Subsurface Objects Research Articles

Influence of denoising of input signal on classification of object location by artificial neural network in ultrawideband radiointroscopy

Background: A wide usage of impulse ultrawideband subsurface radars for a number of practical approaches in archeology, construction and humanitarian demining is holding back because of presence of noises and clutters of high level in the reflected field. It often makes the object classification practically unreal for at not big depths and distances from receiving and transmitting antennas. Besides of using special antenna system designs to improve recognition results, it is interesting to apply modern digital signal filtering techniques. Objectives: To investigate the influence of denoising on the quality of artificial neural network recognition of subsurface objects and their coordinates for a model of additive gaussian noise of a different noise level. Materials and methods: In this paper the idea of improving the stability of recognition of hidden objects in the presence of outside noise by previous processing of input signals with the latest popular noise reduction methods, such as the caterpillar method and wavelet transform method is verified. To eliminate the randomness of the result of the neural network response for each realization of the additive noise of a given level, a sufficient number of attempts are calculated for each of the methods, and statistics are provided to illustrate the effectiveness of each of the approaches. To check the hypothesis of the efficiency of input signal denoising the numerical simulation of the model of a real ground surface with subsurface object is carried out by means of Finite Difference Time Domain method (FDTD). The artificial neural network is trained on the obtained ideal time dependences of the amplitudes of the reflected field to correctly recognize the position of the object. The training is subsequently checked on the same input signals with additional noise of a certain level. Recognition errors in the last case are compared with similar errors when popular noise reduction procedures are applied to noisy input signals. Results: It is demonstrated that artificial neural networks have good approximating properties capable to effectively resist the noises in the input signals It is shown that for all noise levels, the caterpillar method statistically degrades the quality of an object recognition. The wavelet-transform method statistically improves slightly the classification of objects than for absence of denoising, but this result is not stable. Conclusion: For effective application of methods of noise filtration in received signals of impulse radar it is nessusary to have previous knowledge about noise character or peculiarities of useful signal. Implementation of denoising techniques without the use of this knowledge cannot improve the recognition quality of surface objects.

Cluster analysis of Russian oil companies based on tax burden parameters

The oil industry occupies an important place in Russian economy and in the global energy supply system. The industry has recently been facing a number of internal and external problems, for example, the deteriorating quality and structure of the product base and an increase in the share of tight oil reserves. Confronting these challenges will inevitably incur costs, which will directly affect oil companies’ financial performance. The Russian government uses taxation to incentivize oil companies to improve their efficiency, which renders the question of tax burden particularly salient. This study aims to analyze the tax burden on the Russian oil industry in the period from 2010 to 2017 and to identify the key factors that shape the structure and dynamics of oil companies’ tax payments. The article provides an overview of Russian and international research literature on the problem of tax burden. The role of oil and gas revenues in the structure of the Russian federal budget is shown. The analysis demonstrates that there has been a steady decline in the tax burden on oil companies in recent years due to the changes in the method of calculating the mineral extraction tax and export duties as well as the expanding range of preferential categories of subsurface use objects. The factor analysis combined with quantitative analysis reveals the factors that determine the dynamics and structure of oil companies’ tax payments. The method of cluster analysis is applied in this study to compare the performance of Russian oil companies according to a set of tax burden parameters. The companies are divided into three clusters and specific recommendations are provided for each cluster. For example, Gazprom Neft and LUKOIL have a low tax burden and can be seen, therefore, as potential donors of tax revenues; Rosneft, Bashneft and Tatneft need to increase their efficiency through non-tax optimization; a suitable strategy for Surgutneftegaz, RussNeft, and Slavneft, in our view, would be to adjust the structure of their production activities to increase the share of the domestic crude oil market. Based on the results of the cluster analysis, the authors propose guidelines for reforming taxation of the oil industry and describe the main stages of this process. For citation Filimonova I. V., Provornaya I. V., Shumilova S. I., Zemnukhova E. A. Cluster analysis of Russian oil companies based on tax burden parameters. Journal of Tax Reform. 2019;5(1):42–56. DOI: 10.15826/jtr.2019.5.1.059 Article info Received September 24, 2018; accepted March 20, 2019

Borehole muography of subsurface reservoirs.

