Reduced order model approach for imaging with waves

We introduce a novel, computationally inexpensive approach for imaging with an active array of sensors, which probe an unknown medium with a pulse and measure the resulting waves. The imaging function uses a data driven estimate of the “internal wave” originating from the vicinity of the imaging point and propagating to the sensors through the unknown medium. We explain how this estimate can be obtained using a reduced order model (ROM) for the wave propagation. We analyze the imaging function, connect it to the time reversal process and describe how its resolution depends on the aperture of the array, the bandwidth of the probing pulse and the medium through which the waves propagate. We also show how the internal wave can be used for selective focusing of waves at points in the imaging region. This can be implemented experimentally and can be used for pixel scanning imaging. We assess the performance of the imaging methods with numerical simulations and compare them to the conventional reverse-time migration method and the “backprojection” method introduced recently as an application of the same ROM.

Angle-Weighted Reverse Time Migration With Wavefield Decomposition Based on the Optical Flow Vector

Reverse time migration (RTM) is based on the two-way wave equation, so its imaging results obtained by conventional zero-lag cross-correlation imaging conditions contain a lot of low-wavenumber noises. So far, the wavefield decomposition method based on the Poynting vector has been developed to suppress these noises; however, this method also has some problems, such as unstable calculation of the Poynting vector, low accuracy of wavefield decomposition, and poor effect of large-angle migration artifacts suppression. This article introduces the optical flow vector method to RTM to realize high-precision wavefield decomposition for both the source and receiver wavefields and obtains four directions of wavefields: up-, down-, left-, and right-going. Then, the cross-correlation imaging sections of one-way propagation components of forward- and back-propagated wavefields are optimized and stacked. On this basis, the reflection angle of each imaging point is calculated based on the optical flow vector, and an attenuation factor related to the reflection angle is introduced as the weight to generate the optimal stack images. The tests of theoretical model and field marine seismic data illustrate that compared with the conventional RTM with wavefield decomposition based on the Poynting vector, the angle-weighted RTM with wavefield decomposition based on the optical flow vector proposed in this article can achieve wavefield decomposition for both the source and receiver wavefields and calculate the reflection angle of each imaging point more accurately and stably. Moreover, the proposed method adopts angle weighting processing, which can further eliminate large-angle migration artifacts and effectively improve the imaging accuracy of RTM.

Spherical Aberration-Corrected Metalens for Polarization Multiplexed Imaging

We present a terahertz spherical aberration-corrected metalens that uses the dynamic phase to achieve polarization multiplexed imaging. The designed metalens has polarization–dependent imaging efficiencies and polarization extinction ratios that exceed 50% and 10:1, respectively. Furthermore, opposite gradient phases can be applied to orthogonal polarizations to shift the imaging of the two polarized sources in the longitudinal and transverse directions. Indeed, we find that the metalens has a smaller depth-of-focus than a traditional metalens when imaging point sources with limited objective lengths. These results provide a new approach for achieving multifunctional beam steering, tomographic imaging and chiroptical detection.

3D Point Cloud Reconstruction Using Inversely Mapping and Voting from Single Pass CSAR Images

3D reconstruction has raised much interest in the field of CSAR. However, three dimensional imaging results with single pass CSAR data reveals that the 3D resolution of the system is poor for anisotropic scatterers. According to the imaging mechanism of CSAR, different targets located on the same iso-range line in the zero doppler plane fall into the same cell while for the same target point, imaging point will fall into the different positions at different aspect angles. In this paper, we proposed a method for 3D point cloud reconstruction using projections on 2D sub-aperture images. The target and background in the sub-aperture images are separated and binarized. For a projection point of target, given a series of offsets, the projection point will be mapped inversely to the 3D mesh along the iso-range line. We can obtain candidate points of the target. The intersection of iso-range lines can be regarded as voting process. For a candidate, the more times of intersection, the higher the number of votes, and the candidate point will be reserved. This fully excavates the information contained in the angle dimension of CSAR. The proposed approach is verified by the Gotcha Volumetric SAR Data Set.

A compressed data approach for image-domain least-squares migration

We consider the problem of image-domain least-squares migration based on efficiently constructing the Hessian matrix with sparse beam data. Specifically, we use the ultra-wide-band phase space beam summation method, where beams are used as local basis functions to represent scattered data collected at the surface. The beam domain data are sparse. One can identify seismic events with significant contributions so that only beams with non-negligible amplitudes need to be used to image the subsurface. In addition, due to the beams' spectral localization, only beams that pass near an imaging point need to be taken into account. These two properties reduce the computational complexity of computing the Hessian matrix - an essential ingredient for least-squares migration. As a result, we can efficiently construct the Hessian matrix based on analyzing the sparse beam domain data.

RAY-BINNING ANGLE STACK DOMAIN IN ENHANCING THE ROBUSTNESS OF CONVERTED-WAVE SEISMIC JOINT INVERSION

Converted-Wave Seismic has been proven as imaging alternative in aiding conventional seismic data when passing through gas cloud accumulation. However, asymmetrical approximation effect during Converted-Wave Seismic binning still remains in offset domain. PS-Reflection events in offset-domain are mapped using common-ray re-sorting technique by implementing the basic Snells Law of Mode Conversion. This produces an Angle Profile correspondent with the PP incident angle. Re-sorting the angle of converted-wave ray path to the PP-Wave propagation within the common imaging point, the Converted-Wave seismic would share similar angle range. Thus, improving the match in PP to PS event as data input preparation for Joint Inversion. Grouping the angle based on AVA Analysis, followed by stacking the Angle Profile into Common-Ray Partial Angle Stack, had proven to eliminate the fault shadow sagging zone and gas absorption illumination area in Converted-Wave Seismic. The final result of PP-Seismic imaging is more coherent with the Converted-Wave Seismic, in term of event alignment and amplitude character. This result lead to more robust PP-PS Joint Inversion, as the coherency between input data is an important key in simultaneous process. The comparison on derived Vp/Vs shows better improvements of subsurface imaging, especially in the near-surface gas masking area of conventional seismic.