Subsurface Objects Research Articles

Polarisation Synthesis Applied to 3D Polarimetric Imaging for Enhanced Buried Object Detection and Identification

Detecting sub-surface objects poses significant challenges, partly due to attenuation of the ground medium and cluttered environments. The acquisition polarisation and antenna orientation can also yield significant variation of detection performance. These challenges can be mitigated by developing more versatile systems and algorithms to enhance detection and identification. In this study, a novel application of a 3D SAR inverse algorithm and polarisation synthesis was applied to ultra-wideband polarimetric data of buried objects. The principle of polarisation synthesis facilitates an adaptable technique which can be used to match the target’s polarisation characteristics, and the application of this revealed hidden structures, enhanced detection, and increased received power when compared to single polarisation results. This study emphasises the significance of polarimetry in ground-penetrating radar (GPR), particularly for target discrimination in high-lift-off applications. The findings offer valuable insights that could drive future research and enhance the performance of these sensing systems.

Comparative Study of GPR Acquisition Methods for Shallow Buried Object Detection

This paper investigates the use of ground-penetrating radar (GPR) technology for detecting shallow buried objects, utilizing an air-coupled stepped frequency continuous wave (SFCW) radar system that operates within a 2 GHz bandwidth starting at 500 MHz. Different GPR data acquisition methods for air-coupled systems are compared, specifically down-looking, side-looking, and circular acquisition strategies, employing the back projection algorithm to provide focusing of the acquired GPR data. Experimental results showed that the GPR can penetrate up to 0.6 m below the surface in a down-looking mode. The developed radar and the back projection focusing algorithm were used to acquire data in the side-looking and circular mode, providing focused images with a resolution of 0.1 m and detecting subsurface objects up to 0.3 m below the surface. The proposed approach transforms B-scans of the GPR-based data into 2D images. The provided approach has significant potential for advancing shallow object detection capabilities by transforming hyperbola-based features into point-like features.

The use of groups of drones for diagnostics of subsurface objects in the radio range

Diagnostics of objects that are bodies of complex shape can be based on representing their boundaries of such objects (including subsurface ones) in the form of a set of paraboloids. It is shown that the determination of specific geometric characteristics of such paraboloids is ensured through the artificial synthesis of a distributed lens, the individual elements of which are placed on individual unmanned aerial vehicles. Technically, lens synthesis is achieved by changing the phase of radio frequency radiation, carried out using phase shifters. The method is implemented through scanning the object under study by adjusting the effective optical power and its orientation in space. Model calculations are presented that prove the prospects of using the proposed method. Specific examples of radio-electronic circuits that ensure the implementation of the proposed approach are presented. A feature of the phase shifters used is the preliminary detection of the phase of the signal arriving at each of the elements of the distributed lens, and the subsequent introduction of an additional phase difference due to the weighted summation of harmonic signals that are in antiphase.

Evaluating efficiency of foreign object detection technology based on the use of passive infrared thermography

A new method for detecting subsurface solid objects buried in farmlands, such as plastic bottles, wasted cans, etc., has been proposed by applying the technique of infrared (IR) thermography to monitor the temperature of soil surface subjected to solar irradiation. Through both experimentation and simulation, this study parameterizes the influence of environmental factors on IR images and validates the detection capabilities of the method. To verify the feasibility of IR thermography testing, the experimental section of the work is devoted to monitoring aluminum and polyethylene terephthalate cans buried in sand with varying grades of moisture. The dependencies between the efficiency of foreign object detection and their depth are derived. A restoring pseudothermal flux algorithm was used to reduce the impact of lateral diffusion on IR thermographic detection of foreign objects buried in soil. Variations of soil temperature caused by varying solar radiation during multiple day-night cycles are used to improve the detectable diameter-depth ratio. The described technique is efficient and provides no harm to human beings.

A Convolutional Neural Network with Multifrequency and Structural Similarity Loss Functions for Electromagnetic Imaging.

In this paper, artificial intelligence (AI) technology is applied to the electromagnetic imaging of anisotropic objects. Advances in magnetic anomaly sensing systems and electromagnetic imaging use electromagnetic principles to detect and characterize subsurface or hidden objects. We use measured multifrequency scattered fields to calculate the initial dielectric constant distribution of anisotropic objects through the backpropagation scheme (BPS). Later, the estimated multifrequency permittivity distribution is input to a convolutional neural network (CNN) for the adaptive moment estimation (ADAM) method to reconstruct a more accurate image. In the meantime, we also improve the definition of loss function in the CNN. Numerical results show that the improved loss function unifying the structural similarity index measure (SSIM) and root mean square error (RMSE) can effectively enhance image quality. In our simulation environment, noise interference is considered for both TE (transverse electric) and TM (transverse magnetic) waves to reconstruct anisotropic scatterers. Lastly, we conclude that multifrequency reconstructions are more stable and precise than single-frequency reconstructions.