Imaging subsurface rock formations or geological objects like oil and gas reservoirs, mineral deposits, cavities or even magmatic plumbing systems under active volcanoes has been for many years a major quest of geoscientists. Since these subsurface objects cannot be observed directly, different indirect methods have been developed. These methods are all based on variations of certain physical properties of the subsurface materials that can be detected from the ground surface or from boreholes. To determine the density distribution, a new imaging technique using cosmic-ray muon detectors deployed in a borehole has been developed and a first prototype of a borehole muon detector successfully tested. In addition to providing a static image of the subsurface density in three dimensions (or three-dimensional tomography), borehole muography can also inform on the variations of density with time, which recently became of major importance with the injection of large volumes of fluids, mainly water and CO2, in porous subsurface reservoirs (e.g. aquifer storage and recovery, wastewater disposal, enhanced oil recovery and carbon sequestration). This raises several concerns about the risk of leakage and the mechanical integrity of the reservoirs. Determining the field scale induced displacement of fluids by geophysical methods like muography is thus a priority.This article is part of the Theo Murphy meeting issue 'Cosmic-ray muography'.

Radius estimation of subsurface cylindrical objects from ground-penetrating-radar data using full-waveform inversion

Ray-based radius estimations of subsurface cylindrical objects such as rebars and pipes from ground-penetrating-radar (GPR) measurements are not accurate because of their approximations. We have developed a novel full-waveform inversion (FWI) approach that uses a full-waveform 3D finite-difference time-domain (FDTD) forward-modeling program to estimate the radius including other object parameters. By using the full waveform of the common-offset GPR data, the shuffled complex evolution (SCE) approach is able to reliably extract the radius of the subsurface cylindrical objects. A combined optimization of radius, medium properties, and the effective source wavelet is necessary. Synthetic and experimental data inversion returns an accurate reconstruction of the cylinder properties, medium properties, and the effective source wavelet. Combining FWI of GPR data using SCE and a 3D FDTD forward model makes the approach easily adaptable for a wide range of other GPR FWI approaches.

Depth sensitivity of subsurface imaging using atomic force acoustic microscopy: FEA Study

Atomic force acoustic microscopy makes it possible to image objects below the surface of a sample. Such subsurface imaging capabilities are of great interest in electronics, semiconductors, (bio)materials and manufacturing, polymers and microbiology. In this article, we report a numerical method to study the subsurface depth sensitivity. We calculated the depth sensitivity of atomic force microscopy and atomic force acoustic microscopy for subsurface objects of different heights and elasticity, as well as for different probe radii, applied forces and indentation depths. We also conducted experiments to validate the simulations. The results indicate that atomic force acoustic microscopy has higher subsurface depth sensitivity than atomic force microscopy.

Method of estimating linear dimensions of object by subsurface radar sounding

The methods for recognizing subsurface objects by the length of radar range portraits in a georadar with wideband probing signals, optimized for the maximum likelihood criterion for complex hypotheses, for the case of a known internal noise power, have been developed. It is shown that, using the optimal method, an energy gain is achieved when the threshold value of the probability of correct recognition is reached.

Clutter Elimination and Random-Noise Denoising of GPR Signals Using an SVD Method Based on the Hankel Matrix in the Local Frequency Domain.

Ground-penetrating radar (GPR) is a kind of high-frequency electromagnetic detection technology. It is mainly used to locate targets and interfaces in underground structures. In addition to the effective signals reflected from the subsurface objects or interfaces, the GPR signals in field work also include noise and different clutters, such as antenna-coupled waves, ground clutters, and radio-frequency interference, which have similar wavelet spectral characteristics with the target signals. Clutter and noise seriously interfere with the target’s response signal. The singular value decomposition (SVD) filtering method can select appropriate singular values and characteristic components corresponding to the effective signals for signal reconstruction to filter the GPR data. However, the conventional time-domain SVD method introduces fake signals when eliminating direct waves, and does not have good suppression of random noise around non-horizontal phase axes. Here, an SVD method based on the Hankel matrix in the local frequency domain of GPR data is proposed. Different numerical models and real field GPR data were handled using the proposed method. Based on the power of fake signals introduced via different processes, qualitative and quantitative analyses were carried out. The comparison shows that the newly proposed method could improve efforts to suppress random noise around non-horizontal phase reflection events and weaken the horizontal fake signals introduced by eliminating clutter such as ground waves.

Underground Object Classification for Urban Roads Using Instantaneous Phase Analysis of Ground-Penetrating Radar (GPR) Data

Ground-penetrating radar (GPR) has been widely used to detect subsurface objects, such as hidden cavities, buried pipes, and manholes, owing to its noncontact sensing, rapid scanning, and deeply penetrating remote-sensing capabilities. Currently, GPR data interpretation depends heavily on the experience of well-trained experts because different types of underground objects often generate similar GPR reflection features. Moreover, reflection visualizations that were obtained from field GPR data for urban roads are often weak and noisy. This study proposes a novel instantaneous phase analysis technique to address these issues. The proposed technique aims to enhance the visibility of underground objects and provide objective criteria for GPR data interpretation so that the objects can be automatically classified without expert intervention. The feasibility of the proposed technique is validated both numerically and experimentally. The field test utilizes rarely available GPR data for urban roads in Seoul, South Korea and demonstrates that the technique allows for successful visualization and classification of three different types of underground objects.

