Ground Penetrating Radar Radargrams Research Articles

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.

Toward the optimisation of the Kalman Filter approach in ground penetrating radar application for detection and locating buried utilities

Ground Penetrating Radar (GPR) stands as a pivotal non-destructive tool for identifying and assessing buried utilities. However, the noisy GPR radargram data requires extensive expert interpretation, making it time-consuming and subjective. While various signal processing techniques, like time-zero correction and background subtraction, help mitigate noise, mathematical estimators like the Kalman Filter (KF) and particle filter present advanced solutions. Notably, KF, known for its efficiency and computational benefits, excels in denoising and decluttering GPR radargrams. This research introduces an innovative KF-based optimisation algorithm tailored to minimise user input and pinpoint buried utilities and anomalies. Leveraging the distinctive KF parameter, Normalised Innovation Squared (NIS), the algorithm aims for enhanced target detection. A genetic algorithm-driven multi-objective optimisation model evaluates the method's efficacy, focusing on the receiver operating characteristic (ROC) and mean of innovations. Cost functions considered encompass noise covariances and optimal Fourier analysis frequency. Preliminary results showed a 23.13% rise in the area under the ROC with optimised parameters (91.4%) compared to user-selected ones (68.27%). This method not only reduces GPR data noise but also augments the detection of buried utilities. Traditional chi-squared hypothesis testing was replaced with an NIS signal function analysis, facilitating more refined anomaly detection. Collectively, this study offers a transformative approach to GPR data post-processing, emphasising efficiency and reduced user dependency.

Yield Adjustment Using GPR-Derived Spatial Covariance Structure in Cassava Field: A Preliminary Investigation

Many processes concerning below-ground plant performance are not fully understood, such as spatial and temporal dynamics and their relation to environmental factors. Accounting for these spatial patterns is very important as they may be used to adjust for the estimation of cassava fresh root yield masked by field heterogeneity. The yield of cassava is an important characteristic that every breeder seeks to maintain in their germplasm. Ground-Penetrating Radar (GPR) has proven to be an effective tool for studying the below-ground characteristics of developing plants, but it has not yet been explored with respect to its utility in normalizing spatial heterogeneity in agricultural field experiments. In this study, the use of GPR for this purpose was evaluated in a cassava field trial conducted in Momil, Colombia. Using the signal amplitude of the GPR radargram from each field plot, we constructed a spatial plot error structure using the variance of the signal amplitude and developed GPR-based autoregressive (AR) models for fresh root yield adjustment. The comparison of the models was based on the average standard error (SE) of the Best Linear Unbiased Estimator (BLUE) and through majority voting (MV) with respect to the SE of the genotype across the models. Our results show that the GPR-based AR model outperformed the other models, yielding an SE of 9.57 and an MV score of 88.33%, while the AR1 × AR1 and IID models had SEs of 10.15 and 10.56% and MV scores of 17.37 and 0.00%, respectively. Our results suggest that GPR can serve a dual purpose in non-destructive yield estimation and field spatial heterogeneity normalization in global root and tuber crop programs, presenting a great potential for adoption in many applications.

What Indicative Information of a Subsurface Wetted Body Can Be Detected by a Ground-Penetrating Radar (GPR)? A Laboratory Study and Numerical Simulation

