In this article, we present an adaptive subaperture integration method for wide-angle synthetic aperture radar (SAR) for improved imaging, with emphasis on short-to-medium range applications. In order to avoid full-aperture integration, traditional approaches use fixed-width subapertures, which may not conform to the persistence angle of the scatterers. Coherent integration gains over the aperture are possible if integration is carried out over the persistence angle of the scatterers, because integrating shorter than the persistence angle may spread the scattering response across multiple subapertures or, conversely, integrating more than the persistence angle may cause noise accumulation along with the useful signal. In this article, we propose to use change-point detection methods to estimate the persistence widths of the scatterers, and consequently enhance the coherent integration gains, resulting in improved imaging. We compare our proposed methods with the standard integration approaches as well as a recently proposed adaptive integration approach. We provide qualitative and quantitative analyses to prove that our proposed methods outperform the existing approaches. We present experimental results on the real-data of our low-terahertz radar as well as a publicly available dataset to validate our claims.

In this article, we propose a novel concept of cross-learning in order to improve synthetic aperture radar (SAR) images by learning from the camera images, in the manifold domain. We present multilevel abstraction approaches to materialize knowledge transfer between these two very different modalities (i.e., the radar and the camera), namely, a canonical correlation analysis-based approach and a manifold alignment-based approach. We provide experimental results on real data, along with qualitative as well as quantitative analyses, to validate the proposed methodologies.

In this paper, we extend the forward-scanning synthetic aperture radar (SAR) methodology to reconstruct images of the moving targets, for a forward-looking automotive radar. We adapt a matrix decomposition approach to forward-scanning SAR in order to separate moving targets from clutter/stationary objects. To solve our optimization problem, we propose an iterative solution based on augmented Lagrangian method. Image focusing, over the synthetic aperture, is achieved through spatial segmentation and cross-correlation maximization. Our proposed method results in well-focused imaging of the moving targets with enhanced angular resolution. Experimental results from simulation as well as real-data corroborate our proposed methodology.

In this paper, we propose an adaptive sub-aperture integration approach for wide-angle synthetic aperture radar (SAR) with emphasis on the automotive applications. Traditional SAR integration approaches use fixed-width sub-apertures. These approaches suffer from the fact that scatterers may have different persistence widths over the angular aperture and can result in spreading the response across multiple sub-apertures or, conversely, coherently integrate extra noise with the signal. Our proposed method employs change-point detection to identify the persistence widths of the scatterers and consequently increases the coherent integration gain. Experimental results on two datasets validate our proposed methodology.

In this paper we present data from a new low THz radar. We show that the resolution of the raw data is limited by range independent phase error which is generated by multiple reflections in the receiver cable. The transmitted waveform is found to be extremely linear. The phase gradient algorithm is used to remove the phase error. The resolution of the corrected data is illustrated by imaging a metal trolley to reveal features in a cm scale.

We have examined the idea that a towed neutrally buoyant electromagnetic (EM) streamer suffers from noise induced according to Faraday’s law of induction. A simple analysis of a horizontal streamer in a constant uniform magnetic field determined that there was no induction noise. We have developed an experiment to measure the induced noise in a prototype EM streamer suspended in the Edinburgh FloWave tank, and we subjected it to water flow along its length and to waves propagating in the same direction, at 45° and 90° to the streamer direction. The noise level was found to increase with increasing flow velocity. The motion of the prototype EM streamer in response to parallel constant current flow and wave motion was found to generate significant noise. The main finding is that wave motion was the major source of noise and was much larger than the noise of a static cable. The noise level can probably be reduced by towing the cable deeper and increasing the cable tension.

Summary We present results of tests conducted using a prototype EM streamer section in a wave tank to investigate sources of noise associated with towing an EM streamer. Flow was run at velocities of 0–1.5m/s parallel to the streamer suspended 1m below the water surface. Waves were also generated at different heights, frequencies and in different directions relative to the streamer. Motion capture cameras were used to record the motion of the streamer at 13 points. The results show conclusively that the dominant electric field noise in a the streamer is due to the motion of the streamer within the earth’s magnetic field.

Summary We present results of tests conducted in February 2015 on a prototype EM streamer section in a wave tank to investigate sources of noise associated with towing an EM streamer. Flow was run at velocities of 0-1.5m/s parallel to the streamer suspended 1m below the water surface. Waves were also generated at different heights, frequencies and in different directions relative to the streamer.

The marine controlled source EM surveying method has become an accepted tool for deep water exploration for oil and gas reserves. In shallow water (<500 m) data are complicated by the signal which interacts with the water-air interface which can dominate the response at the receiver. By decomposing the 1-D response to an impulsive current dipole source in the time domain and frequency domain! separate the response into: (1) an earth response, (2) a direct arrival, (3) a coupled airwave which travels through the air and (4) a surface coupling term which travels through the earth. The last two terms are coupled to the sea surface as well as to the earth resistivity structure but one travels through the air between source and receiver and the other only through the earth. Using a range of simple models I quantify the effect of these four terms in the time domain and the frequency domain. The results show that in shallow water the total response is significantly larger than in very deep water and that a large part of this extra energy comes from surface coupling, which is reflected at the sea surface and does not propagate through the air but through the earth. As a result, this term is highly sensitive to the resistivity of the earth. This means that the sea surface in shallow water not only significantly increases the signal strength of CSEM data but also enhances the sensitivity to subsurface resistivity structure. Compared with the surface coupling term, the coupled part of the airwave contains very little information about the earth, and is limited to the near surface.Time domain separation of the airwave from the surface coupling response results in greater sensitivity to a deep resistive target than frequency domain separation although there is also reasonable sensitivity in the frequency domain. (C) 2015 Elsevier B.V. All rights reserved.

