scholarly journals Between Natural and Anthropogenic Coastal Landforms: Insights from Ground Penetrating Radar and Sediment Analysis

2021 ◽  
Vol 11 (8) ◽  
pp. 3449
Author(s):  
Yuniarti Ulfa ◽  
Teoh Ying Jia ◽  
Ahmad Munim Che Yaziz ◽  
Dasapta E. Irawan ◽  
Deny J. Puradimaja
Keyword(s):  
Ground Penetrating Radar ◽  
Urban Landscape ◽  
Solid Wastes ◽  
Apparent Depth ◽  
Depth Of Penetration ◽  
Sediment Analysis ◽  
Coastal Landforms ◽  
Coastal Landform ◽  
Ground Penetrating ◽  
The Impact

Both natural and anthropogenic coastal landforms characterize Penang Island. As years have passed it is a challenge to differentiate the genuineness of landmasses created by natural geological formations or by coastal reclamation projects. An account is given of the environmental impact of solid wastes used for reclaiming land in coastal areas of Penang and of the impact of a major sewage outfall in the western channel. Leaching of heavy metals was shown to be one of the main sources of contamination from solid wastes. This paper presents eight lines of ground penetrating radar (GPR) surveys and sediment analysis to identify the anthropogenic interventions that shaped the urban landscape of Penang Island by excavations, filling, and embankment construction along the coastline and differentiate it from the natural one. The surveys were implemented in two locations, the Batu Ferringhi area, representing the natural coastline, and Persiaran Bayan Indah (the Queensbay Mall area), representing the anthropogenic coastal landform. The apparent depth of penetration that was achieved using a 250-MHz antenna is limited (less than 5 m). The results show between natural and anthropogenic sediment recorded different radar facies. In complement mode, mean grain size distribution, sorting, skewness, and kurtosis graphics of sediment samples from both sites correspond with the GPR data. This technique can likely be applied to the developing coast, where natural and anthropogenic coastal landform data is incomplete, considering future coastline development.

scholarly journals Valuation of Ground Penetrating Radar for the record of structures in fluvio lacustrine soils

2003 ◽  
Vol 1 (01) ◽  
Author(s):  
D. Rangel ◽  
D. Carreón ◽  
M. Cerca ◽  
E. Méndez
Keyword(s):  
Structural Properties ◽  
Ground Penetrating Radar ◽  
Transmitted Wave ◽  
Wave Frequency ◽  
Structural Studies ◽  
Depth Of Penetration ◽  
Geological Structures ◽  
Electromagnetic Pulses ◽  
Geological Characteristics ◽  
Ground Penetrating

In this work, the response of Ground Penetrating Radar (GPR) to geological characteristics of fluvio-lacustrine soils is analyzed. GPR method is a very useful tool for structural studies of the geological media because it provides continuous profiles from the subsoil (radargrams). The identification of thin geological structures in the radar profiles allowed the evaluation of the detection capacity of the GPR Zond 12c for stratigraphical purposes. Its detection capacity depends on the achieved depth of penetration and resolution, on the transmitted wave frequency, and of the system used for acquisition and processing of the signals. The prospecting principle is based on the emission and reception of short electromagnetic pulses that are reflected by electric discontinuities related to physical or structural properties of the ground.

scholarly journals Analysis of the dynamics of coastal landform change based on the integration of remote sensing and gis techniques: Implications for tidal flooding impact in pekalongan, central java, Indonesia

2019 ◽  
Vol 38 (3) ◽  
pp. 17-29 ◽  
Author(s):  
Fajar Yulianto ◽  
Suwarsono ◽  
Taufik Maulana ◽  
Muhammad Rokhis Khomarudin
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
High Tide ◽  
Coastal Areas ◽  
Change Analysis ◽  
Tidal Flooding ◽  
Gis Techniques ◽  
Coastal Landforms ◽  
Coastal Landform ◽  
The Impact

Abstract Coastal landforms are located in the interface zone between atmosphere, ocean and land surface systems formed by the geomorphic process of erosion, depositional, and subsidence. Studying the dynamics of coastal landform change is important for tracing the relationship between coastal landform changes and tidal flooding in the coastal areas of Pekalongan, Indonesia. The method of integrating remote sensing data with geographic information system (GIS) techniques has been widely used to monitor and analyze the dynamics of morphology change in coastal landform areas. The purpose of this study is to map the dynamics of landform change in the study area from 1978 to 2017 and to analyze its implications for the impact of tidal flooding. The results of the mapping and change analysis associated with coastal landforms can be classified into four landform types: beach, beach ridge, backswamp and alluvial plain. Changes in coastal morphology and landform topography affected by land subsidence and changes in land use/ land cover have contributed to the occurrence of tidal flooding in the study area. Beach ridges perform an important role as natural levees which hold back and prevent the entry of seawater at high tide in coastal areas. A limitation of this study is that, as it focuses only on the physical aspects of coastal landform characteristics for one of the factors causing tidal flooding.

