Inversion Of Radar Data Research Articles

Analysis of the resolution of interferometric synthetic aperture radar data inversion and application of the inversion residual to identify shallow hazards

We have conducted a study to investigate the sensitivity of interferometric synthetic aperture radar (InSAR) measurement to subsurface strain change and developed an inversion framework to use InSAR data for subsurface surveillance. Using the spatial domain and spatial-frequency-domain analysis, we have determined that the earth behaves as a low-pass filter in its transmission of deformation from the subsurface to the earth’s surface. As a result of this low-pass filtering effect of the earth, the horizontal resolution of InSAR images is roughly equal to the depth of the targeted activity. Therefore, the goal of InSAR data inversion is to recover high-spatial-frequency subsurface strains from InSAR measurements. Because changes in the reservoir tend to have a lower spatial-frequency InSAR signature, high-spatial-frequency surface displacement can be associated with shallow overburden activities. Based on these insights, we have developed an inversion workflow that takes into account the overburden and reservoir strain changes and applied it to an InSAR data set from the San Joaquin Valley. Our results indicate that an inversion model that only considers strain changes in the reservoir produces large spatially localized inversion residuals in locations with known shallow overburden activity. A residual analysis reveals that the high-spatial-frequency anomalies in InSAR data can be used to identify shallow activities.

Semisupervised Deep Neural Network-Based Cross-Frequency Ground-Penetrating Radar Data Inversion

Semisupervised Deep Neural Network-Based Cross-Frequency Ground-Penetrating Radar Data Inversion

GPRI2Net: A Deep-Neural-Network-Based Ground Penetrating Radar Data Inversion and Object Identification Framework for Consecutive and Long Survey Lines

Ground penetrating radar (GPR) enables infrastructure inspection using consecutive and long survey lines. However, the existing GPR data processing methods may lead to distortions or dislocations in the reconstructed shapes of the detected objects or even inconsistencies of the inverted dielectric values when performing inversion or object identification directly using GPR data obtained from the consecutive and long survey lines. To overcome these issues, this study proposed a novel deep neural network (DNN) architecture named GPRI2Net to simultaneously reconstruct the permittivity maps and categorize the object class labels from the GPR data of consecutive and long survey lines. GPRI2Net combined a convolutional neural network (CNN) based on DenseUnet and a recurrent neural network (RNN) based on bidirectional convolutional long short-term memory (Bi-ConvLSTM) to exploit the contextual information in and between the B-Scan segments extracted from the GPR data of a consecutive and long survey line. In addition, GPRI2Net performed inversion and object identification simultaneously using one network, which highly shared features between the two tasks and greatly reduced the computational complexity. Validation experiments were performed at two levels: first, using synthetic data based on the tunnel liner defects model and then using a sandbox model test in a realistic scenario. The results demonstrated that GPRI2Net can reconstruct consecutive permittivity maps and categorize the object class labels from GPR data with different dominant frequencies and survey line lengths and achieved a superior performance using the synthetic data compared to several other methods. Moreover, GPRI2Net also achieved satisfactory results using real-world GPR data.

Impact of Radar Data Sampling on the Accuracy of Atmospheric Refractivity Inversions Over Marine Surfaces

AbstractThe turbulent nature of the marine atmospheric boundary layer and interactions across the air‐sea interface cause ever‐changing environmental conditions, including atmospheric properties that affect the index of refraction, or atmospheric refractivity. Variations in atmospheric refractivity lead to many types of anomalous propagation phenomena of electromagnetic (EM) signals; thus, improving performance of EM systems requires in situ knowledge of the refractivity. Inversion approaches to estimate refractivity rely on measured EM data; however, despite its importance, few studies have examined the influence of data density on the accuracy of refractivity inversions. This study applies a bistatic radar data inversion process to estimate atmospheric refractivity parameters in evaporative ducting conditions and examines the impacts of the sampling density of radar propagation loss data, and its source location, on accuracy of refractivity inversions. Genetic algorithms and a radar propagation model are used to perform the inversions. Numerical experiments examine various randomly distributed amounts of synthetic data from a 100‐m (altitude) by 60‐km (range) area. Three domains within this area are examined from which data were sourced. A data density of approximately 1% of the prediction domain yielded the smallest errors of refractivity parameters, and root mean square errors of refractivity and propagation loss. These error reductions are attributed to avoidance of nonunique solutions that likely impact lower data densities, supported by their classification as a misleading or difficult inverse problem. Generally, data sourced from long range result in lower refractivity and propagation loss root mean square errors compared to data sourced from other domains.

UWB Vivaldi Antenna Array Lower Band Improvement for Ground Penetrating Radar Applications

This paper concerns a ground penetrating radar system (GPR) presenting beam forming ability. This ability is due to a great flexibility in the emission of wavefronts. The innovative concept is to use an array of antennas which can reconfigure itself dynamically, in order to focus on a desired target. This antennas system can act as a new microwave sensor to detect and characterize buried targets in an inhomogeneous medium which is the case study in various application fields such as geophysics, medical, planetology,... Its electronics are in development with the DORT (Time reversal technique) method integration for optimizing the localization of buried target. This paper aims are to present the antenna optimization used in the GPR applications. Typical antennas used in GPR are generally Vivaldi ones directly on the ground. Especially, in the context of the space mission ExoMars 2020, the radar antenna is set on a mobile station at a distance of about 30 cm from the ground to avoid any contact. However, they are limited by their important size, due to the lowest frequency of their bandwidth. Results of this work concern an increase of the antenna bandwidth by shifting the lower-band limit, making it a UWB type [500 MHz–4 GHz] without changing its size. As low frequency waves can spread deeper into probed medium, this optimization can improve the radar data inversion performances.

