Mapping the electromagnetic permittivities and thicknesses of underground layers is a vital step for proper focusing and image formation in subsurface imaging radars. Compared to traditional ground penetrating radar (GPR) that samples the scattered field along straight lines, detailed spatial information can be obtained using the concept of a newly developed subsurface multistatic synthetic aperture radar (SAR). For such system the scattered field samples are acquired along arbitrary multistatic paths. In this paper, an inversion algorithm for mapping underground layers using a multistatic subsurface SAR system is proposed. The algorithm forms a forward scattering model (FM) for the underground layers based on image theory and the generalized reflection coefficient of the layers. Subsequently, the misfit between the FM and the radar response is minimized with stochastic hill-climbing (SHC) algorithm to achieve inversion. The algorithm is tested on several full-wave simulation scenarios under noisy conditions demonstrating its potential effectiveness for mapping underground layers.

Detection and localization of buried pipelines is an important problem for oil and water distribution industries as well as for excavation and construction companies. The unique scattering mechanism exhibited by pipelines under multi-static radar configuration makes their detection and localization a challenging task. This is because the scattering phase centers (SPCs) along the pipelines change depending on the relative position between the transmitter and receiver which makes point-target based focusing algorithms such as back projection (BP) rather ineffective. To address this issue, this paper uses a special technique for determining the pipelines’ SPCs to construct a first order forward scattering model of multiple buried pipelines that considers the propagation phase and amplitude attenuation. After that, a cost function representing the misfit between the forward model and the radar response is defined. Minimization of such cost function leads to the simultaneous detection and localization of the buried pipelines. Comparison results with a basic focusing algorithm relying solely on the scattered field phase shows the superior ability of the cost function approach to resolve closely spaced buried pipelines.

Detection and localization of buried pipelines is an important problem for oil and water distribution industries as well as for excavation and construction companies. A novel 3D forward scattering model and dynamic grid search-based inversion algorithm of multiple buried pipelines using multistatic synthetic aperture radar (SAR) configuration is demonstrated in this article. Traditional imaging algorithms that focus the SAR signals by compensating only for the phase work well when the targets are far away from each other. By considering both the phase and amplitude of the scattered field and casting the inversion problem as an optimization problem, the detection and localization resolutions are improved for extended targets. A forward scattering model for multiple adjacent pipes is developed to track the scattering phase centers of each pipeline as the receiver location is changed. The model is used to calculate the resulting propagation phase and amplitude attenuation due to propagation in lossy media. The misfit between the measured data and the forward model is used as the cost function. The cost function is minimized by the dynamic grid search algorithm that starts with an initial focusing and then performs a more careful search to detect closely spaced pipelines. The forward model and inversion algorithm are validated and compared to a basic focusing algorithm using full-wave simulations as well as experimental data for both metallic as well as dielectric pipes. The results show improvement in range and cross range detection and localization resolutions over the basic SAR focusing algorithm.

A high resolution 3-D images of underground objects might be achieved using bistatic subsurface synthetic aperture radar (SAR) due the bistatic configuration's ability to view the objects from multiple angles. However, a major obstacle preventing the imaging is the dominance of the direct and ground reflection signals over the scattered signals from the buried objects. This paper presents a technique to mitigate such obstacle. By utilizing the multiple sampling points SAR provides, each adjacent sampling points are processed together as two-element array that has a null along the direction of the direct and ground reflection signals leading to significant enhancement for the scattered signals from the buried objects. The technique is explained and both simulation and measurement results for buried metallic objects are presented as a test of performance.

Multi-static subsurface synthetic aperture radar (SAR) is a promising tool capable of providing 3-D high resolution images of targets buried underground. The high detection capability of the system stems from the fact that the multi-static configuration allows for viewing the targets from many directions. However, a primal limiting factor for such configuration is the dominance of the direct and ground reflection signals over the scattered target signal hindering the system sensitivity. This letter presents a simple technique to overcome such drawback. By an appropriate utilization of SAR data, each adjacent sampling points are co-processed together as a two-element array in such a way as to have a null along the direction of the direct and ground reflection signals suppressing such signals and making the target signals more discernible. The technique is described in details and the potential problems arising from array processing are discussed. Additionally, a modified version of back projection imaging algorithm suitable for the array is provided. The performance of the proposed technique is assessed by simulation data of buried metallic spheres as well as measurement data for a buried metallic pipeline. Both simulation and measurements show a significant improvement in target detection and image quality.