Estimation of the spatial resolution of a synthetic aperture radar antenna of the VHF frequency range placed on board an unmanned aerial vehicle along a real flight path

Background. Currently, there is an intensive development of radar systems for remote sensing of the Earth, including those placed on unmanned aerial vehicles. Synthetic aperture radars are an effective means of round-the-clock and all-weather observation of objects on the Earth’s surface. Radars operating in the VHF range allow you to observe hidden and subsurface objects. The need for practical implementation of VHF band radar with synthesizing the aperture on board unmanned aerial vehicles requires additional research on the spatial resolution of radars, taking into account the unstable trajectory of the carrier. Aim. The article considers the analysis of the influence of the flight path and its measurement errors on the quality of the radar image. Methods. The research method provides an analysis of the shape of the radar uncertainty function with the synthesis of an aperture, which was built along a real trajectory using semi-natural mathematical modeling. Results. As a result of the conducted research, it is shown that the quality of radar images is influenced not only by errors in the knowledge of motion parameters, but also by the very nature of the carrier’s movement and the duration of the flight. Conclusion. Based on the results of field tests, it turned out to be possible to analyze these factors and determine the requirements for the UAV navigation system. Thus, the article shows the possibility of quality control of VHF band radar with synthesized aperture placed on board a UAV.

New Design of an Electrical Excavator and Its Path Generation for Energy Saving and Obstacle Avoidance

This study’s goals are divided into two categories. The first is to design and build an excavator equipped with parallel electrical linear actuators. The second is to generate and test a PSO-based and a PFM-based path for this excavator in order to save energy by reducing energy consumption, improve the digging accuracy by minimizing the deviation between the desired and dug surfaces of the ground, and prevent colliding with subsurface objects. For this purpose, computer vision was employed to improve monitoring and verification. Five types of experiments were carried out in this investigation. The first two and the other three examined the impact of energy conservation in PSO- and PFM-based path generation, respectively. Finally, the results from these experiments were compared to identify and show the effect of optimal path generation.

Angle-corrected GPR hyperbolic fitting models for improved parameter estimation

Ground-penetrating radar (GPR) is commonly employed as a non-destructive technique for detecting subsurface cylindrical objects such as pipes, cables, rebars, and tree roots. Hyperbolic features generated in GPR radargrams are often used to estimate key parameters like burial depth, object radius, and electromagnetic wave velocity. However, traditional approaches frequently rely on the assumption that GPR traverses are perpendicular to the alignment of target—an assumption or a negligence that is not always valid in real-world scenarios. To address this limitation, a novel method is introduced for simultaneously estimating the orientation and burial depth of pipes, as well as wave velocity, from the hyperbolic patterns observed in GPR data. The method innovates by incorporating an angle correction index into the classical hyperbolic fitting model. This modified model is then formulated as an optimization problem, which is solved using a hybrid approach combining the Multi-Verse Optimizer (MVO) and Gradient Descent (GD) algorithms. A unique index, termed the “C-value,” is introduced to quantitatively analyse the influence of oblique angles on the hyperbolic fitting models. Two distinct fitting models are validated through both simulation and field experiments. The study also scrutinizes the impact of varying pipe radius and burial depth on the accuracy of parameter estimation at different pipe orientation. The methodology presented herein enables the simultaneous estimation of burial depth, wave velocity, and pipe orientation directly from hyperbolic fitting—a significant advancement, as orientation is typically assumed as a known input yet is often challenging to ascertain obtain in practical field situations.

Rebar detection: Comparison of stepped frequency continuous wave and pulsed GPR

Reinforced concrete structures are widely used in civil engineering owing to their versatility, strength, and durability. One of the most recommended methods for rebar mapping is Ground-Penetrating Radar (GPR) because of its non-destructive and non-invasive character. In concrete mapping, GPR is mainly used for locating subsurface objects (rebars, tendons, ducts), measuring concrete cover, mapping mesh configuration (spacing), and detecting bottom of slab. To achieve these, concrete scanning GPR practitioners often need very high-resolution images. This work presents a comparison in rebar mapping between two GPR systems with different modulation techniques: Stepped frequency continuous wave (SFCW) and pulsed radar. The SFCW system used has a frequency range of 400-6000 MHz, while the pulsed system used a ground-coupled central frequency antenna of 2.3 GHz. Measurements were conducted on laboratory specimens, with rebar diameters ranging from 8 to 32 mm. Three different specimens were used, with one for calibration and other two to analyze both the horizontal and vertical resolutions of the frequency antennas.

