Preliminary investigation on the feasibility of radiometric techniques to detect faults in buried cable joints

This paper investigates the preliminary use of radiometric techniques to the detection of PDs in buried cables, and in particular to cable joints. The transfer function from the source to the detector is a function of the propagation characteristics of the media involved. In the case of radiometric detection the inclusion of soil, in general a lossy and dispersive medium with frequency and content dependent characteristics, further contributes to signal attenuation. The work undertaken here examines whether a repetitive pulse of varying amplitude and frequency, injected into an experimental arrangement that simulates buried power cables, is being detected by two simple antennae above ground. Successful detection of the pulses showed the preliminary possibility of the use of such techniques in PD detection, which creates the need for further experiments and antenna designs to be explored

Signal Superposition Model with Mineralogy Based Spectroscopic Dielectric Modelin Wireless Underground Sensor Networks

The propagation of EM waves in soil is defined by permittivity and permeability which are in turn affected by the soil parameters such as soil moisture and texture. Therefore, a suitable Dielectric Model like MBSDM is required for the channel characterization of WUSN. Effect of soil parameters and environmental conditions on signal propagation is modelled using Superposition Model. The simulation of these stages is done in MATLAB for UG-UG, UG-AG and AG-UG scenarios. The system is further implemented on the ZYNQ ZC-702 hardware platform

Moisture content investigation in the soil samples using microwave dielectric constant measurement method

The microwaves of typical frequency ranges of 3 GHz to 30 GHz have been in use for remote sensing applications which are progressing rapidly. The microwaves can sense existing moisture in any material that absorbs moisture such as soil or vegetation. In case of soils which may be comprised of variable mix proportionate of solids, liquids or gases and distinct textures subjected to the associated size and the arrangements of soil particles. Hence, the moisture absorption by a specific type of soil used to be different. The inherent physical and electrical properties such as color, texture, grains, dielectric constant, conductivity or permeability, etc. differentiate various soils. In this work, authors present soil moisture measurement by simple estimation of emissivity i.e. the ratio of energy radiated by an object to absorbing the body of same physical temperature. A strategic method of measuring dielectric constant using a microwave signal is used in this research work. The measurement of the dielectric constant of the soils collected from the specific regions and analysis of results has been reported. The proposed method is less complex and can further be used for the identification of soil moisture and agricultural applications

Signal Superposition Model with Mineralogy Based Spectroscopic Dielectric Modelin Wireless Underground Sensor Networks

The propagation of EM waves in soil is defined by permittivity and permeability which are in turn affected by the soil parameters such as soil moisture and texture. Therefore, a suitable Dielectric Model like MBSDM is required for the channel characterization of WUSN. Effect of soil parameters and environmental conditions on signal propagation is modelled using Superposition Model. The simulation of these stages is done in MATLAB for UG-UG, UG-AG and AG-UG scenarios. The system is further implemented on the ZYNQ ZC-702 hardware platform

A Deep Learning-Based GPR Forward Solver for Predicting B-Scans of Subsurface Objects

The forward full-wave modeling of ground-penetrating radar (GPR) facilitates the understanding and interpretation of GPR data. Traditional forward solvers require excessive computational resources, especially when their repetitive executions are needed in signal processing and/or machine learning algorithms for GPR data inversion. To alleviate the computational burden, a deep learning-based 2D GPR forward solver is proposed to predict the GPR B-scans of subsurface objects buried in the heterogeneous soil. The proposed solver is constructed as a bimodal encoder-decoder neural network. Two encoders followed by an adaptive feature fusion module are designed to extract informative features from the subsurface permittivity and conductivity maps. The decoder subsequently constructs the B-scans from the fused feature representations. To enhance the network's generalization capability, transfer learning is employed to fine-tune the network for new scenarios vastly different from those in training set. Numerical results show that the proposed solver achieves a mean relative error of 1.28%. For predicting the B-scan of one subsurface object, the proposed solver requires 12 milliseconds, which is 22,500x less than the time required by a classical physics-based solver

Investigation of dielectric constant variations for Malaysians soil species towards its natural background dose

