Correlation of Ground Penetrating Radar Data with Geotechnical Prospect Profiles: Reduto Case Study, Belém-PA, Brazil

The study presented in this manuscript aimed to relate the sedimentary strata imaged by the ground penetrating radar(GPR)method through numerical modeling with the mapping of sedimentary strata acquired through geotechnical surveys.The study aimed to expose how obtaining subsoil information through noninvasive/destructive electromagnetic waves is beneficial,as they are reliable and less costly than drilling holes beyond what is necessary to have a subsurface mapping.In this sense,physical-geological modeling was carried out.The information on the type of sediments,acquired through simple recognition surveys carried out in the city of Belém-PA,helped to create a model of a sedimentary package with its respective intrinsic physical properties.The result shows that the GPR recovered with good vertical and horizontal resolution at the beginning and end of the layers of the sedimentary package studied,proving to be very effective for locating geotechnical sounding points and safely reducing costs.

Feature Extraction and Classification of Echo Signal of Ground Penetrating Radar

Automatic feature extraction and classification algorithm of echo signal of ground penetrating radar is presented. Dyadic wavelet transform and the average energy of the wavelet coefficients are applied in this paper to decompose and extract feature of the echo signal. Then, the extracted feature vector is fed up to a feed forward muhi layer perceptron classifier. Experimental results based on the measured GPR, echo signals obtained from the Mei shan railway are presented.

Root imaging from ground penetrating radar data by CPSO-OMP compressed sensing

As the amount of data produced by ground penetrating radar （GPR） for roots is large, the transmission and the storage of data consumes great resources. To alleviate this problem, we propose here a root imaging algorithm using chaotic particle swarm optimal （CPSO） compressed sensing based on GPR data according to the sparsity of root space. Radar data are decomposed, observed, measured and represented in sparse manner, so roots image can be reconstructed with limited data. Firstly, radar signal measurement and sparse representation are implemented, and the solution space is established by wavelet basis and Gauss random matrix; secondly, the matching function is considered as the fitness function, and the best fitness value is found by a PSO algorithm; then, a chaotic search was used to obtain the global optimal operator; finally, the root image is reconstructed by the optimal operators. A-scan data, B-scan data, and complex data from American GSSI GPR is used, respectively, in the experimental test. For B-scan data, the computation time was reduced 60 % and PSNR was improved 5.539 dB; for actual root data imaging, the reconstruction PSNR was 26.300 dB, and total computation time was only 67.210 s. The CPSO-OMP algorithm overcomes the problem of local optimum trapping and comprehensively enhances the precision during reconstruction.

Modified Method for Estimating Organic Carbon Density in Discontinuous Karst Soil Using Ground-Penetrating Radar and Geostatistics

The conventional method which assumes the soil distribution is continuous was unsuitable for estimating soil organic carbon density(SOCD) in Karst areas because of its discontinuous soil distribution. The accurate estimation of SOCD in Karst areas is essential for carbon sequestration assessment in China. In this study, a modified method,which considers the vertical proportion of soil area in the profile when calculating the SOCD, was developed to estimate the SOCD in a typical Karst peak-cluster depression area in southwest China. In the modified method, ground-penetrating radar(GPR) technology was used to detect the distribution and thickness of soil. The accuracy of the method was confirmed through comparison with the data obtained using a validation method, in which the soil thickness was measured by excavation. In comparison with the conventional method and average-soil-depth method,the SOCD estimated using the GPR method showed the minimum relative error with respect to that obtained using the validation method. At a regional scale, the average SOCDs at depths of 0-20 cm and 0-100 cm, which were interpolated by ordinary kriging,were 1.49(ranging from 0.03-5.65) and 2.26(0.09-11.60) kgm-2based on GPR method in our study area(covering 393.6 hm2), respectively. Therefore, the modified method can be applied on the accurate estimation of SOCD in discontinuous soil areas such as Karst regions.

Interpretation of Dune Genesis from Sedimentogical Data and Ground Penetrating Radar (GPR) Signatures: A Case Study from Ashirmata Dune Field, Mandvi Beach, Gujarat, India

The coastal dunes located near the Ashirmata region, south of Mandvi beach lies near the straight coast have been stud-ied by making use of sedimentological information and Ground Penetrating Radar (GPR) data. Sedimentological analy-sis reveals the NNW-SSE trending longitudinal dunes consists of well sorted fine sands with unimodal distribution pos-sibly formed by constant wind gust and also the point out to the origin of sediments from single source;mostly the sediments derived from Indus delta transported to beach by long shore drift and tidal waves, carried inland by local on-shore winds. The radargram confirms, the homogenous sand layers with paleosols at shallow depth slip faces are proba-bly formed due to extreme storm activity of Recent.