Influence of 2-D Transceiver Array Aperture Size and Polarization on 3-D Microwave Imaging of Subsurface Objects Under Born Approximation

Influence of 2-D Transceiver Array Aperture Size and Polarization on 3-D Microwave Imaging of Subsurface Objects Under Born Approximation

A Simple Model Generator for Radar Sounding Applications Based on Stochastic Media Model and gprMax

Ground penetrating radar (GPR), or more generically called radar sounding, is an established technique for probing subsurface objects and sublayer structures. Simulating pre-constructed models and comparing with the real-world observations can contribute to retrieve the geometrical and physical properties of subsurface objects. However, most GPR simulations are limited to geometrically simple models with homogeneous physical properties of subsurface media which are generally unable to represent the real-world situations. In this paper, we develop a simple model generator by combining the stochastic media model and gprMax, an open-source software for electromagnetic simulations, to jointly generate models with geometrically random fragments, which are forward models with minimum human intervention. These easily prepared and simulated models can enrich the model training datasets which can be further utilized in machine learning for GPR retrieval algorithms.

Detection of subsurface water pipeline under SZJT Track Using Ground-Penetrating Radar Method

Surabaya’s public facilities and rapid infrastructure development will change the physical environment and require careful attention in all aspects of development. One aspect is the location determination of subsurface objects such as gas pipes, electrical cable lines, and water pipes. Lack of pipeline management and mapping can fail underground pipeline identification during excavation. The subsurface water pipe is one of the most important things to support people’s needs. Knowing the location of these pipes is essential for government agencies in carrying out maintenance, pipeline development, and repair activities. We identified the subsurface water pipeline using the Ground-Penetrating Radar (GPR) method. It is because of a non-destructive working type and very well applied in investigating underground infrastructure with significant electromagnetic contrast with the surrounding soil. We conducted this research around Surabaya Zoo and Joyoboyo Terminal (SZJT) track. We conducted data acquisition with the GPR GSSI SIR-3000 system with a shielded antenna frequency of 270 MHz on 21 measurement lines (JB07 to JB27). The aim is to determine the location, depth, and structure of the subsurface water pipeline at the research location. We processed the measurement data using MatGPR R-3.1 software by adjusting signal position, dc removal, dewow, median filter, inverse amplitude decay, background removal, K-L filter, bandpass filter, and time-to-depth conversion. We conducted 2-D and 2.5-D modeling to visualize the water pipeline. A hyperbolic anomaly, suspected to be a water pipe, is detected from the presence of high amplitude at a depth of 1 – 2 meters, which we saw in almost every line with velocity values from 0.0609 - 0.113 m/ns and dielectric constant value of 7.05 – 24.27. The 2.5-D modeling shows that the water pipeline continues from the research location’s south (Surabaya River) to the north.

Object Detection and Localization in GPR B- Scan images using Hyperbola Fitting Algorithm

Ground-penetrating radar [GPR] is a non-invasive method, that generates an electromagnetic wave to map a subsurface object. It finds applications in various fields such as civil / structural engineering, environmental, Geotechnical and Military applications. This research is conducted at the Civil Engineering laboratory, IISc, Bangalore to collect the experimental data, using the Mala Pro-ex ground-coupled antenna GPR system from Mala Geoscience. GPR technology emits electromagnetic pulses into the ground and captures the reflected echoes from the target,enabling a detailed visualization of the subsurface targets.This paper presents the Hyperbola fitting algorithm for accurate detection and localization of the underground objects such as metallic and nonmetallic. It involves several steps, including preprocessing, identifying hyperbolas, fitting hyperbolas, determining target locations and depths, and validating the results. Following the fitting of the hyperbola, the algorithm proceeds to estimate the target's position and depth using the hyperbola's parameters. These estimated values for the target location and depth are subsequently validated through a model generated using the gprMax numerical modeling tool and is also tested on experimental data. There are numerous iterations of hyperbola fitting algorithms, and their implementation varies according to the attributes of the GPR data and the subject of interest. This method provides significant advantages for engineers before excavation of the ground. The algorithm has been developed and implemented using MATLAB. It has been rigorously tested using both experimental data and simulated data generated through gprMax Numerical modeling tool. The algorithm achieves a minimum error of 0.02 in the lateral position (x) and 0.01 in the depth (Y) direction. These results demonstrate the algorithm's accuracy and reliability in identifying and locating buried objects.