Nondestructive and noninvasive visualization and quantification of soil wetted bodies (SWBs) is of great significance to the development of water-saving agriculture. Unfortunately, measuring the parameters of SWBs is difficult due to the invisibility of SWBs buried underneath the ground and the non-variability of the soil moisture under partial irrigation conditions. Therefore, we performed a laboratory experiment to investigate what SWB attributes can be detected by a GPR. In the laboratory, three typical partial irrigation experiments were conducted to collect the GPR data of SWBs of different sizes, shapes, and burial depths. Additionally, numerical simulation scenarios were designed according to the laboratory experiment. Then, the simulated and measured GPR data were processed by the FK migration method. Based on the simulation, a calibration model for the width of SWBs was constructed. We found that SWB attributes, such as type and location can be obtained from raw radargrams owing to the obvious reflection of the top and bottom interfaces. The results showed that estimating the depth and thickness of SWBs from FK migration radargrams is more reliable than from raw radargrams. Moreover, estimation of the width of SWBs relies on the FK migration radargrams. Our findings indicate that the size and depth of SWBs dominate the width detection accuracy, and the estimated width gained via the width calibration model is improved. Our results highlight the potential for using GPR data to detect SWBs, as well as the potential of using numerical simulation, FK migration, and calibration modeling in combination to extract the size information of SWBs from GPR radargrams.

Implementation of EML and GPR Technology for Underground Utilities Detection

Underground utility mapping is important for extracting underground utility information such as electrical and telephone cables, gas pipes, drainage pipes and water pipes. Electromagnetic Location Surveys (EML) and Ground Penetrating Radar (GPR) have been widely used for mapping underground utilities at certain depths and refined utility characteristics. If it involves certain soils such as involving soils that are harder than usual, there is a possibility that the waves transmitted by the GPR are slower as compared to softer soils. Even the moisture rate on the soil also affects the GPR image. This study was conducted to find out how these two equipments are used with reference to the accuracy of hyperbolic images in GPR Radargram. The methodology carried out is by utility installation detection work and marking of detection information using EML and GPR by taking several images using the same frequency in the same area which is located in Perumahan Makmur Penor Utara Jaya (PUJ) using GPR model equipment (Leica DS2000), GPS (Topcon Hyper V) and EML (RD8100). Thus, the analysis found that appropriate conditions was when electrical cables can be detected more easily by using the clamping method which used the "peak" mode at frequencies of 8KHz - 33 KHz for EML devices and for GPR. The use of high frequencies (1GHz or more) is more suitable at making detection work for smaller utilities, in line with the formula 0.1*λ, where λ is the wavelength. Therefore, the results of this study are very important for quality improvement during data collection work as well as an increase in the production of high-quality underground utility maps.

Estimation Diameter of Buried Pipe Using Principle of Ground Penetrating Radar and Electromagnetic Locator

A comprehensive utility map presents a complete information for users to interpret and extract the information. Its importance for underground utility database to support future infrastructure planning. However, there is not all information is available such as material, voltage, colour and diameter of pipe. The surveyor needs to observe the surrounding area of survey such as utility marker and utility box. There are lots of utility marker was missing and damage due to the road maintenance and construction work. Hence, to ensure a complete information could be presented on the map, the surveyor needs to find a method to gather the information. In this study the geophysics method was used to identify the diameter of buried pipe. The integration of geophysics principles is tested between Electromagnetic Locator (EML) and Ground penetrating Radar (GPR). The test was conducted at Jalan B2-B9, Taman Melawati, Kuala Lumpur where five points were marked on the ground as point 1, 2, 3, 4 and 5. The EML with 33kHz & 65kHz frequency have been used to locate the position and depth of buried pipe using Direct Connection technique. The GPR antenna with 700MHz frequency were tested to penetrate the subsurface up to 2.5 meter, respectively. The post processing is carried out to enhance the quality of GPR radargram profile. Thus, the velocity of hyperbola was calibrated trough curve fitting process to get the actual depth of buried pipe. The result of the study shown that the diameter of pipe is 100mm in which corresponded to the information at the pipe marker. EML and GPR is a useful method to detect the diameter of pipe, although it is required field verification and the proper selection of antenna frequency.

