxgate sensor array having 33 components was used for passive magnetic field measurements, and the nearest neighborhood algorithm was preferred for classifying the resulting data. In experimental studies, 33 different samples having different amounts of ferromagnetic properties were used. Successful imaging and classification were achieved for the measurements up to 20 cm below the surface of soil. Data were recorded as 32 × 25 matrices, and then they were reduced to 32 × 2 matrices having the same features. Samples having explosive properties were distinguished from other underground objects with success rates of 86% and 95% for 32 × 25 and 32 × 2 data matrices, respectively. Classification times for 32 × 25 and 32 × 2 data matrices were 42 ms and 3.62 ms, respectively. For data groups where the best results were obtained for the data matrices, frame numbers classified in one second were calculated as 23.80 and 276.2, respectively. False alarm rate achieved was 5.31%. The experimental results proved the successes of the matrices reduction and classification approach. One of the most common problems encountered in passive detecting techniques is that the sensor position affects the measurements negatively. In this paper, a solution has been proposed for this important problem.

The terms of soils removal from the defects of the underground structures’ lining

The lack of filtration reliability of bearing structures of urban underground facilities laid down at the design stage is associated with the unresolved issues of their filtration interaction with the adjacent soil or rock body. Water flows through the lining of the underground structure destroy the load-bearing structures, contribute to the removal of loose material into underground workings and create unacceptable microclimatic conditions for human stay. The question arises not only about the permissible degree of permeability of these structures, about the influence of filtration processes on the properties of the lining materials and their stability, but also about the relationship of water permeability of the bearing elements with the properties of the rock mass. Therefore, ensuring the filtration reliability of urban underground structures should be based on a set of protective measures aimed at involving in the work of the relevant properties of the underground object, and rock mass and technological methods of its construction, maintenance and operation. The problem of sealing the underground structure cannot be solved only by improving the concrete lining and raises the question of the need for measures to manage the characteristics of the state of the soil mass, which is the subject of this study.

Triplanar Imaging of 3-D GPR Data for Deep-Learning-Based Underground Object Detection

This article proposes a deep-learning-based underground object classification technique that uses triplanar ground-penetrating radar images consisting of B-, C-, and D-scan images. Although multichannel ground-penetrating radar (GPR) systems provide three-dimensional (3-D) information about underground objects, there is currently no suitable technique available for processing 3-D data as opposed to 2-D images. In this article, a triplanar deep convolutional neural network technique is proposed for use in processing 3-D GPR data for use in automatized underground object classification. The proposed method was validated experimentally using 3-D GPR road scanning data obtained from urban roads in Seoul, South Korea. In addition, the classification performance of the method was compared to that of a conventional method that uses only B-scan-images. The results of the validation and comparison tests reveal that the classification performance of the proposed technique is notably better than that of the conventional B-scan-image-based method and that its use results in decrease misclassification ratios.

Classification of different materials for underground object using artificial neural network

Ground Penetrating Radar (GPR) is widely used for non-destructive investigation of the shallow subsurface exploration especially in locating the buried infrastructure such as pipes, cables and road inspections. However, the interpreting hyperbolic signature of buried object in GPR images remains a challenging task. Therefore, this paper presented the classification of different materials based on GPR images using artificial neural network (ANN). In this research, GPR images so called the B-scan radargram represented by hyperbolic signature are firstly acquired and pre-processed. Then, the hyperbolic signature features are extracted using statistical techniques. The extracted features is then fed up as input to the multilayer perceptron (MLP) neural network classifier. A series of experiments have been conducted based on extracted hyperbolic features of different materials and shapes. Based on the results, the proposed method in classifying different materials based on GPR images using neural network showed promising results.

Components of a Technology Roadmap for Automated Excavation

In the construction industry, especially in earthmoving equipment, automation has received a great deal of attention. Fully environmentally friendly automatic construction equipment, such as excavators, are perceived as vital for safe, productive, and resilient future construction sites. However, there is much uncertainty about the technologies which need to be adopted and developed in order to effectively achieve this end and provide the industry with strategic direction. This paper explores current academic thinking about technology roadmaps and technological innovations of excavation operations, in order to identify the components for automated excavation technology roadmaps. Literature in well-established databases are identified using robust search strategies. Text mining, a quantitative approach, was used to analyse and organise findings. Three main technology innovation categories were identified: 1) real time positioning systems; 2) technologies to detect over ground objects, 3) technologies to detect below underground objects. In addition, multi-sensory data fusion methods are identified as an essential and vital requirement for construction automation.

