Abstract
Whenever data sampling is regular along a coordinate in space and/or time, it is relevant to look for approximate shift invariance which casts the forward problem into a convolution formulation. The resulting computations may be speeded up significantly through the Fourier transform. For nonlinear problems the Born approximation in horizontally stratified media leads to just such a result. In many cases the noise is well approximated by a stationary process, and it turns out that the resulting inverse solution is then a multi-channel deconvolution. This formulation allows very fast inversion in the periodic approximation of densely sampled high volume data sets. New applications within geophysical well logging, continuous geoelectrical sounding/profiling, and 3D helioseismic tomography demonstrate the wide applicability of this method.
| Original language | English |
|---|---|
| Pages (from-to) | 215-220 |
| Number of pages | 6 |
| Journal | Physics and Chemistry of the Earth, Part A: Solid Earth and Geodesy |
| Volume | 24 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 1999 |
| Externally published | Yes |
Programme Area
- Programme Area 2: Water Resources
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