Reconstructing unseen spaces in collapsed structures for search and rescue via deep learning based radargram inversion

This paper developed a novel deep learning-based approach to processing ground-penetrating radar (GPR) radargrams to reconstruct the occluded interior spaces of collapsed structures and extract essential information such as survivable void spaces to assist search-and-rescue operations. The proposed method innovatively exploits a generative adversarial network (GAN) to augment synthetic GPR training data and an end-to-end deep learning model to invert a GPR radargram to a permittivity map of the cross-section that can be further interpreted to reconstruct the interior scenarios of collapsed structures. First, to address the lack of training data with correct labels, synthetic GPR radargrams were generated from simulated scenarios of collapsed structures. The GAN was applied to augment the realism of synthetic GPR radargrams for training, providing a new mechanism for preparing and augmenting data that is difficult to collect from a real disaster site. Second, instead of detecting and segmenting nonintuitive features in GPR radargrams, a new encoder-decoder structure was trained using the augmented GPR radargrams to directly reconstruct permittivity maps corresponding to the cross-sections of collapsed structures. The visual Turing test indicated that the GAN substantially improved the realism of synthetic GPR radargrams. The proposed GPR inversion method achieved an R2 value of 0.93, a Mean Absolute Error (MAE) of 0.73, and a Structural Similarity Index Measure (SSIM) of 0.95 in inferring the material permittivity from synthetic radargrams. The predicted permittivity map was further used to identify void spaces, achieving an F1 score of 64.34%, a precision of 63.06%, and a recall of 71.84% at the pixel level. On the augmented radargrams, the network achieved an R2 value of 0.76, an MAE of 1.49, and an SSIM of 0.89 in predicting the permittivity map. The void detection on augmented radargrams achieved an F1 score of 45.20%, a precision of 45.35%, and a recall of 53.99% at the pixel level. The feasibility of using the proposed inversion network to reconstruct permittivity maps from radargrams is also experimentally tested and demonstrated in simulations of two multistory building collapses.

3D Migration Depth Focus Velocity Analysis of Hand-Held Ground Penetrating Radar

Hand-held ground penetrating radar (GPR) systems have been widely applied to landmine detections during recent decades. The accuracy of an imaging result by migration for a hand-held GPR is strongly related to the accuracy of subsurface velocity distribution obtained from multi offset data. For shallow targets like landmines, the hyperbolas are usually not distinct in 2D slices and are masked by the surface reflections. In this article, we propose a 3D migration depth focus velocity analysis method for hand-held GPRs to estimate the background velocity of the subsurface. This method is performed based on the images generated by migrations. The objective function is defined as the proportion of the target on the depth slice containing the target. After migrating a GPR radargram with different velocities, the background velocity, which minimizes the objective function, can be determined by comparing the imaging results by migration using different velocities. To test the proposed method, we apply this procedure to experimental GPR data collected with an advanced landmine imaging system (ALIS) in the laboratory. Subsequently, the velocity of the background is obtained, 3D diffraction migration with the obtained velocity achieves subsurface imaging with high quality. The accurate position and depth of the target are obtained from the optimal migration image.

Estimating Parameters of the Tree Root in Heterogeneous Soil Environments via Mask-Guided Multi-Polarimetric Integration Neural Network

Ground-penetrating radar (GPR) has been used as a non-destructive tool for tree root inspection. Estimating root-related parameters from GPR radargrams greatly facilitates root health monitoring and imaging. However, the task of estimating root-related parameters is challenging as the root reflection is a complex function of multiple root parameters and root orientations. Existing methods can only estimate a single root parameter at a time without considering the influence of other parameters and root orientations, resulting in limited estimation accuracy under different root conditions. In addition, soil heterogeneity introduces clutter in GPR radargrams, making the data processing and interpretation even harder. To address these issues, a novel neural network architecture, called mask-guided multi-polarimetric integration neural network (MMI-Net), is proposed to automatically and simultaneously estimate multiple root-related parameters in heterogeneous soil environments. The MMI-Net includes two sub-networks: a MaskNet that predicts a mask to highlight the root reflection area to eliminate interfering environmental clutter, and a ParaNet that uses the predicted mask as guidance to integrate, extract, and emphasize informative features in multi-polarimetric radargrams for accurate estimation of five key root-related parameters. The parameters include the root depth, diameter, relative permittivity, horizontal and vertical orientation angles. Experimental results demonstrate that the proposed MMI-Net achieves high estimation accuracy in these root-related parameters. This is the first work that takes the combined contributions of root parameters and spatial orientations into account and simultaneously estimates multiple root-related parameters. The data and code implemented in the paper can be found at https://haihan-sun.github.io/GPR.html.