Optical-digital complex for detection of remote mines and mapping of minefields

The article is dedicated to a new method of detecting masked ground and subsurface objects for mapping of minefields and engineering reconnaissance. We proposed to use stereoscopic hyperspectral IR remote sensing in order to increase the reliability of detecting and reducing the frequency of false alarms. Stereoscopic surveying allows exclude skipping of objects masked in vegetation and laid at an angle, and separate the disturbed surface (friable signs) from ordinary soil and embankments. The results of processing hyperspectral data from two channels are combined into a thematic thermal stereomodel of the terrain, on which the anomalies found by the algorithm are highlighted. The concept of an optical-electronic complex of remote sensing of minefields, structural and optical schemes, applied calculations and design solutions are presented in the first part of article. We described basic algorithms for processing hyperspectral data, the method of combining them into a thermal stereomodel of the terrain with the possibility of classifying objects and cross-linking with a three-dimensional map of the area in the second part of the article.

Vivaldi Antenna for Ground Penetrating Radar Applications

The Ground Penetrating Radar (GPR) is an advanced technique used for identifying and detecting objects beneath the surface. Ultra Wideband (UWB) antennas are utilized as a part of GPR system for transmission and reception of short electromagnetic pulses. The fundamental source of difficulty lies in the performance of GPR for detecting the subsurface objects is the reflection from the antenna itself. These reflections cause late time ringing; which makes problem in the recognition of the underground object. Reflections in the antenna itself can be examined by studying transient behavior of the antenna. This paper focuses on the theoretical investigation of the design of UWB Vivaldi antenna (1GHz - 2.6GHz) for GPR applications. Vivaldi antenna has been designed, optimized and simulated in the CST Microwave Studios. The optimized Vivaldi antenna has a lower return loss and VSWR at a center frequency (1.8 GHz) of -35dB and around 1.0, respectively. Vivaldi antenna is also modeled with the GPR environment (i.e. with earth and scatterer inside the earth). Hence, the optimized Vivaldi antenna is presented to use for GPR applications.

Near-Field Resonance Microwave Tomography and Holography

We develop the methods of electromagnetic computer near-field microwave tomography of distributed subsurface inhomogeneities of complex dielectric permittivity and of holography (shape retrieval) of internally homogeneous subsurface objects. The methods are based on the solution of the near-field inverse scattering problem from measurements of the resonance-parameter variations of microwave probes above the medium surface. The capabilities of the proposed diagnostic technique are demonstrated in the numerical simulation for sensors with a cylindrical capacitor as a probe element, the edge capacitance of which is sensitive to subsurface inhomogeneities.

Subsurface object recognition in a soil using UWB irradiation by Bow-Tie antenna and artificial neural network

Background: Subsurface radiolocation problems have an important place in the modern world, such as in geology, building, and humanitarian demining. A complex problem that impedes the widespread use of subsurface radars is the processing and interpretation of the parameters of the reflected electromagnetic field. Objectives: The main purpose of this work is to solve the problem of recognition of objects buried in a soil by bow-tie antenna and artificial neural network (ANN). Materials and methods: The problem of recognition an ideally conducting cylindrical object that is situated below the earth's surface is solved by an ANN. The air-ground interface is irradiated by a bow-tie antenna, which is excited by means of a nanosecond impulse current. The irradiation by nearly point-like source in contrast to plane transient electromagnetic wave incidence considered in our previous works is characterized by the significant decrease of field energy reached a hidden object, reflected, and received by antenna. Moreover, the descent of the field energy becomes more sensible proportionally to the distance from the object to the radar. The complications can call into question the possibility the application of the approach on the base of ANN. The electromagnetic problem is solved numerically by using the FDTD method. The time dependences of amplitudes of differently polarized electric field components, which were obtained in four points above the earth's surface were used as the initial data. The points form the shape of a square. The ANN was trained by the obtained data to determine the position of the object beneath the ground. Results: ANN recognition quality was tested by test data with the addition of Gaussian noise and data obtained when the receiving system is moved relative to the object by shift of the value that was absent in training set. Conclusion: Such type of antenna system in combination with the ANN shows good results for determining the distance to the object even in the presence of noise.