Forest classification and impact of BIOMASS resolution on forest area and aboveground biomass estimation

The European Space Agency (ESA) is currently implementing the BIOMASS mission as 7th Earth Explorer satellite. BIOMASS will provide for the first time global forest aboveground biomass estimates based on P-band synthetic aperture radar (SAR) imagery. This paper addresses an often overlooked element of the data processing chain required to ensure reliable and accurate forest biomass estimates: accurate identification of forest areas ahead of the inversion of radar data into forest biomass estimates.The use of the P-band data from BIOMASS itself for the classification into forest and non-forest land cover types is assessed in this paper. For airborne data in tropical, hemi-boreal and boreal forests we demonstrate that classification accuracies from 90 up to 97% can be achieved using radar backscatter and phase information. However, spaceborne data will have a lower resolution and higher noise level compared to airborne data and a higher probability of mixed pixels containing multiple land cover types. Therefore, airborne data was reduced to 50m, 100m and 200m resolution. The analysis revealed that about 50–60% of the area within the resolution level must be covered by forest to classify a pixel with higher probability as forest compared to non-forest. This results in forest omission and commission leading to similar forest area estimation over all resolutions. However, the forest omission resulted in a biased underestimated biomass, which was not equaled by the forest commission. The results underline the necessity of a highly accurate pre-classification of SAR data for an accurate unbiased aboveground biomass estimation.

1D-FDTD Characterization of Ionosphere Influence on Ground Penetrating Radar Data Inversion

A finite-difference time-domain (FDTD) simulator is employed to evaluate distortions induced by the ionosphere on chirp signals transmitted by orbiting ground penetrating radars like the Mars advanced radar for subsurface and ionosphere sounding (MARSIS) and the shallow radar (SHARAD). The main goal is to support the data inversion process, i.e., the estimation of the permittivity of subsurface layers. Also proposed is an approach to study the influence of the ionosphere on chirp signals for present and future missions. Since data inversion relies on both crust attenuation estimation and rough geometries compensation, procedures that could be both strongly influenced by the ionosphere, several ionosphere compensation schemes are compared and their impact on data inversion is discussed.

Top layers charaterization of the Martian surface: Permittivity estimation based on geomorphology analysis

Mars Express spacecraft inserted successfully Martian orbit at the end of 2003. On board this probe, a radar instrument called MARSIS (for Mars Advanced Radar for Surface and Ionosphere Sounding) is looking for water inside the first kilometers of Martian crust. To support MARSIS planning and data inversion, Laboratoire de Planétologie de Grenoble developed a MARSIS signal simulator. We show in this paper that MARSIS can also characterize some surface features, in addition to subsurface water and ionosphere sounding. We study a Martian surface region of special interest: Nilokeras Mensae, inside Acidalia Planitia. We discuss the previous geological studies of this region, and show the geomorphologies analyze of this surface area could lead to a simple terrain model. Then, we present a possible data inversion scheme and applying the MARSIS simulator, we test a first radar data inversion. Finally, we will show that complete dielectric characteristics of surface top layers can be retrieved, at least as often Mars Express flies over some layered terrain (at wavelength scale).

Modeling of radiowave scattering in the melting layer of precipitation

In this paper, a new melting layer model called melting layer with spheroidal particles (CFPS in French) is presented. The physical description of the melting layer and the method of scattering calculation used in this model are developed. Two important contributions are the nonmonotonous law for the axial ratio of melting particles and the scattering calculation, which is made by an exact method for a spheroidal particle with any size and any axial ratio. The CFPS model is validated with radar data at 3 and 35 GHz. Results of validation are shown in this paper. Two applications are discussed: the comparison between three other melting layer models (developed by the European Space Agency, Delft University, and Helsinki University) that run faster in time but involve limitations in particle size or particle shape and a radar data inversion process in order to better characterize the melting layer and to retrieve meteorological parameters.

Dielectric constant as a predictor of porosity in dry volcanic rocks

Measurements of dielectric constant ( K′) are made on 34 samples of volcanic rocks at frequencies of 0.01 to 10 MHz under ambient atmospheric conditions. Bulk density ( ρ T), total porosity ( Φ T) and connected porosity ( Φ Conn) are also measured. The samples derive from two dacitic lava flows (∼60–62 and 68 wt.% SiO 2), dacitic pyroclastic deposits (∼66–68 wt.% SiO 2) and two basalt lava flows (∼49–52 wt.% SiO 2). Each locality provided a suite of samples with similar mineralogy and composition but a range of porosities. Porosity measurements indicate that as much as 17% of pumice pore space can be unconnected. The data show a strong correlation between K′ and Φ T and the dacitic rocks show a 2.5-fold decrease in K′ over a porosity range of 8–79%. The data are fitted to a time propagation (TP) model and to a more general two-parameter model based on the Lichtenecker–Rother equation. For dacitic rocks, the dielectric constant is best related to porosity by: (K′) 0.96=Φ+6.51(1−Φ). K′ and ρ T are also strongly correlated in these sample sets. The trend formed by samples of dacite in ( K′, ρ T) space is linear and the data compare well with published values for other non-basaltic rocks. Samples of basalt show greater variance in measured values of K′, due perhaps to higher and more variable modes of Fe–Ti oxide minerals. These new data suggest the possibility of inverting radar velocity data to obtain estimates of porosity in dry volcanic successions. Inversion of radar data for porosity could be useful in discriminating between units of an eruption cycle (e.g., lava flow, pyroclastic flow, airfall) and mapping porosity variations within deposits such as welded pyroclastic flows.