We process a bathymetry survey from the Sea of Galilee. This HREF="http://sepwww.stanford.edu/data/media/public/docs/sep114/guojian2/paper_html/node5.html#GEO36-03-04670481">Claerbout (1971) produces the correct phase HREF="http://sepwww.stanford.edu/data/media/public/docs/sep114/bill1/paper_html/node6.html#gee">Claerbout (1999); Claerbout (1985). This approximation ignores back-scattering in the HREF="http://sepwww.stanford.edu/data/media/public/docs/sep115/alejandro2/paper_html/node12.html#GEO36.03.04670481">Claerbout (1971).

A new method for estimating interval velocities without

At every point in a CMP gather, a local estimate of RMS velocity is: HREF="http://sepwww.stanford.edu/data/media/public/docs/sep117/bob2/paper_html/node12.html#gee">Claerbout (1999)

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="http://sepwww.stanford.edu/data/media/public/docs/sep120/bill2/paper_html/node5.html#Claerbout.blackwell.92">Claerbout (1992,Lomask and Claerbout (2002); Claerbout (1990), SEP progressed to reproducible

Anti-crosstalk [pdf 76K][source]

Inverse theory can never be wrong because it's just theory. Hubbert math [pdf 76K][source]

Hubbert fits growth and decay of petroleum production Tar sands: Reprieve or apocalypse? [pdf 436K][source]

Based on a Hubbert-type analysis Blocky models via the L1/L2 hybrid norm [pdf 112K][source]

This paper seeks to define robust, Theory and practice of interpolation in the pyramid domain [pdf 25M][source]

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]

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]

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

A new algorithm for bidirectional deconvolution[paper][source]

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]

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]

The blind-deconvolution problem for non-minimum-phase-source Data examples of logarithm Fourier-domain bidirectional deconvolution [pdf 2.7M] [source]

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]

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

A slight modification to Fourier spectral factorization Decon in the log domain with variable gain PDF source

We base deconvolution on the concept of output model sparsity. Modeling data error during deconvolution [PDF 72K SRC]

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]

In testing on several nearby data sets, three shown here, Six tests of sparse log decon [PDF 2.8M SRC]

Previously, we developed a sparseness-goaled decon method. HREF="antoine3/paper_html/node7.html#claerbout.sep.148.jon">Claerbout and Guitton, 2012).