Finite difference time domain method forward simulation of complex geoelectricity ground penetrating radar model

The ground penetrating radar(GPR) forward simulation all aims at the singular and regular models, such as sandwich model, round cavity, square cavity, and so on, which are comparably simple. But as to the forward of curl interface underground or “v” figure complex model, it is difficult to realize. So it is important to forward the complex geoelectricity model. This paper takes two Maxwell’s vorticity equations as departure point, makes use of the principles of Yee’s space grid model theory and the basic principle finite difference time domain method, and deduces a GPR forward system of equation of two dimensional spaces. The Mur super absorbed boundary condition is adopted to solve the super strong reflection on the interceptive boundary when there is the forward simulation. And a self-made program is used to process forward simulation to two typical geoelectricity model.

Wigner Frequency Point Slice Analysis of Superposition Data for Phased-Array Ground Penetrating Radar

According to the frequency property of Phasedarray ground penetrating radar (PGPR), this paper gives a frequency point slice method based on Wigner time-frequency analysis. This method solves the problem of analysis for the PGPR's superposition data and makes detecting outcome simpler and detecting target more recognizable. At last, the analytical results of road test data of the Three Gorges prove the analytical method efficient. Key words phased-array ground penetrating radar - wigner time-frequency analysis - superposition data - object identification CLC number TN 715.7 Foundation item: Supported by the National Nature Science Foundation of China (50099620) and 863 Program Foundation of China (2001AA132050-03)Biography: ZOU Lian (1975-), male, Ph. D candidate, research direction: signal processing.

A NOVEL SVM FOR GROUND PENETRATING SYNTHETIC APERTURE RADAR LANDMINE DETECTION

The use of vehicle- or air-borne Ground Penetrating Synthetic Aperture Radar (GPSAR) to quickly detect landmines over large areas is becoming a trend. However, producing too many false alarms in GPSAR landmine detection is a major challenge in practical applications of GPSAR. Support Vector Machine (SVM), employing structural risk minimization theory, does not need large amounts of training data, which makes it suitable for solving the landmine detection problem. In this paper, a novel SVM with a hypersphere instead of a hyperplane classification boundary is proposed for landmine detection in GPSAR. The HyperSphere-SVM (HS-SVM) can be trained with both landmine and clutter data, or with landmine data only, which are called the two-class HS-SVM and the one-class HS-SVM, respectively. The HS-SVM has better generalization capability than the traditional HyperPlane-SVM (HP-SVM) with respect to varying operating conditions. Quantitative comparisons have been made using real data collected with the rail-GPSAR landmine detection system, which show that both the two-class and the one-class HS-SVMs have better detection performance than the HP-SVM.

Regional characteristics of sea ice thickness in Canadian shelf and Arctic Archipelago measured by Ground Penetrating Radar

Ground Penetrating Radar （GPR） measurements of sea ice thickness including undeformed ice and ridged ice were carried out in the central north Canadian Archipelago in spring 2010. Results have shown a significant spatial heterogeneity of sea ice thickness across the shelf. The undeformed multi-year fast ice of （2.05±0.09） m thick was investigated southern inshore zone of Borden island located at middle of the observational section, which was the observed maximum thickness in the field work. The less thick sea ice was sampled across a flaw lead with the thicknesses of （1.05±0.11） m for the pack ice and （1.24±0.13） m for the fast ice. At the northernmost spot of the section, the undeformed multi-year pack ice was （1.54±0.22） m thick with a ridged ice of 2.5 to 3 m, comparing to the multi-year fast ice with the thickness of （1.67±0.16） m at the southernmost station in the Prince Gustaf Adolf Sea.

Research on Data Combination for Phased-Array Ground Penetrating Radar

To resolve the data combination of Phased-array Ground Penetrating Radar (PAGPR), we first build a model of PAGPR and a layered model, and then a new data combination algorithm is presented based on it. This method calculates time delay of multi-receivers, basing on the signal of the nearest receiver, then shifts other signals and adds them up, and gets one signal at last. It has been proved that this method can restrain noise, multiple waves, clutter waves and improve the precision of time location. In the end, an example is given to prove the method's efficiency.

