Recently, multi-trace impedance inversion has attracted great interest in seismic exploration because it improves the horizontal continuity and fidelity of results by exploiting lateral structure information strata. However, computational inefficiency affects its practical application. Furthermore, terms vertical constraints on model parameters, only considers smooth features while ignoring sha...

Deblending of simultaneous-source data is usually considered to be an underdetermined inverse problem, which can be solved by an iterative procedure, assuming additional constraints like sparsity and coherency. By exploiting the fact that seismic data are spatially band-limited, deblending of densely sampled sources can be carried out as a direct inversion process without imposing these constra...

Acoustic impedance (AI) inversion is widely used in geophysics and reservoir prediction. But the traditional method cannot fully exploit sparse characteristics of geological attributes. There are problems with multiplicity low resolution. To solve this problem, a data-driven acoustic reweighted L1 norm constraints (DRL1) proposed. In process, local cross-correlation analysis introduced to above...

The complexity of computing conventional matrix multiply wave-front reconstructors scales as O(n3) for most adaptive optical (AO) systems, where n is the number of deformable mirror (DM) actuators. This is impractical for proposed systems with extremely large n. It is known that sparse matrix methods improve this scaling for least-squares reconstructors, but sparse techniques are not immediatel...

Due to the ill-posed nature of nonlinear inverse problems of borehole geophysics, a parameterization approach is necessary when the available measurement data are limited and measurements are only carried out from sparse transmitter-receiver positions (limited data diversity). A potential remedy is the joint inversion of multiphysics measurements. A parametric inversion approach has desirable a...

Abstract Pre-stack seismic inversion is an effective method to estimate multiparameter from reflection data in the oil–gas reservoir exploration. In order get a reliable inverted parameter result, constrained model commonly used reduce multi-solution inversion. However, it hard appropriate by conventional well-log interpolation or velocity field offshore exploration with sparse wells. Envelope ...

Regularized inversion methods for image reconstruction are used widely due to their tractability and their ability to combine complex physical sensor models with useful regularity criteria. Such methods were used in the recently developed Plug-and-Play prior method, which provides a framework to use advanced denoising algorithms as regularizers in inversion. However, the need to formulate regul...

[1] This paper investigates the effect of fine-scale spatial variability in carbon fluxes upon regional carbon flux inversion estimates in North America using simulated data from 1 May through 31 August 2004 and a hypothetical sparse network of eight towers in North America. A suite of random smooth regional carbon flux patterns are created and then obscured with random fine-scale spatial flux ...