Application of Geodetic Techniques for Antenna Positioning in a Ground Penetrating Radar Method

The accuracy of determining the location of detectable subsurface objects is related to the accuracy of the position of georadar traces in a given profile, which in turn depends on the precise assessment of the distance covered by an antenna. During georadar measurements the distance covered by an antenna can be determined with a variety of methods. Recording traces at fixed time intervals is the simplest of them. A method which allows for more precise location of georadar traces is recording them at fixed distance intervals, which can be performed with the use of distance triggers (such as a measuring wheel or a hip chain). The search for methods eliminating these discrepancies can be based on the measurement of spatial coordinates of georadar traces conducted with the use of modern geodetic techniques for 3-D location. These techniques include above all a GNSS satellite system and electronic tachymeters. Application of the above mentioned methods increases the accuracy of space location of georadar traces. The article presents the results of georadar measurements performed with the use of geodetic techniques in the test area of Mydlniki in Krakow. A satellite receiver Leica system 1200 and a electronic tachymeter Leica 1102 TCRA were integrated with the georadar equipment. The accuracy of locating chosen subsurface structures was compared.

Discrete tomography method for the processing of ultrawideband subsurface radiolocation by artificial neural network

Background: Recognition of subsurface objects became of a great importance because of the number of practical approaches in construction, archeology and energy branch. A perspective direction for the development of subsurface radiolocation lays in the construction of systems of detection of explosives and objects using ultrashort electromagnetic impulses, since they are the ones that can detect objects without metal components. Objectives: The main purpose of this work is to improve the work of artificial neural network (ANN) for the determination of subsurface object’s depth by preliminary processing of input information on the basis of the discrete tomography method. Materials and methods: The solving of a problem of determination of the location depth of subsurface object by means of irradiation by a plane electromagnetic wave and analysis of time dependences of amplitude of reflected wave at equidistant points above the ground surface is proposed. Analysis of received signals is carried out by artificial neural network of improved structure with the usage of additional data obtained due to knowledge of time dependences of received signals and material parameters of dielectric structure under investigation. The problem of Gaussian pulse propagation in the subsurface medium with objects is solved numerically by Finite Difference Time Domain method. Amplitudes of the electric field strength above the ground in given spatial points and time moments form the first part of set of input data for multilayered artificial neural network. The second part of input data includes a special linear superposition of data from the first part with coefficients received on the basis of the discrete tomography approach and the ray tracing method. Results: The work of the artificial neural network is verified by the problem of impulse electromagnetic wave irradiation of the cylindrical perfectly conducting object located inside the ground at given depth. The precision of the determination of the object depth and the influence of the second part of input data are investigated for test cases. Conclusion: Application of discrete tomography method allows to decrease the volume of input data with saving the good approximative characteristics of ANN.

APPLICATION OF MAGNETIC SURVEY TO EXPLORE THE IRON ORE DEPOSITS IN THE NUSAWUNGU COASTAL REGENCY OF CILACAP CENTRAL JAVA

The research aiming to explore the iron ore deposits in the Nusawungu coastal Regency of Cilacap has been conducted using the magnetic survey. The acquisition of magnetic data was conducted in April – Mei 2017, covering the area in the ranges of 109.314° – 109.345°E and 7.691° – 7.709°S. The obtained magnetic field strength data were corrected, reduced, and mapped to obtain the contour map of local magnetic anomaly. The modeling process was carried out along the path extending over the map from the positions of 109.314°E and 7.695°S to 109.335°E and 7.699°S, so that some subsurface anomalous objects are obtained. The lithological interpretation was performed to identify the types of subsurface rocks and their formations based on the magnetic susceptibility value of each anomalous objects and supported by the geological information of the research area. Based on the interpretation results, three rocks deposits of alluvium formations were obtained, which are estimated to contain iron ore. The first deposit has a length of 164.85 m, a depth of 0.57 – 8.43 m, and a magnetic susceptibility value of 0.0097 cgs. The second deposit has a length of 376.28 m, a depth of 2.56 – 19.66 m, and a magnetic susceptibility value of 0.0108 cgs. The third deposit has a length of 1,306.26 m, a depth of 3.70 – 58.69 m, and a magnetic susceptibility value of 0.0235 cgs. Out of the whole rocks deposits, the third rock deposit is interpreted to have the most prospective iron ore. This interpretation based on its high magnetic susceptibility value, which indicates the presence of many magnetic minerals (i.e. iron ores) in the rock.

