subtraction method attenuates the noise better than the filtering approach Guitton and Claerbout (2003). This is difficult to answer...
Guitton A. and Claerbout J.
We process a bathymetry survey from the Sea of Galilee. This HREF="">Claerbout (1971) produces the correct phase HREF="">Claerbout (1999); Claerbout (1985). This approximation ignores back-scattering in the HREF="">Claerbout (1971).
Guitton A., Claerbout J., and Lomask J.
A new method for estimating interval velocities without
Wolf K., Rosales D., Guitton A., and Claerbout J.
At every point in a CMP gather, a local estimate of RMS velocity is: HREF="">Claerbout (1999)
Lomask J., Guitton A., Fomel S., and Claerbout J.
We present a method for efficiently flattening 3D seismic data volumes. First local dips are calculated over the entire seismic volume. The dips are then resolved into time shifts using a Gauss-Newton iterative approach that exploits the Fourier domain to maximize efficiency. To handle faults (discontinuous reflections), we apply a weight inversion scheme. This approach successfully flattens a synthetic faulted model, a field salt peircement dataset, a field dataset with an angular unconformity, and a faulted field dataset. HREF="">Claerbout (1992,Lomask and Claerbout (2002); Claerbout (1990), SEP progressed to reproducible

An algorithm for interpolation using Ronen's pyramid

[pdf 103k] [src 0.7Mb]Jon Claerbout and Antoine Guitton
Anti-crosstalk [pdf 76K][source]
Jon Claerbout
Inverse theory can never be wrong because it's just theory. Hubbert math [pdf 76K][source]
Jon Claerbout and Francis Muir
Hubbert fits growth and decay of petroleum production Tar sands: Reprieve or apocalypse? [pdf 436K][source]
Jon Claerbout
Based on a Hubbert-type analysis Blocky models via the L1/L2 hybrid norm [pdf 112K][source]
Jon Claerbout
This paper seeks to define robust, Theory and practice of interpolation in the pyramid domain [pdf 25M][source]
Antoine Guitton and Jon Claerbout
With the pyramid transform, 2-D dip spectra can be characterized by Least-squares imaging and deconvolution using the hb norm conjugate-direction solver [pdf 1.8M][source]
Yang Zhang and Jon Claerbout
To retrieve a sparse model, we applied the hybrid norm Claerbout to two interesting geophysical problems: least-squares imaging and blind deconvolution. The results showed that Geophysical applications of a novel and robust L1 solver [pdf 412K][source]
Yunyue Li, Yang Zhang, and Jon Claerbout
L1-norm is better than L2-norm at dealing with noisy data and yielding HREF="elita1/paper_html/node8.html#Jon09">Claerbout (2009) to perform L1 regressions. The solver is tested
Yang Zhang and Jon Claerbout

Chris Leader, Jon Claerbout, and Antoine Guitton
A new algorithm for bidirectional deconvolution[paper][source]
Yi Shen, Qiang Fu, and Jon Claerbout
We introduce a new algorithm for bidirectional HREF="../sep142/index.html#Yang2010">Zhang and Claerbout (2010). An approximation of the inverse Ricker wavelet as an initial guess for bidirectional deconvolution [paper][source]
Qiang Fu, Yi Shen, and Jon Claerbout
Bidirectional deconvolution is a powerful tool for performing blind deconvolution on a signal that contains a mixed-phase wavelet, such as seismic data. Previously, we used a prediction error filter (PEF) as the minimum-phase filter and an impulse function as the maximum-phase filter as initial guesses. This surprised us by apparent instability as the solution would jump from spiking the first pulse of a Ricker wavelet to get larger middle pulse. In this paper, we propose new initial guesses for the causal and anti-causal deconvolution filters that are more effective on data with a Ricker-like wavelet. We test these on both synthetic data and field data. The results demonstrate that the new starting filters do a better job than the previous initial guesses. A log spectral approach to bidirectional deconvolution [paper][source]
Jon Claerbout, Qiang Fu, and Yi Shen
The blind-deconvolution problem for non-minimum-phase-source Data examples of logarithm Fourier-domain bidirectional deconvolution [pdf 2.7M] [source]
Qiang Fu, Yi Shen, and Jon Claerbout
Time-domain bidirectional deconvolution methods show great promise for overcoming the minimum-phase assumption in blind deconvolution of signals containing a mixed-phase wavelet, such as seismic data. However, time-domain bidirectional methods usually suffer from slow convergence (Slalom method) or the starting model (Symmetric method). Claerbout proposed a logarithm Fourier-domain method to perform bidirectional deconvolution. In this paper, we test the new logarithm Fourier-domain method on both synthetic data and field data. The results demonstrate that the new method is more stable than previous methods and that it produces better results. Preconditioning a non-linear problem and its application to bidirectional deconvolution [pdf 1.5M] [source]
Yi Shen, Qiang Fu, and Jon Claerbout
Non-linear optimization problems suffer from local minima. When we use gradient-based iterative solvers on these problems, we often find the final solution to be Polarity preserving decon in ``N log N'' time PDF source
Jon Claerbout
A slight modification to Fourier spectral factorization Decon in the log domain with variable gain PDF source
Jon Claerbout, Antoine Guitton, and Qiang Fu
We base deconvolution on the concept of output model sparsity. Modeling data error during deconvolution [PDF 72K SRC]
Jon Claerbout and Antoine Guitton
Our current decons take the data sacrosanct and find the best noncausal wavelet to deconvolve it with. Decon comparisons between Burg and conjugate-gradient methods [PDF 408K SRC]
Antoine Guitton and Jon Claerbout
In testing on several nearby data sets, three shown here, Six tests of sparse log decon [PDF 2.8M SRC]
Antoine Guitton and Jon Claerbout
Previously, we developed a sparseness-goaled decon method. HREF="antoine3/paper_html/node7.html#claerbout.sep.148.jon">Claerbout and Guitton, 2012).