Learning to Remove Clutter in Real-World GPR Images Using Hybrid Data

The clutter in the ground-penetrating radar (GPR) radargram disguises or distorts subsurface target responses, which severely affects the accuracy of target detection and identification. Existing clutter removal methods either leave residual clutter or deform target responses when facing complex and irregular clutter in the real-world radargram. To tackle the challenge of clutter removal in real scenarios, a clutter-removal neural network (CR-Net) trained on a large-scale hybrid dataset is presented in this study. The CR-Net integrates residual dense blocks into the U-Net architecture to enhance its capability in clutter suppression and target reflection restoration. The combination of the mean absolute error (MAE) loss and the multi-scale structural similarity (MS-SSIM) loss is used to effectively drive the optimization of the network. To train the proposed CR-Net to remove complex and diverse clutter in real-world radargrams, the first large-scale hybrid dataset named CLT-GPR dataset containing clutter collected by different GPR systems in multiple scenarios is built. The CLT-GPR dataset significantly improves the generalizability of the network to remove clutter in real-world GPR radargrams. Extensive experimental results demonstrate that the CR-Net achieves superior performance over existing methods in removing clutter and restoring target responses in diverse real-world scenarios. Moreover, the CR-Net with its end-to-end design does not require manual parameter tuning, making it highly suitable for automatically producing clutter-free radargrams in GPR applications. The CLT-GPR dataset and the code implemented in the paper can be found at https://haihan-sun.github.io/GPR.html.

Prediction of Root Biomass in Cassava Based on Ground Penetrating Radar Phenomics

Cassava as a world food security crop still suffers from an inadequate means to measure early storage root bulking (ESRB), a trait that describes early maturity and a key characteristic of improved cassava varieties. The objective of this study is to evaluate the capability of ground penetrating radar (GPR) for non-destructive assessment of cassava root biomass. GPR was evaluated for this purpose in a field trial conducted in Ibadan, Nigeria. Different methods of processing the GPR radargram were tested, which included time slicing the radargram below the antenna surface in order to reduce ground clutter; to remove coherent sub-horizontal reflected energy; and having the diffracted energy tail collapsed into representative point of origin. GPR features were then extracted using Discrete Fourier Transformation (DFT), and Bayesian Ridge Regression (BRR) models were developed considering one, two and three-way interactions. Prediction accuracies based on Pearson correlation coefficient (r) and coefficient of determination (R2) were estimated by the linear regression of the predicted and observed root biomass. A simple model without interaction produced the best prediction accuracy of r = 0.64 and R2 = 0.41. Our results demonstrate that root biomass can be predicted using GPR and it is expected that the technology will be adopted by cassava breeding programs for selecting early stage root bulking during the crop growth season as a novel method to dramatically increase crop yield.

Foundation features investigation of a heritage structure in India through noninvasive and invasive techniques

Investigation of the foundation system of the heritage site is a challenging task. The heritage site in the present study is Sun temple, Konark. The temple is a 700–800 years old stone masonry structure and world heritage site in the Indian state of Odisha. The investigation of the foundation details, i.e., type, depth, and horizontal extent, along with the soil layering below and around the temple premises, involves non-invasive and invasive techniques. The shielded and unshielded Ground Penetrating Radar (GPR) antenna, with a central frequency of 250 MHz & 500 MHz, and 100 MHz, are used for non-invasive investigation. In addition, invasive investigation, i.e., drilling at the top of the platform, at the plinth level, collection of samples, and characterization are performed to determine the soil layering below and around the temple premises. The GPR radargrams of the layering system corroborated with the bore logs obtained by performing the invasive investigation. The non-invasive and invasive study establishes the foundation system as a block type having a depth of 10.5 m from the top of the temple platform. The study demonstrates the efficacy of GPR as a support system for conventional invasive techniques. The paper in detail presents the GPR investigations, their inferences, invasive testing, and the corroboration of the non-invasive and invasive tests.