Cross-Polarized SfM Photogrammetry for the Spatial Reconstruction of Challenging Surfaces, the Case Study of Dobšiná Ice Cave (Slovakia)

Geodetic methods are integral to mapping surface and subsurface objects and phenomena. Modern geodetic technologies such as laser scanning and digital photogrammetry have also become a standard part of the mapping and documentation of cave spaces. In some cases, these technologies cannot accurately capture the measured surface and thus provide reliable data. One such example is the ice with specific surface characteristics in caves with ice deposits. One of the world’s most studied ice caves is the Dobšiná Ice Cave (Slovakia), which has undergone significant changes in the ice-filling area and volume in recent years. To monitor and analyze all these changes properly, we need to know the surface and volume of this ice mass and monitor it regularly. Where modern geodetic methods such as terrestrial laser scanning (TLS) or digital photogrammetry may fail due to the ice’s physical properties, we propose using cross-polarized Structure-from-Motion (SfM) photogrammetry. As a case study, this method was used in a 28 m long ice tunnel in this cave. Two polarizing filters (on the flash as a light source and on the camera lens) were used in 90° rotation to each other to achieve the cross-polarization effect and remove surface reflections. This removed the surface reflections, giving us a compact and accurate point cloud of the entire tunnel. The dense cloud from cross-polarized (CP) photogrammetry is denser and more compact and does not contain as many outliers and noise points when compared to non-cross-polarized (non-CP) photogrammetry. The TLS point cloud covers the entire surface of the tunnel without significant holes; however, the penetration of the beam through the ice makes such a cloud unusable. Only the cloud from CP photogrammetry covers the entire surface of the tunnel densely enough without additional noise. This methodology can then be used in other parts of the cave or other geomorphological applications to suppress reflections so high-quality results for further processing and analysis can be obtained.

Classification Size of Underground Object from Ground Penetrating Radar Image using Machine Learning Technique

Ground Penetrating Radar (GPR) is a useful tool in detecting subsurface object or hidden structure defects However, the time-consuming problems and high requirement of professional manpower is required to analyse the GPR data. Machine learning is a tool that endowed with the ability to learn, and it can reduce time taken for the GPR data analysing. To simplify the identification process, a framework is proposed to classify the size of underground metallic pipe by using Histogram of Oriented Gradient (HOG) as a feature extraction algorithm. Two machine learning algorithms namely Support Vector Machines (SVM) and Backpropagation Neural Network were proposed to classify the size of the underground metallic pipe. As a result, the accuracy from the identification is more than 98% for both classifier algorithm.

Development of a hybrid neural network model for mine detection by using ultrawideband radar data

The object of the study is the architecture of a hybrid neural network for mine recognition using ultra-wideband radar data. The work solves the problem of filtering reflected signals with interference and recognizing mines detected by ultra-wideband (UWB) radar. A hybrid neural network model in combination with the Adam learning algorithm is proposed. Filtering of reflected signals from mines is carried out using an MLP (multilayer perceptron) filter, which selects low-amplitude parts of signals that carry information about a hidden mine from the entire reflected signal. Mine recognition is carried out by a Hilbert block and an oscillatory neural network, which are included in the structure of a hybrid neural network. The peculiarity of the obtained results, which allowed to solve the investigated problem, is the transformation of the signal frequency by the Hilbert block and the recognition of mines by the oscillatory neural network in the resonant mode. The three-layer MLP filter effectively filters out the unwanted component in the total signal reflected from the subsurface object, as the MSE (Mean Squared Error) of the MLP filter is 1·10-5. If the frequency of the Hilbert signal is equal to the natural frequency of oscillations of neurons then the recognition of signals with a small amplitude from subsurface objects is carried out by an oscillatory neural network based on the resonant amplitude, which is indicated by a small value of cross-entropy. The proposed model of a hybrid neural network provides amplification of useful signals due to resonance and has higher performance compared to existing models of artificial neural networks. The practical significance of the obtained results lies in their application in the field of automated neural network technologies for detection and recognition of subsurface objects of various nature based on reflected radar signals with an amplitude at the noise level

Polarization synthesis applied to UHF band SAR data for subsurface object detection

Polarization synthesis applied to UHF band SAR data for subsurface object detection

Improving 3D Metric GPR Imaging Using Automated Data Collection and Learning-Based Processing

Ground Penetrating Radar (GPR) is one of the most important non-destructive evaluation (NDE) devices to detect subsurface objects (i.e., rebars, utility pipes) and reconstruct the underground scene. There are two challenges for GPR-based inspection, which are GPR data collection and 3D subsurface object imaging. To address these challenges, we first propose a robotic solution that automates the GPR data collection process with a free motion pattern. It facilitates the 3D metric GPR imaging by tagging the pose with GPR measurement in real-time. Moreover, to improve the 3D GPR imaging, we introduce a learning-based GPR data analysis method, which includes a noise removal module to clear the background noise in raw GPR data and a Convolutional Recurrent Neural Network (CRNN) to estimate the dielectric value of subsurface medium in each GPR B-scan data. We use both field and synthetic data to verify the proposed methods. Experimental results demonstrate that our proposed methods can achieve higher performance and faster processing speed in 3D GPR imaging than baseline methods.