Identifying soil structure along headwater hillslopes using ground penetrating radar based technique

Soil structure plays an important role in understanding soil attributes as well as hydrological processes. Effective method to obtain high quality soil map is therefore important for both soil science research and soil work ability improvement. However,traditional method such as digging soil pits is destructive and time-consuming. In this study, the structure of headwater hillslopes from Hemuqiao catchment(Taihu Basin, China) have been analyzed both by indirect(ground penetrating radar, GPR) and direct(excavation or soil auger) methods. Four transects at different locations of hillslopes in the catchment were selected for GPR survey. Three of them(#1, #2, and #3) were excavated to obtain fullscale soil information for interpreting radar images.We found that the most distinct boundary that can be detected by GPR is the boundary between soil and underlain bedrock. In some cases(e.g., 8-17 m in transect #2), in which the in situ soil was scarcely affected by colluvial process, different soil layers can be identified. This identification process utilized the sensitive of GPR to capture abrupt changes of soil characteristics in layer boundaries, e.g., surface organic layer(layer #1) and bamboo roots layer(layer#2, contain stone fragments), illuvial deposits layer(layer #3) and regolith layer(layer #4). However, in areas where stone fragments were irregularly distributed in the soil profile(highly affected bycolluvial and/or fluvial process), it was possible to distinguish which part contains more stone fragments in soil profile on the basis of reflection density(transect #3). Transect #4(unexcavated) was used to justify the GPR method for soil survey based on experiences from former transects. After that, O horizon thickness was compared by a hand auger.This work has demonstrated that GPR images can be of a potential data source for hydrological predictions.

The Impact of Frequency in Surveying Engineering Slopes Using Ground Penetrating Radar

Ground Penetrating Radar (GPR) is one of the non-invasive techniques commonly used to identify “anomalies” in the ground. It has been proven very effective in different fields ranging from the location of pipes and other underground services to the identification of archaeological sites. After the 1994 Kwun Lung Lau accident in Hong Kong, the Government has been commissioning the feasibility of different geophysics techniques to identify any issues related to engineering slopes and retaining walls. Among the different techniques tested during phase I, Electrical Imaging (EI) and Ground Penetrating Radar (GPR) were the most applicable in the study of old masonry walls. This paper aims to stress the importance of using the appropriate frequencies during the GPR survey of engineering slopes. In order to do that, two independent contractors who used different frequencies to carry out the GPR survey on the same area will be compared.

Mode decomposition methods and their application in ground penetrating radar data processing

Ground Penetrating Radar(GPR) method is a widely used method in engineering geophysical exploration at home and abroad. Compared with other geological exploration methods, the GPR method has the advantages of faster detection, higher resolution, convenient operation and relatively low detection cost. With the wide application and continuous development of GPR methods, the processing and interpretation of GPR data is increasingly important. The authors introduce the development process and current situation of the modal decomposition method in processing GPR data, summarize the principles of four modal decomposition methods, and compare their advantages and disadvantages in ground penetrating radar data processing. The results show that when the quality of GPR data is good and the noise is small, Empirical Mode Decomposition(EMD) and Ensemble Empirical Mode Decomposition(EEMD) methods can be used for processing, whereas when the noise interference is large or the underground medium is complex, Complete Ensemble Empirical Mode Decomposition(CEEMD) and Variational Mode Decomposition(VMD) methods can be used for processing. The four modal decomposition methods have their own advantages and disadvantages in GPR data processing. At present, the processing of GPR data by CEEMD and VMD methods is the focus of research and discussion at home and abroad.

