Crustal thickness is a critical parameter for understanding the processes of continental rifting and breakup and the evolution of petroleum systems within passive margins. However, direct measurements of crustal thickness are sparse and expensive, highlighting the need for methodologies using gravity anomaly data, jointly with other geophysical data, to estimate crustal thickness. We evaluated ...

Missing trace reconstruction is an ongoing challenge in seismic processing due to incomplete acquisition schemes and irregular grids. Noise is also a concern because it is naturally present in acquired seismic data through several mechanisms such as natural noise and equipment noise. Both problems need to be adequately addressed, especially because they negatively affect several important proce...

This paper is devoted to the problem of sampling Gaussian fields in high dimension. Solutions exist for two specific structures of inverse covariance : sparse and circulant. The proposed approach is valid in a more general case and especially as it emerges in inverse problems. It relies on a perturbation-optimization principle: adequate stochastic perturbation of a criterion and optimization of...

We propose an algebraic combinatorial method for solving large sparse linear systems of equations locally that is, a method which can compute single evaluations of the signal without computing the whole signal. The method scales only in the sparsity of the system and not in its size, and allows to provide error estimates for any solution method. At the heart of our approach is the so-called reg...

We propose an algebraic combinatorial method for solving large sparse linear systems of equations locally that is, a method which can compute single evaluations of the signal without computing the whole signal. The method scales only in the sparsity of the system and not in its size, and allows to provide error estimates for any solution method. At the heart of our approach is the so-called reg...

[1] Geostatistical inverse modeling has been shown to be a viable alternative to synthesis Bayesian methods for estimating global continental-scale CO2 fluxes. This study extends the geostatistical approach to take advantage of spatially and temporally varying auxiliary data sets related to CO2 flux processes, which allow the inversion to capture more grid-scale flux variability and better cons...

seismic tomography is an imaging technique which creates maps of subsurface elastic properties such as p/s wave velocity, density and attenuation, based on observed seismograms and use of sophisticated inversion algorithms. amongst different acquisition geometries, seismic cross-hole tomography has a special position in geophysical surveys with many applications in hydrocarbons, coal and other ...

Sparsity has become a key concept for solving of high-dimensional inverse problems using variational regularization techniques. Recently, using similar sparsity-constraints in the Bayesian framework for inverse problems by encoding them in the prior distribution has attracted attention. Important questions about the relation between regularization theory and Bayesian inference still need to be ...

Data-driven reduced order models (ROMs) are combined with the Lippmann-Schwinger integral equation to produce a direct nonlinear inversion method. The ROM is viewed as Galerkin projection and sparse due Lanczos orthogonalization. Embedding into continuous problem, data-driven internal solution produced. This then used in equation, thus making further iterative updates unnecessary. We show numer...