scholarly journals Working with Gaussian Random Noise for Multi-Sensor Archaeological Prospection: Fusion of Ground Penetrating Radar Depth Slices and Ground Spectral Signatures from 0.00 m to 0.60 m below Ground Surface

2019 ◽  
Vol 11 (16) ◽  
pp. 1895 ◽  
Author(s):  
Agapiou ◽  
Sarris
Keyword(s):  
Regression Model ◽  
Normal Distribution ◽  
Ground Penetrating Radar ◽  
Regression Models ◽  
Random Noise ◽  
Ground Surface ◽  
Archaeological Prospection ◽  
Spectral Signatures ◽  
Ground Penetrating ◽  
The Impact

The integration of different remote sensing datasets acquired from optical and radar sensors can improve the overall performance and detection rate for mapping sub-surface archaeological remains. However, data fusion remains a challenge for archaeological prospection studies, since remotely sensed sensors have different instrument principles, operating in different wavelengths. Recent studies have demonstrated that some fusion modelling can be achieved under ideal measurement conditions (e.g., simultaneously measurements in no hazy days) using advance regression models, like those of the nonlinear Bayesian Neural Networks. This paper aims to go a step further and investigate the impact of noise in regression models, between datasets obtained from ground-penetrating radar (GPR) and portable field spectroradiometers. Initially, the GPR measurements provided three depth slices of 20 cm thickness, starting from 0.00 m up to 0.60 m below the ground surface while ground spectral signatures acquired from the spectroradiometer were processed to calculate 13 multispectral and 53 hyperspectral indices. Then, various levels of Gaussian random noise ranging from 0.1 to 0.5 of a normal distribution, with mean 0 and variance 1, were added at both GPR and spectral signatures datasets. Afterward, Bayesian Neural Network regression fitting was applied between the radar (GPR) versus the optical (spectral signatures) datasets. Different regression model strategies were implemented and presented in the paper. The overall results show that fusion with a noise level of up to 0.2 of the normal distribution does not dramatically drop the regression model between the radar and optical datasets (compared to the non-noisy data). Finally, anomalies appearing as strong reflectors in the GPR measurements, continue to provide an obvious contrast even with noisy regression modelling.

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

BioResources ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. 2237-2257
Author(s):  
Mingkai Wang ◽  
Jian Wen ◽  
Wenbin Li
Keyword(s):  
Ground Penetrating Radar ◽  
Simulation Experiment ◽  
Simulation Software ◽  
Dielectric Constants ◽  
Three Dimensions ◽  
Coarse Roots ◽  
Coarse Root ◽  
Ground Penetrating ◽  
The Impact ◽  
Root Orientation

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.

Traveltime tomography of crosshole ground-penetrating radar based on an arctangent functional with compactness constraints

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. H1-H14 ◽  
Author(s):  
Shufan Hu ◽  
Yonghui Zhao ◽  
Tan Qin ◽  
Chunfeng Rao ◽  
Cong An
Keyword(s):  
Ground Penetrating Radar ◽  
Gradient Algorithm ◽  
Order Differential Operator ◽  
Data Set ◽  
Actual Structure ◽  
Trade Off ◽  
Traveltime Tomography ◽  
Ground Penetrating ◽  
The Impact ◽  
Low Order

Regularization has been an effective technique to provide unique and stable solutions for crosshole ground-penetrating radar (GPR) traveltime tomography. The traditional form of this method, in which a low-order differential operator was used, commonly yields a smooth solution that may not be appropriate when anomalies occur in block patterns, such as voids or irregular objects. The minimum support (MS) functional can be used to improve the resolution of blocky structures; however, in crosshole GPR traveltime tomography, the MS functional is unable to resolve residual artifacts, whose departure from an a priori model are smaller than the focusing parameter selected from a trade-off curve. In addition, it would result in severe instability and yield a trade-off curve with poorly defined corners when the focusing parameter nears the precision of the apparatus. We have developed a new stabilizing functional based on the arctangent (AT) function that effectively removes the artificially small values in the crosshole GPR traveltime tomography, and ultimately is more efficient because it does not require the user to select a focusing parameter. We inverted three 2D synthetic data sets based on the reweighted regularized conjugate gradient algorithm. Compared with the low-order differential and MS functional, the user will be able to clearly distinguish the anomaly boundary using this method, which will yield results that are closer to the actual structure. We also discussed the impact of some influencing factors caused by the noise contained in the data, the central frequency of the antenna, the anomalous trends, and the ray coverage angle. We further inverted an experimental data set to test the effectiveness and robustness of the method.

scholarly journals Performance of Soil Water Content Using Ground Penetrating Radar with Different Antenna Frequencies

2018 ◽  
Vol 7 (2.29) ◽  
pp. 815
Author(s):  
Nurul Izzati Abd Karim ◽  
Samira Albati Kamaruddin ◽  
Rozaimi Che Hasan
Keyword(s):  
Dielectric Permittivity ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Ground Penetrating Radar ◽  
Peat Soil ◽  
Relative Dielectric Permittivity ◽  
Depth Of Penetration ◽  
Engineering Research ◽  
Ground Penetrating

