SUMMARY To investigate the small-scale elastic structure of subsurface at length scales below resolution limits waveform tomography, envelopes high-frequency scattered seismic waveforms have been used with a variety approaches. However, rigorous framework for iterative inversion seismogram to image heterogeneity and attenuation comparable full (FWI) is missing. We present mathematical an envelo...

Characterizing the elastic properties in deep-buried reservoirs beneath complex overburden structures remains challenging for seismic inversion. Elastic full-waveform inversion (FWI) is capable of quantitatively estimating subsurface with a reasonably high resolution. However, FWI using frequencies computationally expensive because fine discretization required to stabilize wavefield simulation....

Abstract. Forest fires constitute a major environmental and socioeconomic hazard in the Mediterranean. Weather climate are among main factors influencing forest fire potential. As danger is expected to increase under changing climate, seasonal forecasting of meteorological conditions conductive paramount importance for implementing effective prevention policies. The aim current study provide hi...

Imaging subsurface small-scale features and monitoring transport of tracer plumes at a fine resolution is interest to characterize processes in aquifers. Full-waveform inversion (FWI) crosshole ground penetrating radar (GPR) measurements enables aquifer characterization decimeter-scale resolution. The method produces images both relative dielectric permittivity (εr) bulk electrical conductivity...

The subsurface velocity model is crucial for high-resolution seismic imaging. Although full-waveform inversion (FWI) a high-accuracy method, it inevitably suffers from challenging problems, including human interference, strong nonuniqueness, and high computing costs. As an efficient accurate nonlinear algorithm, deep learning (DL) has been used to estimate models. However, conventional DL insuf...

Full waveform inversion (FWI) commonly stands for the state-of-the-art approach imaging subsurface structures and physical parameters, however, its implementation usually faces great challenges, such as building a good initial model to escape from local minima, evaluating uncertainty of results. In this paper, we propose implicit full (IFWI) algorithm using continuously implicitly defined deep ...

SUMMARY Full-waveform inversion has become an essential technique for mapping geophysical subsurface structures. However, proper uncertainty quantification is often lacking in current applications. In theory, related to the inverse Hessian (or posterior covariance matrix). Even common problems its calculation beyond computational and storage capacities of largest high-performance computing syst...

Full Waveform Inversion (FWI) methods use generally gradient based method, such as the nonlinear conjugate gradient method or more recently the l-BFGS quasi-Newton method. Several authors have already investigated the possibility of accounting more accurately for the inverse Hessian operator in the minimization scheme through Gauss-Newton or exact Newton algorithms. We propose a general framewo...

This is a survey of interpretations of q hypergeometric orthogonal polynomials on quantum groups The rst half of the paper gives general background on Hopf algebras and quantum groups The emphasis in the rest of the paper is on the SU quantum group An interpretation of little q Jacobi polynomials as matrix elements of its irreducible representations is presented In the last two sections new res...

SUMMARY Efficient numerical wave modelling is essential for imaging methods such as reverse-time migration (RTM) and full waveform inversion (FWI). In 2D, frequency-domain modelling with LU factorization as a direct solver can outperform time-domain methods by one order. For 3-D problems, the computational complexity of the LU factorization as well as its memory requirements are a disadvantage ...