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conclusion

Estimating filters is an important step of many processing techniques like signal/noise separation, data interpolation, missing data restoration or regularized tomography. The stationarity of the data is at the heart of many filter estimation techniques. In the case where the data have strong amplitudes variations, I show that a weighting function that leverages the fitting equations during the filter estimation step must be used for improved results. In other words, the residual should be weighted and not the data points. Test with 2-D and 3-D field data with stationary and non-stationary filters for two signal-noise separation problems prove that a weighted residual PEF estimation scheme give the best estimated noise and signal while preserving the amplitudes.


next up previous print clean
Next: REFERENCES Up: Guitton: Weighted PEF estimation Previous: A 3-D land data
Stanford Exploration Project
7/8/2003