Interpretation of Ground Penetrating Radar Dataset using Normalised Cross-Correlation Technique

Ground Penetrating Radar (GPR) is one of the latest non-destructive geophysical technology and most widely used in detecting underground utilities. GPR can detect both metal and non-metal, however, it is unable to identify the type of underground utility object. Many researchers come out with their techniques to interpret the GPR image. The current method requires experience in interpretation. Thus, in this study, a new method to detect underground utility utilizing the Normalised Cross-Correlation (NCC) template matching technique is proposed. This technique will reduce the dependency on experts to interpret the radargram, less time consuming and eventually save cost. Upon detection, the accuracy of the system is assessed. From the accuracy assessment performed, it is shown that the system provides accurate detection results for both, depth and pipe size. The Root Mean Square Error (RMSE) for the buried pipe depth obtained by using the proposed system is 0.110 m, whereas the highest percentage match obtained is 91.34%, the remaining 8.66% mismatched might be due to the soil condition, velocity or processing parameter that affected the radargram. Based on the assessment, the developed system seems capable to detect the subsurface utility if the radar image and template image used is acquired using the same antenna frequency, point interval, and similar GPR instrument.

3D GPR Image-based UcNet for Enhancing Underground Cavity Detectability

This paper proposes a 3D ground penetrating radar (GPR) image-based underground cavity detection network (UcNet) for preventing sinkholes in complex urban roads. UcNet is developed based on convolutional neural network (CNN) incorporated with phase analysis of super-resolution (SR) GPR images. CNNs have been popularly used for automated GPR data classification, because expert-dependent data interpretation of massive GPR data obtained from urban roads is typically cumbersome and time consuming. However, the conventional CNNs often provide misclassification results due to similar GPR features automatically extracted from arbitrary underground objects such as cavities, manholes, gravels, subsoil backgrounds and so on. In particular, non-cavity features are often misclassified as real cavities, which degrades the CNNs’ performance and reliability. UcNet improves underground cavity detectability by generating SR GPR images of the cavities extracted from CNN and analyzing their phase information. The proposed UcNet is experimentally validated using in-situ GPR data collected from complex urban roads in Seoul, South Korea. The validation test results reveal that the underground cavity misclassification is remarkably decreased compared to the conventional CNN ones.

A Miniaturized Dual Band Antenna for Harmonic RFID Tag

Abstract A miniaturized antenna is required for a small form factor RFID tag. For harmonic RFID tag, the tag should be capable of receiving and transmitting at two different frequencies (fundamental and harmonic). Implementation of two different antennas for the operation would increase the footprint of the antenna. Hence, an optimized antenna structure is proposed, which will have a small form factor while maintaining a considerable gain. The dual band antenna would be capable of receiving at fundamental frequency and transmit information at harmonic frequency while maintaining small tag size. The dual band antenna has a miniaturized rectangular board dimension of 96.5 mm and 81 mm with resonance at 434 MHz at low frequency and 860 MHz to 1000 MHz at high frequency. The harmonic tag was designed with nonlinear transmission line and the dual band antenna. The harmonic RF tag would be useful for numerous RF applications where the single frequency tags will not be a good option such as underground object tagging, tag detection in an industrial set up with strong reflectors such a metal in the vicinity. In this paper, the design, fabrication and characterization of dual band harmonic RFID tag antenna is presented.

Reflectivity and Resolution Improvement of Underground Rectilinear Objects Detection Using GPR

In our previous works, we presented a fundamentally new method for detecting underground rectilinear objects by means of georadars. To improve the lateral resolution of the proposed method, a variant of this method using two transmitting antennas fed in antiphase was proposed. In this letter, we extend the previous work and present a deeper analysis of this method. It is shown that using the proposed method, a significant increase in both the relative area reflectivity (RAR) of rectilinear objects and the lateral resolution in the detection of underground narrow objects can be achieved. Our calculations show that using this method, it is possible to achieve a high horizontal resolution of the order of 0.2 m at depths of 20–30 m. The dependencies of RAR on the object’s depth and width are calculated and presented. This method is applicable for both orthogonal and any other scanning scheme.