Subsurface pipeline detection and localization is an important problem in gas and oil transport as well as in construction and excavation missions. A 3-D subsurface multistatic imaging radar with a novel focusing algorithm for pipelines is demonstrated in this article. The traditional back-projection (BP) algorithm, which is suitable for discrete scatterers, cannot be used for extended scatterers such as pipelines whose scattering phase centers are dependent on the relative positions of the transmitter and receiver. To circumvent this problem, a new imaging algorithm that tracks the scattering phase centers in a way that is specific to pipelines is presented and shown to provide significantly better imaging performance. An analytical solution for pipeline scattering is used to derive the algorithm, and justifications for the simplifying assumptions made are provided. The algorithm is tested by applying it to realistic lossy sand simulations data as well as experimental measurements data obtained by a portable vector network analyzer (VNA). Both simulation and measurement results demonstrate the ability of the algorithm to detect and localize metallic as well as dielectric pipes.

Oil pipeline leak detection and localization is an important problem with regard to environmental issues as well as loss of resources in oil transport systems. This article shows that a 3-D subsurface multistatic imaging radar can better detect water or oil leak when more scattered signals from the leaked region are collected from multiple directions and added coherently. However, a primal drawback in achieving detection is the dominance of the signal from the pipeline itself compared to the leak signal making direct radar-based leak detection quite challenging. By utilizing the differences in the scattering mechanisms of the pipeline and the leak, this article proposes a technique that identifies and eliminates the pipeline signal from the overall radar response, which improves leak detectability. Permittivity of oil–sand mixture is determined experimentally and fit to a semiempirical mixing formula. A realistic physics-based model is used to determine the 3-D volumetric shape of oil leak. Then, the pipe with leak is simulated in a full-wave simulator with the permittivity of the leak assigned using the mixing formula. Comprehensive simulations are carried out for oil leaks in various soil mixtures and pipe materials to test the effectiveness of the proposed approach. In addition, the proposed approach is investigated experimentally using a portable vector network analyzer (VNA) where the response of a small water leak out of metallic as well as PVC pipes is measured in bistatic settings. Both simulation and measurement results demonstrate the effectiveness of the proposed approach in detecting and localizing pipeline leaks.

Detecting and localizing pipeline leaks is a problem of importance as regards to environmental issues as well as loss of resources in water and oil transport systems. Since pipelines are extended targets and the oil or water leak is more localized, the methods for detection and localization of the pipeline itself and the leak are quite different. Another issue pertains to the fact that the scattered signal from the pipeline tends to be larger and can obscure the signal from the leak making direct radar-based leak detection quite challenging. To overcome this challenge, this paper proposes a technique to estimate the pipeline signal from the overall radar response then subtract it to make the oil leak signal easier to detect. The technique is explained and the simulation results for a realistic oil leak out of plastic (PVC) and metallic pipes buried in sand are reported and discussed to demonstrate the effectiveness of the technique.

In this letter, a hybrid analytical and numerical simulation method is presented for the time-efficient calculation of bistatic scattering from the buried targets. This computation is used for a fixed transmitter and target location but for a moving receiver to generate the data set for a 3-D synthetic aperture radar (SAR) simulation. In this method, initially, the current distribution on the target when illuminated by the transmitter (in the absence of the receiver) is calculated. Then, the receiver is used as a transmitter and its field is calculated everywhere in the lower half-space. The reciprocity is then used to find the open-circuit voltage across the receiver terminals due to the equivalent current distribution on the target. This approach substitutesNfull-wave simulations forNsampling points required in SAR imaging with two simulations leading only to a factor ofN/2reduction in the simulation time. The proposed method is validated by comparing it with the brute-force full-wave simulation method as well as by image reconstruction from the data set generated by the method.

Pipeline detection is a problem with important applications in oil and gas pipeline transport systems maintenance. The scattering mechanism for extended targets such as pipelines is different than the scattering mechanism of localized targets which poses a challenge when trying to detect and image such targets using traditional imaging schemes such as back-projection algorithm. In this paper, a novel method for detection and estimation of pipeline depth, orientation and displacement is proposed. The method utilizes the unique way a pipeline scatters incident waves to find the actual bistatic path the signal takes and compensate for such path to focus the signals gathered by a moving receiver from the pipeline. The method is explained and preliminary results are presented as a proof of concept.