Frequency based signal processing technique for pulse modulation ground penetrating radar system

This paper discusses the method of processing the pulse modulation (PM) ground penetrating radar (GPR) system to detect an embedded object underground. The proposed technique is using frequency domain operation which can be classified based on two parameters which are magnitude and phase. The process of detecting the position and depth of iron objects in dry sandy soil is easier to identify using the techniques and parameters that have been introduced. The selection of the Dipole antenna as a sensor device to detect iron objects has been designed in a frequency range of 70 MHz to 80 MHz. Based on the simulation, the proposed technique seems to be able to detect underground iron objects. By using the magnitude value, the underground iron object that can be detected as displayed in GPR radargram is in the depth range from 0 mm until 1000 mm. Meanwhile, by using the phase value, the embedded underground iron object detected is in the range of depth between 900 mm and 1000 mm. Therefore, based on this promising result, the proposed technique and parameters are considered to be used in

Networks of unusually large fossil periglacial polygons, Campine area, northern Belgium

A series of polygon networks has been discovered on the most recent LiDAR (Light Detection and Ranging) DEM (Digital Elevation Model) of Flanders (Belgium) available in a resolution of 1 m2. They are located in the sandy Campine area (northern Belgium) and resemble thermal contraction crack polygon networks from present-day permafrost regions. Different network types were observed, ranging from orthogonal to hexagonal and various combinations of these. The inter-polygon troughs are typically several decimeters deep and up to several meters wide. The average polygon size is ca. 3000 m2, which is equivalent to a diameter of ca. 60 m if the polygon shape is approximated with a perfect circle, or a side of ca. 55 m length if it were to be approximated by a perfect square. The average size is (much) larger than any of the studied present-day analogues, and also larger than fossil networks in the western part of Flanders, Poland and France. In contrast to the Campine polygons presented here, the fossil analogues in these countries were detected using satellite imagery and orthophotos, which may partially explain the observed size differences. The morphometric analysis of the Campine networks shows relationships between polygon type and local geomorphological position as orthogonal networks seem to have a preference to develop near shallow valley slopes. In addition, GPR (Ground Penetrating Radar) radargrams were acquired across polygon boundaries to investigate subsoil disturbances related to the former position of ice wedges or sand wedges. However, the evidence is not unequivocal due to the low dielectric contrast between the host and wedge material. It is not clear yet whether smaller 2nd and 3rd order cracks did develop but without leaving a topographical imprint. The observed polygon networks in the Campine area are interpreted as first-order networks that developed during a time span of several thousands of years, up to 10 kyr at most, in a former Late Weichselian permafrost climate.

Integrated geophysical techniques for subsurface imaging of active deformation across the Himalayan Frontal Thrust in Singhauli, Kala Amb, India

