scispace - formally typeset
Journal ArticleDOI

Generalized Nonlinear Inverse Problems Solved Using the Least Squares Criterion

Albert Tarantola, +1 more
- 01 May 1982 - 
- Vol. 20, Iss: 2, pp 219-232
Reads0
Chats0
TLDR
In this article, a general definition of the nonlinear least squares inverse problem is given, where the form of the theoretical relationship between data and unknowns may be general (in particular, nonlinear integrodierentia l equations).
Abstract
We attempt to give a general definition of the nonlinear least squares inverse problem. First, we examine the discrete problem (finite number of data and unknowns), setting the problem in its fully nonlinear form. Second, we examine the general case where some data and/or unknowns may be functions of a continuous variable and where the form of the theoretical relationship between data and unknowns may be general (in particular, nonlinear integrodierentia l equations). As particular cases of our nonlinear algorithm we find linear solutions well known in geophysics, like Jackson’s (1979) solution for discrete problems or Backus and Gilbert’s (1970) a solution for continuous problems.

read more

Content maybe subject to copyright    Report

Citations
More filters
Journal ArticleDOI

EMMA—A Geophysical Training and Education Tool for Electromagnetic Modeling and Analysis

TL;DR: An interactive modeling and analysis program with a user-friendly graphical interface, for students and professionals in the field of exploration geophysics, has been developed as mentioned in this paper, where the ElectroMagnetic...
Journal ArticleDOI

Velocity estimation via registration-guided least-squares inversion

TL;DR: An iterative inversion scheme in which the notion of proximity of two traces is not the usual LS distance, but instead it involves registration as in image processing is evaluated, which can converge to the correct model in situations in which conventional LS inversion suffers from a very narrow basin of attraction near the global minimum.
Posted Content

Velocity estimation via registration-guided least-squares inversion

TL;DR: In this article, the authors proposed an iterative scheme for acoustic model inversion where the notion of proximity of two traces is not the usual least-squares distance, but instead involves registration as in image processing.
Journal ArticleDOI

Box Tomography: first application to the imaging of upper-mantle shear velocity and radial anisotropy structure beneath the North American continent

TL;DR: In this article, the authors presented a 3D isotropic and radially anisotropic shear wave velocity model of the North American upper mantle, using a combination of teleseismic and regional waveforms down to 40 s period and wavefield computations are performed using the spectral element method.
Journal ArticleDOI

A new 3-D inversion algorithm for magnetic total field anomalies

TL;DR: In this paper, a 3D inversion algorithm for the interpretation of magnetic total field anomalies is presented, which allows the inclusion of a priori information of model parameters and to treat this information as well as the measured data in a probabilistic manner.
References
More filters
Book

Linear statistical inference and its applications

TL;DR: Algebra of Vectors and Matrices, Probability Theory, Tools and Techniques, and Continuous Probability Models.
Journal ArticleDOI

Uniqueness in the Inversion of Inaccurate Gross Earth Data

TL;DR: In this article, it was shown that a given set G of measured gross Earth data permits such a construction of localized averages, and if so, how to find the shortest length scale over which G gives a local average structure at a particular depth if the variance of the error in computing that local average from G is to be less than a specified amount.
Journal ArticleDOI

The general linear inverse problem - Implication of surface waves and free oscillations for earth structure.

TL;DR: In this paper, the discrete general linear inverse problem is reduced to a set of m equations in n unknowns and a linear combination of the eigenvectors of the coefficient matrix can be used to determine parameter resolution and information distribution among the observations.