A DEEP LEARNING APPROACH FOR URBAN UNDERGROUND OBJECTS DETECTION FROM VEHICLE-BORNE GROUND PENETRATING RADAR DATA IN REAL-TIME

Abstract. GPRs (Ground Penetrating Radar) are widely adopted in underground space survey and mapping, because of their advantages of fast data acquisition, convenience, high imaging resolution and NDT (Non Destructive Testing) inspection. However, at present, the automation of the GPR data post-processing is low and the identification of underground objects needs expert interpretation. The heavy manual interpretation labor limits the GPR applications in large-scale urban scenarios. According to the latest research, it is still an unsolved problem to detect targets or defects in GPR data automatically and needs further exploration. In this paper, we propose a deep learning method for real-time detection of underground targets from GPR data. Seven typical targets in urban underground space are identified and labelled to construct the training dataset. The constructed dataset is consist of 489 labelled samples including rainwater wells, cables, metal/nonmetal pipes, sparse/dense steel reinforcement, voids. The training dataset is further augmented to produce more samples. DarkNet53 convolutional neural network (CNN) is trained using the constructed training dataset including realistic data and augmented data to extract features of the buried objects. And then the end-to-end YOLO detection framework is used to classify and locate the seven specific categories buried targets in the GPR data in real time. Experiments show that the automatic real-time detection method proposed in this paper can effectively detect the buried objects in the ground penetrating radar image in real time at Shenzhen test site (typical urban road scene).

Towards an Underground Utilities 3D Data Model for Land Administration

With the pressure of the increasing density of urban areas, some public infrastructures are moving to the underground to free up space above, such as utility lines, rail lines and roads. In the big data era, the three-dimensional (3D) data can be beneficial to understand the complex urban area. Comparing to spatial data and information of the above ground, we lack the precise and detailed information about underground infrastructures, such as the spatial information of underground infrastructure, the ownership of underground objects and the interdependence of infrastructures in the above and below ground. How can we map reliable 3D underground utility networks and use them in the land administration? First, to explain the importance of this work and find a possible solution, this paper observes the current issues of the existing underground utility database in Singapore. A framework for utility data governance is proposed to manage the work process from the underground utility data capture to data usage. This is the backbone to support the coordination of different roles in the utility data governance and usage. Then, an initial design of the 3D underground utility data model is introduced to describe the 3D geometric and spatial information about underground utility data and connect it to the cadastral parcel for land administration. In the case study, the newly collected data from mobile Ground Penetrating Radar is integrated with the existing utility data for 3D modelling. It is expected to explore the integration of new collected 3D data, the existing 2D data and cadastral information for land administration of underground utilities.

On the use of the profiled singular-function expansion in gravity gradiometry

We demonstrate a method of inverting gravity data, based on profiled singular-function expansions. It is well known that the inverse problem of determining underground density variations from gravity data is severely ill-posed and prior knowledge is needed to restrict the range of possible solutions. Viewed as a linear inverse problem, various standard methods, all of which produce solutions approximating the generalised solution, tend to give density variations concentrated near the surface. To overcome this potentially undesirable trait, various authors have introduced depth weighting. In this paper we carry this idea a step further and introduce a method based on a profiled singular-function expansion which uses the prior knowledge that the underground object is centred at a particular depth. The use of an appropriate depth-weighting profile leads to a solution at the correct depth. Furthermore, we show that if the depth of the object is unknown, a range of solutions at different depths can be produced, allowing other prior knowledge, such as object size or density, to be introduced to determine which solution is the most plausible. The truncation point of the profiled singular-function expansion is determined by the level of noise on the data. We examine how the achievable horizontal resolution varies with this truncation point. A notable property of our approach is that when the centre of the profile corresponds to the true object depth, the solution appears, in a certain sense, to be the most focussed one. Finally we consider a gravimetry example using real data.