A REVIEW OF 3D SPATIAL DATA MODELS FOR SURFACE AND SUBSURFACE INTEGRATION OF SPATIAL OBJECTS

Abstract. Three dimensional (3D) spatial data models have been known as a geographic information system platform for representing the dimensionality of spatial objects with respect to the real world. They can be used to mimmick the real-world objects above the ground, on the ground and underground. A 3D spatial data model that can integrate surface and subsurface elements will have a significant impact on engineering, spatial and urban planning, and the built environment. Such an impact includes good infrastructure development, proper planning, result-oriented installation of utilities, location-specific excavation of existing utilities, and cost-effective economy resulting from minimal damage of infrastructures. This article explores and documents the state-of-the-art with respect to 3D spatial data models specifically for the integration of surface and subsurface objects based on a thorough review of the literature in academic articles, technical reports, and web-based materials. To better understand the scope, this paper reviews the different 3D spatial modelling approach for surface spatial objects, subsurface spatial objects, and with keen focus on surface and subsurface spatial objects integration. The Open Geospatial Consortium (OGC) has played an important role in data integration and has a core space in 3D spatial data model evolution. One of the recent data models for such task is CityJSON due to its simplicity in data storage, visualization, manipulation, and data update compared to CityGML. The parameters influencing the choice of models can be attributed to the availability of data structure, applicability, exchange format and relevance. Based on the range of 3D spatial data model reviewed, a unified model is expedient for integration of surface and subsurface spatial objects.

Technology Focus: Well Testing (February 2023)

The energy transition is in a continuous pursuit of innovative technology applications from all corners of the oil and gas industry. With the exponential growth of carbon capture and sequestration (CCS) projects, similar subsurface appraisal objectives remain. Derisking dynamic reservoir performance and characterizing storage pore space are key enablers for prospective CCS projects. The mention of well testing often takes readers to a visualization of hydrocarbon disposition by a flare. Once a bright and vibrant spectacle of our industry’s exploration updates, the days of flaunting a chairman’s flow or banker’s burn are past. Waning application of well testing during our energy transition is a specious assumption, and it’s the optics that are transforming as the well testing discipline proves its value to long-term carbon storage projects. Injecting CO2 into the subsurface is hardly a new concept; operators have been doing this for decades as part of enhanced oil recovery. However, the application of injecting CO2 is changing in response to operators’ environmental, social, and governance ambitions. While carbon storage concepts exist in many forms, anchoring a storage project requires pore space of adequate quantity and quality in proximity to a fixed source of emissions. Carbon storage projects also must manage additional risks such as sustained injectivity performance, geologic seal integrity, and plume migration, to name a few. Enter well testing. Quantifying injection performance of supercritical fluids is crucial for optimizing wells to meet project needs while minimizing the number of required penetrations through a structural seal. Whether for a saline aquifer or a previously depleted field, pressure transient analysis enhances understanding of the pore space intended for storage and the potential heterogeneities within. Furthermore, integrating well testing with other reservoir-description tools may be used to monitor migration of stored fluids within the reservoir. With corporate and regulatory targets driving the unprecedented pace and scale of CCS opportunities, well testing is quickly reaffirming itself as a powerful tool in characterizing pore space essential to our lower-carbon goals. This month’s papers highlight ongoing developments within the well testing discipline and important reminders about how to properly use dynamic data. The application of these well testing fundamentals to a nascent carbon storage market is still evolving. Recommended additional reading at OnePetro: www.onepetro.org. SPE 208967 Rate-Pseudopressure Deconvolution Enhances Rate-Time Models Production History-Matches and Forecasts of Shale Gas Wells by L.M. Ruiz Maraggi, The University of Texas at Austin, et al. URTeC 3705570 Analysis of Multiple Flow/Buildup Tests Including a 5-year Buildup: Case Study of an Australian Shale Gas Well by Christopher R. Clarkson, University of Calgary, et al. OTC 31691 Challenges and Mitigation Strategies for High-Rate Gas Well Testing in High-Pressure/High-Temperature DST Operation by Jakpakorn Hemaprasertsuk, PTTEP, et al.