The correlation of natural background gamma radiation and real part of the complex relative permittivity (dielectric constant) for various species Malaysian soils was investigated in this research. The sampling sites were chosen randomly according to soils groups that consist of sedentary, alluvial and miscellaneous soil which covered the area of Batu Pahat, Kluang and Johor Bahru, Johor state of Malaysia. There are 11 types of Malaysian soil species that have been studied; namely Peat, Linau-Sedu, Selangor-Kangkong, Kranji, Telemong- Akob-Local Alluvium, Holyrood-Lunas, Batu Anam-Melaka- Tavy, Harimau Tampoi, Kulai- Yong Peng, Rengam-Jerangau, and Steepland soils. In-situ exposure rates of each soil species were measured by using portable gamma survey meter and ex-situ analysis of real part of relative permittivity was performed by using DAK (Dielectric Assessment Kit assist by network analyser). Results revealed that the highest and the lowest background dose rate were 94 ±26.28 μR hr-1 and 7 ±0.67 μR hr-1 contributed by Rengam Jerangau and Peat soil species respectively. Meanwhile, dielectric constant measurement, it was performed in the range of frequency between 100 MHz to 3 GHz. The measurements of each soils species dielectric constant are in the range of 1 to 3. At the lower frequencies in the range of 100 MHz to 600 MHz, it was observed that the dielectric constant for each soil species fluctuated and inconsistent. But it remained consistent in plateau form of signal at higher frequency at range above 600 MHz. From the comparison of dielectric properties of each soil at above 600 MHz of frequency, it was found that Rengam-Jerangau soil species give the highest reading and followed by Selangor-Kangkong species. The average dielectric measurement for both Selangor-Kangkong and Rengam-Jerangau soil species are 2.34 and 2.35 respectively. Meanwhile, peat soil species exhibits the lowest dielectric measurement of 1.83. It can be clearly seen that the pattern of dielectric measurement for every soil at the frequency above 600 MHz demonstrated a specific distribution which can be classified into two main regions which are higher and lower between the ranges of 1.83 to 2.35. Pearson correlation analysis between the frequency of 100 MHz and 2.6 GHz with respect to exposure rate for every soil species was r = 0.38 and r = 0.51, respectively. This indicates that there was no strong correlation between both parameter, natural background dose and soils dielectric for each soils sample. This factor could be contributed by major and minor elements contained in each soils sample species, especially Ferum, Fe and Silica, Si

A new approach to estimating the path loss in underground wireless sensor networks

Unlike terrestrial Wireless Sensor Networks (WSNs), communication between buried nodes in WUSNs happens through the ground. Due to the complexity of soil, accurate estimation of the underground signal attenuation is challenging. Existing path loss models mainly rely on semi-empirical and empirical mixing models for calculating the dielectric properties of the soil. In this paper, two existing models for estimating the path loss in soil (i.e., the CRIM-Fresnel and Modified-Friis models) are compared with measurements obtained at three locations. In addition, an improved method is proposed for estimating the path loss based on a new approach for calculating the dielectric properties of soil from Time Domain Reflectometry (TDR) measurements. The proposed approach calculates the complex permittivity values from TDR waveform based on a new modified method and subsequently use them as inputs into the Modified-Friis model. The results from the field trials were compared with the proposed method and the existing models. The results of this comparison showed that the proposed estimation technique provides a better estimation of Radio Frequency (RF) attenuation than the existing models. It also eliminates the need to take samples back to the laboratory by providing in situ calculation of attenuation based on TDR

Controlled laboratory test for the investigation of LNAPL contamination using a 2.0 GHz ground penetrating radar

Groundwater is an important source of fresh water and, consequently, its quality should be properly monitored. Different contaminants can be identified with different types of equipment and/or measurement procedures. Fuel oil contamination forms a "floating" layer over the water table, which has different electrical properties, therefore electromagnetic techniques can be used to image such contaminants. This paper presents a scale-laboratory test where a 2.0 GHz ground penetrating radar (GPR) is used to assess a controlled-fuel oil injection in a shallow sand tank setup. The test examined several scenarios involving different levels of water saturation and fuel oil contamination. The increase of water content produces a reduction of EM wave propagation velocity, moving some fixed/reference targets to higher reflection times. We use simplified relations to obtain approximated dielectric permittivity values, where the inverted results are consistent with those available in the literature for similar scenarios. Rather than suggesting a true quantitative procedure, these observations could be exploited in a qualitative long-term monitoring strategy in common field situations where a contaminant enters a soil matrix and moves through its pore spaces. Finally, the integration of GPR measurements with other monitoring techniques could increase the reliability of the interpretation and the sensitivity to the contaminant concentration