A novel 3D GPR image arrangement for deep learning-based underground object classification

ABSTRACT Ground-penetrating radar (GPR) is widely used for detecting buried underground object. Deep learning technique is recently being adopted into this field thanks to its powerful image classification capacity. However, it uses only GPR B-scan images, although multichannel GPR device can provide more informative three-dimensional (3D) data for underground object. In this study, a novel deep learning-based underground object classification method is proposed by using two-dimensional (2D) grid image which consists of several B-scan and C-scan images. Spatial information of an underground object can be well represented in the 2D grid image. The 2D grid images are then used to train deep convolutional neural networks. The proposed method is experimentally validated by field data collected from urban roads in Seoul, South Korea. The performance is also compared to a conventional method which uses only B-scan images. The proposed method successfully classifies cavity, pipe, manhole and subsoils background having very small false-positive errors.

Cartographic support of design, construction and operation of underground gas storage in rock salt

Abstract. Investigated the directions of use of topographic maps and requirements for them at different stages of design, construction and operation of underground gas storages in rock salt – one of the most important parts of Russia's Unified gas supply system. The specificity, complexity and diversity of cartographic support of this industry emphasized. A new type of topographic map is proposed, the content of which is subjected specially to the purpose of creating underground gas storage with the display of information about the objects of the entire complex of storage, their parameters, and data about development of dangerous natural and anthropogenic processes.The system of notation conventions is developed: a) wells of various type and purpose, their characteristics; b) underground and surface objects of storage in the form of water-brine and technological complexes, pipelines for various purposes; c) environmental monitoring facilities, specially protected areas. The necessity substantiated to supplement the main original of the map with text characteristics with thematic maps-insert and 3-D models (figure 1).As the most current thematic maps are considered: the depth and power of the salt rocks (figure 2); the calculated subsidence trough of the earth's surface (figure 3); the zone of maximum spread of the construction of the brine and mineralization of groundwater (figure 4).In general, all the presented developments characterize a new type of specialized topographic map for the design, construction and operation of underground gas storage.

A method of using geomagnetic anomaly to recognize objects based on HOG and 2D-AVMD

In order to identify the shape of underground small magnetic anomaly objects, we use Support Vector Machines (SVM) to identify the underground magnetic anomaly targets. Firstly, as the SVM needs a lot of training data, and we also need to make full use of the magnetic field signal, nine component signals including total magnetic intensity (TMI) and five independent components of tensor are calculated from the original detected magnetic signal. Secondly, the nine component signals are subjected respectively to two-dimensional adaptively variational mode decomposition (2D-AVMD), which is advanced based on the two indicators, namely Mutual information (MI) and empirical entropy (EE), and we can get the nine primary signals from the decomposition results of nine component signals called the Intrinsic Mode Function (IMF). Then, the Histogram of Oriented Gradients (HOG) of the nine primary signals is extracted, and the feature data would be constructed into feature vectors. In the end, Support Vector Machines (SVM) are adopted to process these feature vectors. The output of the SVM can indicate the result of small objects’ shape recognition under the ground. Experiments prove that the shape recognition accuracy of underground small magnetic anomaly object recognition reaches 90%.

Risk of Emergency Situations of Underground Objects in the Perspective of Multi-Criteria Decision Making

Abstract The diaphragm wall and the open caisson represent two main competitive technologies used in the construction of underground objects. In modern times, diaphragm walls are primarily applied for large-size objects, with open caissons being preferred in the case of small-sized ones. Currently, objects of this type are designed mainly for sewage treatment plants and detention reservoirs. Their construction involves highly labour-intensive processes. During the execution of works unforeseen negative effects are observed to occur. During the underground objects construction the most common phenomena are: deviations from the vertical (tilt), sagging, sinking below the designed level, cracking, scratches or leakage through the wall. The purpose of the paper is to classify undesired risk factors emerging in the process of underground objects construction and selection of the optimal technological and material solution for municipal facilities. The implementation of this task involved the selection of Multi-Criteria Decision Making methods, taking into account the cause-effect rating, as the mathematical apparatus. The Ratio Estimation in Magnitudes or deciBells to Rate Alternatives which are Non-DominaTed (REMBRANDT) method was applied. The research proved that it is possible to analytically assess unforeseen risk factors conducive to emergency situations during the implementation of underground objects, using the REMBRANDT method.

