Inversion of seismic reflection data in the acoustic approximation

Инверсия сейсмических отраженных данных в акустическом приближении
Albert Tarantola
1984-08-01

acoustic approximationbulk modulus and density estimationgeneralized least-squaresmigration of unstacked dataseismic reflection data inversion
Abstract The nonlinear inverse problem for seismic reflection data is solved in the acoustic approximation. The method is based on the generalized least-squares criterion, and it can handle errors in the data set and a priori information on the model. Multiply reflected energy is naturally taken into account, as well as refracted energy or surface waves. The inverse problem can be solved using an iterative algorithm which gives, at each iteration, updated values of bulk modulus, density, and time source function. Each step of the iterative algorithm essentially consists of a forward propagation of the actual sources in the current model and a forward propagation (backward in time) of the data residuals. The correlation at each point of the space of the two fields thus obtained yields the corrections of the bulk modulus and density models. This shows, in particular, that the general solution of the inverse problem can be attained by methods strongly related to the methods of migration of unstacked data, and commercially competitive with them.
1
A nonlinear inverse method for seismic reflection data is formulated within the acoustic approximation using a generalized least-squares criterion.
2
An iterative algorithm updates bulk modulus, density, and the time source function at each iteration.
3
Each iteration involves forward propagation of current sources and backward-in-time propagation of data residuals, with their spacewise correlation yielding model corrections.
4
Multiply reflected energy, refracted energy, and surface waves are naturally accounted for in the inversion.
5
The general solution connects closely to migration of unstacked data and is commercially competitive with migration methods.
6
The method can incorporate errors in the data and a priori information about the model.

Seismic reflection data inverse problem in the acoustic approximation

Estimation/reconstruction of subsurface bulk modulus, density, and source time function (accounting for multiples, refractions, and surface waves) using a generalized least-squares iterative inversion correlating forward-propagated sources and backpropagated data residuals

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1984-08-01
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Albert Tarantola
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