Recently, a new approach to multiple removal has been introduced: estimation of primaries by sparse inversion (EPSI). Although based on the same relationship between primaries and multiples as surface-related multiple elimination (SRME), it involves quite a different process: instead of prediction and subtraction of multiples, in EPSI the unknown primaries are the parameters of a large-scale in...

Continuity along reflectors in seismic images is used via Curvelet representation to stabilize the convolution operator inversion. The Curvelet transform is a new multiscale transform that provides sparse representations for images that comprise smooth objects separated by piece-wise smooth discontinuities (e.g. seismic images). Our iterative Curvelet-regularized deconvolution algorithm combine...

The near-surface atmosphere of the polar region is characterized by temperature inversions throughout most of the year. However, radiosonde data are sparse, and numerical weather prediction models have relatively poor vertical resolution for boundary layer studies. A method is developed for detecting and estimating the characteristics of clear-sky, low-level temperature inversions using the Mod...

Accurate earthquake source parameters such as fault mechanism, depth, and moment magnitude are not only important in seismic-hazard assessment, but also are crucial to studies of earthquake rupture processes and seismotectonics. Although large earthquakes (Mw 7+) may cause substantial damage, they occur less frequently. In contrast, moderate earthquakes (Mw 5.0–6.5) occur with much higher frequ...

Consider the solution of large sparse symmetric positive de nite linear systems using the preconditioned conjugate gradient method. On sequential architectures, incomplete Cholesky factorizations provide ef13 fective preconditioning for systems from a variety of application domains, some of which may have widely di ering preconditioning requirements. However, incomplete factorization based prec...

A number of recent works have proposed to solve the line spectral estimation problem by applying off-the-grid extensions of sparse estimation techniques. These methods are preferable over classical line spectral estimation algorithms because they inherently estimate the model order. However, they all have computation times which grow at least cubically in the problem size, thus limiting their p...

An experimental study of the phase inversion behavior of liquid-liquid dispersion has been conducted in a spray extraction column for systems of toluene / water, n-hexane/water, CCl4/water, toluene /water + glycerol (25 % wt), toluene + CCl4 (25 % wt) / water and toluene / acetic acid (5 % wt)/water. The effects of physical properties, mass transfer and column geometry...

Modal analysis classicaly used signals that respect the Shannon/Nyquist theory. Compressive sampling (or Compressed Sampling, CS) is a recent development in digital signal processing that offers the potential of high resolution capture of physical signals from relatively few measurements, typically well below the number expected from the requirements of the Shannon/Nyquist sampling theorem. Thi...

We give a short introduction to methods for the data-sparse approximation of matrices resulting from the discretisation of non-local operators occurring in boundary integral methods, as the inverses of partial differential operators or as solutions of control problems. The result of the approximation will be so-called hierarchical matrices (or short H-matrices). These matrices form a subset of ...

Pan-sharpening, a method for constructing high resolution images from low resolution observations, has recently been explored from the perspective of compressed sensing and sparse representation theory. We present a new pansharpening algorithm that uses a Bayesian nonparametric dictionary learning model to give an underlying sparse representation for image reconstruction. In contrast to existin...