Direct measurements of tree root relative permittivity for the aid of GPR forward models and site surveys

ABSTRACTGround penetrating radar has been used extensively in near‐surface studies to detect underground objects and features typically located within a few metres beneath the surface. In urban areas, ground penetrating radar is widely used to study buried utilities such as pipes and cables. A more recent and unconventional application of ground penetrating radar is the detection of tree roots, which can interact negatively with the human infrastructure in a number of ways. However, the geophysical study of tree roots has proven quite challenging and site‐specific. Most tree roots (even coarse roots) have a small diameter and are hard to resolve through geophysical methods. In addition, the sheer amount of potential variability regarding the tree species, age, size, health and the subsurface environment (e.g., soil or a man‐made material such as concrete or asphalt) makes it very hard to implement a one‐size‐fits‐all approach. This is where robust, easily customizable forward models can be of assistance, indicating the range of detectable geophysical contrast and the limitations of the method, as well as the suitable antenna frequencies. Here, a vector network analyser with a commercial open‐ended coaxial probe was used to take direct measurements of the relative permittivity of freshly cut tree root segments at frequencies from 50 MHz to 3 GHz. The results were used as inputs to ground penetrating radar forward modelling using gprMax open source software, depicting various realistic scenarios which could be encountered in actual field surveys. The developed models help better understand the applicability, potential and limitations of ground penetrating radar surveys for detecting tree roots in different environments, aiding the development of future surveys. The notable variability in the tree roots is a significant consideration for surveys and forward models.

Prototype of the 3D Cadastral System Based on a NoSQL Database and a JavaScript Visualization Application

3D cadastral systems are more complex than traditional cadastral systems and they require more complex technical solutions and innovative use of developing technologies. Regarding data integrity and data consistency, 3D cadastral data should be maintained by a Database Management System (DBMS). Furthermore, there are still challenges regarding visualization of 3D cadastral data. A prototype of the 3D cadastral system based on a NoSQL database and a JavaScript application for 3D visualization is designed and tested in order to investigate the possibilities of using new technical solutions. It is assumed that this approach, with further development, could be a good basis for the development of a modern 3D cadastral system. MongoDB database is used for storing data and Cesium JavaScript library is used for 3D visualization. The system uses an LADM (Land Administration Domain Model) based data model. Additionally, script languages, libraries, application programming interfaces (APIs), software and data formats are used for the system development. The case study is based on the real cadastral data. The underground object and building units located below and above the ground level are used to test the proposed data model and the system’s functionality. The proposed system needs further development in order to provide full support to a modern 3D cadastral system. However, it allows maintenance of 3D cadastral data and basic 3D visualization with the interactive approach.

Principles of sustainable land use in the territory of Almaty agglomeration

The land resources of cities are not only the basis for placing a significant part of the productiveforces, but also serve as a space for the vital activity of the urban population as a whole. The study ofthe dynamics of urban development at the present time indicates the need to revise the principles ofland use and the model of urban land management. Optimization of urban land use is an extremelydifficult task, since urban land is represented by all types of land resources: residential, industrial,road, forest, water, agricultural. In contrast to agricultural land, urban land includes located abovegroundand underground construction and engineering objects that create a complex polymorphicstructure.The specificity of land use in any territory depends on the natural landscape features. An importantaspect of optimizing a land use area is the improvement of its territorial structure, the creation of an optimal ratio of landfor various functional purposes. Land use optimization should be promoted by anintegrated, systemic, regional approach to the study of land resources. Landscape-ecological features ofthe territory determine the degree of environmental resistance to anthropogenic influences. The articledefines the basic principles of sustainable land use in the territory of the agglomeration, which is causedby the need to take a set of specific measures to ensure the preservation of existing land resources andenvironmental and economic security.

Natural stress estimation by solving inverse problem using acoustic sounding data

Under consideration is the approach allowing quantitative estimation of natural stress field components in the vicinity of underground objects based on solution of an inverse boundary-value problem using the data on acoustic sounding in an extended mine working. To ensure solvability of an inverse problem, the optimal sonic transmitter–receivers positioning and the limiting nominal error of measurements are determined. The objective function is set as a root-mean-square difference between the theoretical and actual head P-wave arrival times at the receivers; unimodelity of the objective function is illustrated using synthetic input data.