We propose an efficient iterative curvelet-regularized deconvolution algorithm that exploits continuity along reflectors in seismic images. Curvelets are a new multiscale transform that provides sparse representations for images (such as seismic images) that comprise smooth objects separated by piece-wise smooth discontinuities. Our technique combines conjugate gradient-based convolution operat...

We apply a nonlocal adaptive spectral transform for sparse modeling of phase and amplitude of a coherent wave field. The reconstruction of this wave field from complex-valued Gaussian noisy observations is considered. The problem is formulated as a multiobjective constrained optimization. The developed iterative algorithm decouples the inversion of the forward propagation operator and the filte...

Compressive sensing is a methodology for the reconstruction of sparse or compressible signals using far fewer samples than required by the Nyquist criterion. However, many of the results in compressive sensing concern random sampling matrices such as Gaussian and Bernoulli matrices. In common physically feasible signal acquisition and reconstruction scenarios such as superresolution of images, ...

We present a scheme for recovering the complex input field launched into a waveguide array, from partial measurements of its output intensity, given advance knowledge that the input is sparse. In spite of the fact that in general the inversion problem is ill-conditioned, we demonstrate experimentally and in simulations that the prior knowledge of sparsity helps overcome the loss of information....

We investigate the Gaussian beam migration of commonshot and common-receiver data. The imaging of commonshot data is useful for seismic data where the receiver coordinates are well sampled, but the source coordinates are less well sampled. By reciprocity, this approach can also be applied to common-receiver data, such as from OBS experiments where the source locations are dense but the receiver...

Deep-learning has achieved good performance and demonstrated great potential for solving forward inverse problems. In this work, two categories of innovative deep-learning-based modeling methods are proposed compared. The first category is deep-learning surrogate-based inversion methods, in which the Theory-guided Neural Network (TgNN) constructed as a surrogate problems with uncertain model pa...

Magnetization vector inversion has been developed since it can increase accuracy due to the unknown magnetization direction caused by remanence. However, three components of total magnetizations are simultaneously inverted and then synthesized into magnitude direction, which increases inherent non-uniqueness inversion. The positions originally consistent. If there is a lack constraints between ...

BIKE is a Key Encapsulation Mechanism selected as an alternate candidate in NIST’s PQC standardization process, which performance plays significant role the third round. This paper presents FPGA implementations of with best area-time reported literature. We optimize two key arithmetic operations, are sparse polynomial multiplication and inversion. Our multiplier achieves time-constancy for poly...

Sparse coding is an unsupervised method which learns a set of over-complete bases to represent data such as image and video. Sparse coding has increasing attraction for image classification applications in recent years. But in the cases where we have some similar images from different classes, such as face recognition applications, different images may be classified into the same class, and hen...

We present one of the first attempt at implementing a massively parallel frequency-domain full-waveform inversion algorithm for imaging 3D acoustic media. The inverse method is based on a classic steepest-descent algorithm. The algorithm was designed so that one or several frequencies are inverted at a time. Wave propagation modeling, a key component of the inversion algorithm, is performed wit...