Controlled source EM (CSEM) has become a standard tool in exploration for hydrocarbons over the last 10 years. Various approaches exist including static autonomous node receivers (Constable u0026 Srnka, 2007), static ocean bottom cable (OBC) (Ziolkowski et al., 2010) and static vertical seafloor dipoles (Holten et al., 2009). The source is generally towed over the stationary receivers. This approach enables data with the lowest possible noise to be acquired but greatly reduces the acquisition efficiency when compared to that of 2D seismic. An obvious improvement could be made by towing an EM streamer with the source as proposed by (Anderson u0026 Mattsson, 2010). However, a towed system generates more noise at the electric dipole receivers than a static receiver. Through controlled tests in a wave tank we have identified the nature and relative contribution of different sources of noise

We created a workflow to predict controlled-source electromagnetic (CSEM) responses from seismic velocities and compared the predicted responses with CSEM data. The first step was to calculate a resistivity model from seismic velocities in a Bayesian framework to account for the uncertainties. The second step was to estimate the electric anisotropy and improve the resistivity model for the depths at which there was no well control. The last step was to use this updated resistivity model to forward-model CSEM responses and compare the result with CSEM data. The comparison with real data revealed that the measured CSEM responses were generally within plus and minus one standard deviation of the predicted responses. This workflow was able to predict CSEM responses, which can prove very useful for feasibility studies before acquisition and interpretation after acquisition of CSEM data.

Summary Towed-streamer receivers are in use for efficient acquisition of controlled source electromagnetic (CSEM) data. We investigate the contribution to the noise made by voltages induced, according to Faraday’s law of induction, in the telluric cables moving in the Earth’s magnetic field. We consider the Earth’s magnetic field to be spatially-invariant over the length of the receiver cable, and consider it to be either constant, or time variant. If the cable is straight behind the vessel, following the ship’s track, there is no induced voltage. If there are cross-currents and if the Earth’s magnetic field is time variant, there can be induced voltages which increase which the feathering angle, the length of the electric dipole receiver, and the magnitude of the rate of change of the magnetic field. Using a maximum feathering angle of 10° and the dipole length of 1100 m, the estimated noise is about two orders of magnitude less than the measured noise. We conclude that this is not the main source of noise. There must be some other mechanism causing the dominant component of the noise.

The conventional approach is to perform iterative forward modelling, or inversion. Synthetic data are created using the data acquisition configuration and a subsurface resistivity model. The model is adjusted until the synthetic data fit the measured data. However, there are many different models that fit the data equally well and it is a problem to select the range of most likely models. Constraints are required. Seismic data yield complementary information, which can constrain the range of possible resistivity models that fit the data.

We developed a methodology to estimate resistivities from seismic velocities. We applied known methods, including rock physics, depth trends, structural information, and uncertainty analysis. The result is the range of background resistivity models that is consistent with the known seismic velocities. We successfully tested the methodology with real data from the North Sea. These 2D or 3D background resistivity models yield a detailed insight into the background resistivity, and they are a powerful tool for feasibility studies. They could also serve as starting models or constraints in (iterative) forward modeling of electromagnetic data for the determination of subsurface resistivities.

In this article, we show that the controlled source electromagnetic (CSEM) method is complementary to the seismic method.

1053-5888/12/$31.00©2012IEEE I n this article, we show that the controlled source electromagnetic (CSEM) method is complementary to the seismic method. Land and marine CSEM methods have developed almost independently. Both methods are discussed, but the focus is more on the application of the marine CSEM method, since this has had the most attention in the past decade. Active methods using man-made EM sources are used to investigate subsurface reservoirs and, in principle, are able to distinguish between those that are saturated with electrically resistive hydrocarbons and those that are saturated with electrically conductive brine. Therefore, they have the potential to rank the prospectivity of structures known from seismic data, but before drilling. Novel techniques for the processing of marine CSEM data include removal of the airwave that travels through the air at the speed of light and the suppression of magnetotelluric (MT) noise. For transient pseudorandom binary sequence (PRBS) data, deconvolution is an important part of signal-to-noise ratio enhancement. EM data have much lower resolution than seismic data and therefore need to use the subsurface structure obtained from seismic data plus rock physics relations to constrain resistivities in starting models for inversion.

Joint analysis of seismic and electromagnetic data is difficult because the data sets lack a common physical parameter, and rock physics is usually applied to link the two methods via porosity. However, rock physics parameters are not well known in near field exploration, and estimates are likely to have large errors associated with them. In this work, we use the Gassmann equation to link velocity to porosity, and the self-similar model to link porosity to resistivity. We calculate a simple depth-trend from the data, and estimate the uncertainty of our model. We apply our methodology to well logs from the North Sea. We show that the background resistivities of a field can be modelled by (1) calibrating the rock physics model on a well log from an adjacent field (including a depth-trend), and (2) calculating the corresponding uncertainty. This method is a useful tool for joint analysis of seismic and electromagnetic data.

The water layer above the source and receiver in active source marine EM surveying is known to affect the measured response with a significant amount of energy travelling from the source to the receiver through the air. This has the effect of reducing the sensitivity to resistivity variations in the subsurface. This is especially true in shallow water. The use of a transient source waveform allows for a degree of temporal separation of the water layer and subsurface responses; this separation increases for decreasing water depth and decreasing subsurface resistivity. In the frequency domain no such separation exists and the airwave is only suppressed by increasing water depth.