Non-Split PML Boundary Condition for Finite Element Time-Domain Modeling of Ground Penetrating Radar

As a highly efficient absorbing boundary condition, Perfectly Matched Layer (PML) has been widely used in Finite Difference Time Domain (FDTD) simulation of Ground Penetrating Radar (GPR) based on the first order electromagnetic wave equation. However, the PML boundary condition is difficult to apply in GPR Finite Element Time Domain (FETD) simulation based on the second order electromagnetic wave equation. This paper developed a non-split perfectly matched layer (NPML) boundary condition for GPR FETD simulation based on the second order electromagnetic wave equation. Taking two-dimensional TM wave equation as an example, the second order frequency domain equation of GPR was derived according to the definition of complex extending coordinate transformation. Then it transformed into time domain by means of auxiliary differential equation method, and its FETD equation is derived based on Galerkin method. On this basis, a GPR FETD forward program based on NPML boundary condition is developed. The merits of NPML boundary condition are certified by compared with wave field snapshots, signal and reflection errors of homogeneous medium model with split and non-split PML boundary conditions. The comparison demonstrated that the NPML algorithm can reduce memory occupation and improve calculation efficiency. Furthermore, numerical simulation of a complex model verifies the good absorption effects of the NPML boundary condition in complex structures.

An element-free Galerkin method for ground penetrating radar numerical simulation

An element-free Galerkin method(EFGM) is used to solve the two-dimensional(2D) ground penetrating radar(GPR)modelling problems, due to its simple pre-processing, the absence of elements and high accuracy. Different from element-based numerical methods, this approach makes nodes free from the elemental restraint and avoids the explicit mesh discretization. First, we derived the boundary value problem for the 2D GPR simulation problems. Second, a penalty function approach and a boundary condition truncated method were used to enforce the essential and the absorbing boundary conditions, respectively. A three-layered GPR model was used to verify our element-free approach. The numerical solutions show that our solutions have an excellent agreement with solutions of a finite element method(FEM). Then, we used the EFGM to simulate one more complex model to show its capability and limitations. Simulation results show that one obvious advantage of EFGM is the absence of element mesh, which makes the method very flexible. Due to the use of MLS fitting, a key feature of EFM, is that both the dependent variable and its gradient are continuous and have high precision.

Layer-Constrained Triangulated Irregular Network Algorithm Based on Ground Penetrating Radar Data and Its Application

In this paper,a layer-constrained triangulated irregular network（ LC-TIN） algorithm is proposed for three-dimensional（ 3 D） modelling,and applied to construct a 3 D model for geological disease information based on ground penetrating radar（ GPR） data. Compared with the traditional TIN algorithm,the LCTIN algorithm introduced a layer constraint to the discrete data points during the 3 D modelling process,and it can dynamically construct networks from layer to layer and implement 3 D modelling for arbitrary shapes with high precision. The experimental results validated this method,the proposed algorithm not only can maintain the rationality of triangulation network,but also can obtain a good generation speed. In addition,the algorithm is also introduced to our self-developed 3 D visualization platform,which utilized GPR data to model geological diseases. Therefore the feasibility of the algorithm is verified in the practical application.

Ground Penetrating Radar(GPR) Applications in Hydrogeological Study of Aquifers

Ground penetrating radar is a noninvasive electromagnetic geophysical technique for subsurface exploration,characterization and monitoring.Ground penetrating radar is sometimes called georadar, ground probing radar,or subsurface radar,earth sounding radar / radar terrestre penetrant,Well Probing Radar,and Borehole Radar.The principles involved are similar to reflection seismology,except that electromagnetic energy is used instead of

Condition Assessment of August A. Busch Bridge Deck Using Portable Seismic Property Analyzer and Ground Penetrating Radar

Ground penetrating radar (GPR) and the portable seismic property analyzer (PSPA) have been extensively used in the past two decades for monitoring, quantifying, and mapping the deterioration of bridge decks. Using PSPA and GPR ensures regular monitoring of bridge conditions, leads to the early detection of deterioration. This research is to address the condition of August A. Busch bridge deck owned by the Missouri Department of Conservation. Visual inspection, GPR, and PSPA data were acquired on the bridge deck. Over 90% of the bridge deck was in fair to good condition with an average compressive strength of over 2500 psi. GPR data showed no indication of significant deterioration. The overall bridge deck was determined to be in fair to good condition.

An Algorithm for Estimating the Object Depth Accurately by Ground-Penetrating Radar

Ground penetrating radar (GPR) is a remote sensing technique used to obtain information on subsurface features from data collected over the surface. We propose an automatic algorithm for estimating object depth using f-k migration and velocity scanning methods in a homogeneous medium. To improve the accuracy of the algorithm, the formula used to calculate the GPR valid lateral aperture is also presented. Experimental results show that the relative estimating error of depth is as low as 5% in a homogeneous medium.