Non-invasive geophysical techniques employed across the Himalayan Frontal Thrust (HFT) at Singhauli in the north-western Frontal Himalaya to understand shallow subsurface geological structures and their nature in the context of active tectonics. The combination of high-resolution Ground Penetrating Radar (GPR), Electrical Resistivity Tomography (ERT) and Multi-channel Analysis of Surface Waves (MASW) techniques with the different acquisition parameters found to be beneficial in gaining high-resolution images of shallow subsurface geological structures. The contact zone of resistivity obtained between <134 and >134 Ωm suggests the juxtaposition of dissimilar lithological units explained as two faults dipping to the northwest related to the HFT. The average separation between Fault 0 and Fault I is 12m which in general agreement with the trench excavation survey. The GPR radargram over a length of 45m show offset reflectors at a horizontal distance of ~12–15m close to the active deformation zone. The horizontal reflectors abruptly truncate across two northerly dipping planes. The 2D Vs section (MASW1&2) exhibited dipping attitude of the litho units (with different velocity zones) in the northeast direction which are also consistent with the local geological setting. Prominent liquefaction feature identified in the ERT section specifically with the 1 and 1.5m electrode spacing show explicit expression of its existence due to contrasting resistivity ranging between 100 and 450 Ωm. A strong correlation has been established between the field based geological observations, carried out earlier in this area through a trench excavation survey, with the present work using geophysical techniques. The integrated approach found to be highly beneficial in those areas where contrasting sub lithological units exist in terms of their physical properties. This study argues for judicious use of the ERT, GPR and MASW techniques to delineate shallow subsurface geology across various active faults in the Himalayan terrain to comprehend the active deformation related to large magnitude paleoearthquakes and earthquake hazard assessment.

Three-dimensional convolutional neural network–based underground object classification using three-dimensional ground penetrating radar data

Ground-penetrating radar is a typical sensor system for analyzing underground facilities such as pipelines and rebars. The technique also can be used to detect an underground cavity, which is a potential sign of urban sinkholes. Multichannel ground-penetrating radar devices are widely used to detect underground cavities thanks to the capacity of informative three-dimensional data. Nevertheless, the three-dimensional ground-penetrating radar data interpretation is unclear and complicated when recognizing underground cavities because similar ground-penetrating radar data reflected from different underground objects are often mixed with the cavities. As it is prevalently known that the deep learning algorithm-based techniques are powerful at image classification, deep learning-based techniques for underground object detection techniques using two-dimensional GPR (ground-penetrating radar) radargrams have been researched upon in recent years. However, spatial information of underground objects can be characterized better in three-dimensional ground-penetrating radar voxel data than in two-dimensional ground-penetrating radar images. Therefore, in this study, a novel underground object classification technique is proposed by applying deep three-dimensional convolutional neural network on three-dimensional ground-penetrating radar data. First, a deep convolutional neural network architecture was developed using three-dimensional convolutional networks for recognizing spatial underground objects such as, pipe, cavity, manhole, and subsoil. The framework of applying the three-dimensional convolutional neural network into three-dimensional ground-penetrating radar data was then proposed and experimentally validated using real three-dimensional ground-penetrating radar data. In order to do that, three-dimensional ground-penetrating radar block data were used to train the developed three-dimensional convolutional neural network and to classify unclassified three-dimensional ground-penetrating radar data collected from urban roads in Seoul, South Korea. The validation results revealed that four underground objects (pipe, cavity, manhole, and subsoil) are successfully classified, and the average classification accuracy was 97%. In addition, a false alarm was rarely indicated.

Qualitative research: The impact of root orientation on coarse roots detection using ground-penetrating radar (GPR)

The growth of coarse roots is complex, leading to intricate patterns of root systems in three dimensions. To detect and recognize coarse roots, ground-penetrating radar (GPR) was used. According to the GPR theory, a clear profile hyperbola is formed on the GPR radargrams when electromagnetic waves travel across two surfaces with different dielectric constants. First, the forward models (different root orientations) were built with simulation software (GprMax3.0) based on the finite-different time-domain method (FDTD). As the radar moved forward, the signal reflection curve was generated in different root orientations. An algorithm was proposed to obtain the coordinates of a single coarse root and analyze the influence of root direction on the hyperbola of coarse root through a symmetry curve and relative error (RE). Based on GPR datasets from the simulation experiment, the controlled experiment evaluated feasibility and effectiveness of the simulation experiment. To demonstrate the effect of the root orientation, the algorithm was applied to in situ recognition of the Summer Palace. The results showed that the localization of root orientation was relatively accurate. However, the proposed algorithm was unable to implement automatic detection, and the results still required human intervention. This research provides a solid basis for the biomass measurement, diameter estimation, and especially the three-dimensional reconstruction of ancient and famous trees.