A Comparison of Feature Representations for Explosive Threat Detection in Ground Penetrating Radar Data

The automatic detection of buried threats in ground penetrating radar (GPR) data is an active area of research due to GPR’s ability to detect both metal and nonmetal subsurface objects. Recent work on algorithms designed to distinguish between threats and nonthreats in GPR data has utilized computer vision methods to advance the state-of-the-art detection and discrimination performance. Feature extractors, or descriptors, from the computer vision literature have exhibited excellent performance in representing 2-D GPR image patches and allow for robust classification of threats from nonthreats. This paper aims to perform a broad study of feature extraction methods in order to identify characteristics that lead to improved classification performance under controlled conditions. The results presented in this paper show that gradient-based features, such as the edge histogram descriptor and the scale invariant feature transform, provide the most robust performance across a large and varied data set. These results indicate that various techniques from the computer vision literature can be successfully applied to target detection in GPR data and that more advanced techniques from the computer vision literature may provide further performance improvements.

Improvement of Handheld Radar Operators’ Hazard Detection Performance Using 3-D Visualization

Handheld ground-penetrating radar systems are employed in both military and humanitarian demining operations. Radar system operators are given the difficult job of determining the nature of subsurface objects from signal reflections in real time. Current systems require operators to multitask both collection and classification. This letter tested a 3-D data visualization method against a 2-D method. The 3-D method attempts to separate tasks by not forcing operators to classify objects in real time. Data showed that users classifying objects with the 3-D system had better performance and also reported that this system was more user-friendly. In addition, users were able to classify underground objects quicker with the 3-D system. The results of this letter demonstrate the benefit of 3-D visualization in ground scanning systems in increasing performance and decreasing cognitive load.

Exploration of Iron Sand at The Eastern Coast Area of Binangun in Cilacap Regency Using Magnetic Survey

<p class="AbstractText">Exploration of the spread of iron sand on the eastern coastal of Binangun District in Cilacap Regency has been conducted using the magnetic surveys. The magnetic data acquisition was conducted in April 2017. The total magnetic field data obtained is processed, so that can be obtained the local magnetic anomaly data. The modeling of the local magnetic anomaly data is performed on the trajectory of AB that extending from the position point of 109,274698° E and 7.686620° S to 109.2296195° E and 7.689099° S so that obtained various model of the subsurface anomalous objects. Interpretation on the subsurface anomalous objects is done to estimate the types of rocks and their formations based on the magnetic susceptibility value of each object which supported by the geological information of the research area. Based on the interpretation results to be obtained two layers of subsurface rocks that can be estimated as the iron sand that coexists with silt dan clay derived from the alluvium formation. The first rock has a length of 1238.2 meters, a depth of 1.709 – 20.513 meters, and a magnetic susceptibility value of 0.0183 cgs unit. The second rock has a length of 643.055 meters, a depth of 16.524 – 34.188 meters, and a magnetic susceptibility value of 0.0174 cgs unit. The results of this research are also supported by the results of geoelectric data interpretation, where the iron sand that coexists with silt and clay is found at a depth of 9.42 – 19.48 meters with a resistivity value of 52.99 Ωm at Geo-1 point; and a depth of 10.56 – 22.20 meters with resistivity value of 49,03 Ωm at Geo-2 point. Based on the results of of this research, the eastern coastal area of Binangun District is estimated to contain potentially iron ore and economically is a prospect for exploitation.</p>

Non-Destructive Testing of Steel Fibre Reinforced Concrete

For standard reinforced concrete, there are several non-destructive test (NDT) methods available for measuring the concrete cover and for locating subsurface objects and defects. Whether or not these methods may also be applied to fibre reinforced concrete has been the subject of a recent study. The results and a recommendation for the most suitable technology for use with fibre reinforced concrete is the topic of this paper.

Enhancing signals from buried Roman structures in Magnetometer data by combining continuous wavelet transform and tensor voting

AbstractIn this paper, a processing chain is proposed for enhancing larger‐scale building structures in magnetometer data using the continuous wavelet transform for extracting the horizontal location, depth, and homogeneity (or shape) of subsurface objects. Even though the values estimated from our data do not clearly describe the archaeological site, they are used as a filter step for removing objects whose depth and homogeneity fall outside a predefined range. This yields a binary map, where valid points represent the horizontal source positions of magnetic anomalies. In a second step, curvilinear subsets of points are identified on a larger scale by the tensor voting framework indicating archaeological relevant structures, such as arcs and lines. Here, a point casts votes on neighbouring points and their values depend on the distance, and relative position of the points. All votes are summed up and the final value discriminates, whether a certain point (and therefore a magnetic source) is part of a salient curve (e.g. arc, line). Thresholding removes points with lower values while keeping those with larger values. Although the result shows many geometric features, it still needs to be combined with other data, e.g. from aerial photography or excavations. In combination, the extracted features continue those structures in the aerial photograph, thus expanding the knowledge about the archaeological site.