Accurate measurements of Soil Water Content (SWC) with applicable and relevant support are essential in many fields of earth and soil engineering research. Ground Penetrating Radar (GPR) is a geophysical tool that measures and provides accurate results for determination of the SWC. To prove the accuracy of SWC measurement using GPR, a field survey was performed in peat soil. This paper presents a fieldwork survey with the aim of assessing the SWC measurement using GPR. The survey work was conducted at Johor Bharu using different antenna frequencies (250 and 700 MHz). Five profiles, which is 5m by 5m in length, were scanned along an east-west direction with a common offset at an equal spacing of 1m.  To measure the SWC using GPR, the researchers used the velocity from the GPR’s signal from the receiving antenna to the soil. Statistical analysis was carried out based on the dielectric permittivity and SWC. Schaap’s equation and Roth’s equation were used to distinguish the relative dielectric permittivity of the soil to SWC. The results of this study show the linear function,  for the measured SWC. The validation graph shows that at a frequency of 250 MHz, the depth of penetration was greater compared to the frequency of 750 MHz. These results, suggest that a higher frequency will give higher resolution but lower depth penetration.  

scholarly journals Symmetry between Theoretical and Physical Investigation of Water Contamination Using Amplitude Variation with Offset Analysis of Ground-Penetrating Radar Data

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 991
Author(s):  
Ibrar Iqbal ◽  
Gang Tian ◽  
Zhejiang Wang ◽  
Zahid Masood ◽  
Yu Liu ◽  
...  
Keyword(s):  
Aqueous Phase ◽  
Ground Penetrating Radar ◽  
Water Contamination ◽  
Experimental Models ◽  
Theoretical Modeling ◽  
Avo Analysis ◽  
Geophysical Surveys ◽  
Phase Liquid ◽  
Ground Penetrating ◽  
The Impact

We evaluated the symmetry of theoretical and experimental analysis of water contamination such as non-aqueous phase liquid (NAPL) by using amplitude variations with offset analysis (AVO) of ground-penetrating radar (GPR) data. We used both theoretical and experimental approaches for AVO responses of GPR to small distributions of contamination. Theoretical modeling is a tool used to confirm the feasibility of geophysical surveys. Theoretical modeling of NAPL-contaminated sites containing wet sand—both with the water and light non-aqueous phase liquid—was applied by keeping in consideration the GPR AVO analysis in acquisition. Reflectivity was significantly altered with the changes in the contents of water and NAPL during modeling. The wet and dry sands introduced in our model changed two major phenomena: one, the wave pattern—implying a slight phase shift in the wave; and two, an amplitude jump with the dim reflection radar gram observed in the model. Experimental data were collected and analyzed; two observations were recorded during physical data analysis. First, relative permittivity confirmed the presence of NAPL in an experimental tank. Second, reflection patterns with jumps in amplitude and changes in polarity confirmed the theoretical investigation. Our results demonstrate that GPR AVO analysis can be as effective for detection of non-aqueous phase liquid (NAPLs) as it has been used to determine moisture contents in the past. The theoretical and experimental models were in symmetry, and both found a jump in reflection strength. The reflection pattern normally jumped with NAPL-intrusion. From the perspective of water contamination, this study emphasizes the need to take into account the impact of GPR AVO analyses along with the expert’s adaptive capacities.

Signal Stability and the Height-Correction Method for Ground-Penetrating Radar In Situ Asphalt Concrete Density Prediction

2021 ◽  
pp. 036119812110045
Author(s):  
Qingqing Cao ◽  
Imad L. Al-Qadi
Keyword(s):  
Adaptive Filter ◽  
Ground Penetrating Radar ◽  
Asphalt Concrete ◽  
Correction Method ◽  
Least Square ◽  
Least Mean Square ◽  
Signal Stability ◽  
Density Prediction ◽  
Ground Penetrating ◽  
The Impact

Ground-penetrating radar (GPR) has shown great potential for asphalt concrete density prediction used in quality control and quality assurance. One challenge of continuous GPR measurements is that the measured dielectric constant could be affected by signal stability and antenna height. This would jeopardize the accuracy of the asphalt concrete density prediction along the pavement. In this study, signal instability and shifting antenna height during continuous real-time GPR measurements were identified as main sources of error. After using a bandpass filter to preprocess the signal, a least-square adaptive filter, using gradient descent and least mean square methods, was developed to reconstruct the received signal to improve its stability. In addition, simulations were performed to evaluate the impact of geometric spreading caused by shifting antenna height during testing. A height correction was developed using a power model to correct the height-change impact. The proposed filter and height-correction method were assessed using static and dynamic tests. The least-square adaptive filter improved signal stability by 50% and the height-correction method removed the effect of shifting antenna height almost entirely.

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