Mapping an Agricultural Field Experiment by Electromagnetic Induction and Ground Penetrating Radar to Improve Soil Water Content Estimation

A growing interest in proximal sensing technologies for estimating soil water content (SWC) will be highlighted. On this matter the objectives of this study were: (1) to use both the combined electromagnetic induction (EMI) sensor and Ground Penetrating Radar (GPR) to characterize an innovative field experiment located in southern Italy, in which different agricultural practices are tested, including a soil hydraulic arrangement; (2) to implement a geostatistical approach in order to merge different geophysical sensor data as auxiliary variables for SWC estimation. The multi-sensor recorded data were: (1) SWC data measured by gravimetric method; (2) Differential Global Positioning System height; (3) apparent electrical conductivity measured by an EMI sensor; (4) depths of soil discontinuities individuated by GPR radargrams interpretation; and (5) amplitude of GPR signal data at two different frequencies. Geostatistical techniques were used both to map all variables and improve the SWC estimation. The findings of this research indicate that: (1) the GPR radargrams identified four reflection events as a consequence of interfaces; (2) the EMI and GPR mapping provided identification of areas with high potential for water stagnation; and (3) the outputs of geophysical sensors can be effectively used as auxiliary tools to supplement the sampling of the target variable and to improve water content estimation.

Image-based non-destructive evaluation method for building condition assessment

Building condition assessment (BCA) is one of the important approaches in total asset management during the asset life cycle. BCA is to monitor the functional status of the asset during the asset use and maintenance phase in the asset life cycle. With regards to this, various non-destructive evaluation (NDE) technologies are adopted for building structure physical condition assessment from time to time to prevent any catastrophic incident to occur. Two case studies on inspection of internal structure condition of concrete wall and slab using image-based NDE were presented in this study. A well-known NDE technology, namely ground penetrating radar (GPR) was used to scan the concrete wall and slab to identify the internal structure condition of the concrete wall and slab. The GPR radargram obtained by the shorter wavelength radar signal is evaluated to resolve the internal structure and the defect in detail. Results of this study prove that GPR can detect the internal structure of the concrete wall and locate the moisture in the concrete slab due to poor waterproof design. With such convincing results, the application of GPR has set a new benchmark in BCA for building physical condition assessment; a crucial aspect for better building maintenance and management in total asset management.

Integration of satellite remote sensing and geophysical techniques (electrical resistivity tomography and ground penetrating radar) for landslide characterization at Kunjethi (Kalimath), Garhwal Himalaya, India

During the extreme precipitation event of 15th–17th June 2013 in Garhwal Himalaya, glacial lake outburst flooding accompanied by numerous landslides and flash flood events caused widespread death and destruction. Many hill slopes and steep river banks had developed fractures and fissures indicative of landslides and one such scarp was observed at Kunjethi (Kalimath) village on satellite images during routine analysis. Rainfall threshold analysis for years 2012, 2013, 2014 and 2015 has confirmed that it was the main triggering factor which happened due to extreme precipitation in June 2013 leading to scarp development and initiation of landslide. In order to characterize the landslide and get subsurface information, two highly cost-effective and fast non-invasive geophysical techniques, electrical resistivity tomography (ERT) and ground penetrating radar (GPR), were used. Four ERT profiles and one GPR radargram profile were used to determine the approximate depth to slip surface, which is inferred around 15–19 m. Integration of satellite remote sensing, geophysical studies and field observations have been used to demarcate the maximum possible slide zone. This study reiterates that earth observation tools in integration with faster, non-invasive and cost-effective geophysical techniques can establish the slip surface, which is an essential information required for landslide hazard mitigation measures.