Wednesday, April 2. 2014Program of the month: sfcostaper
sfcostaper applies cosine tapering to the edges of the input data.
Cosine tapering amounts to multiplying the edges by ${\mathrm{sin}}^{2}\left(\genfrac{}{}{0.1ex}{}{\pi \phantom{\rule{0.167em}{0ex}}x}{2}\right)=\genfrac{}{}{0.1ex}{}{1+\mathrm{cos}\left(\pi \phantom{\rule{0.167em}{0ex}}x\right)}{2}$, where $x$ is the relative distance from the start of the taper. sfcostaper works in N dimensions, the size of the taper in #th dimension in samples is specified by nw#=. In the following example from gee/ajt/igrad1 the images are tapered at both edges by specifying nw1=50 nw2=50. 10 previous programs of the month:Tuesday, March 25. 2014Optimal aperture in Kirchhoff migration
A new paper is added to the collection of reproducible documents:
Selecting an optimal aperture in Kirchhoff migration using dipangle images We present a method for selecting a migration aperture in Kirchhoff migration. We first split migrated data into constantdipangle partial images. Then, in every partial image, we estimate the consistency between each event and the constant dip of the analyzed section. We filter out events whose slope is far from the corresponding dip. Stacking of the filtered partial images corresponds to migration having an optimal aperture. Synthetic and real data examples demonstrate that the proposed approach to migrationaperture optimization is able to reduce migration noise while preserving diffraction energy, which characterizes small geological objects and brings additional resolution to the image. Tuesday, March 11. 2014Matt Hall's tutorial on smoothing
If you enjoyed Matt Hall's tutorial on smoothing surfaces and attributes in the last month's issue of The Leading Edge, you can find it reproduced in Madagascar (with some extensions) under rsf/tutorials/smoothing.
If you like IPython notebooks, you can also reproduce this exercise using the provided notebook. Madagascar users are encouraged to try improving this reproducible example. Program of the math: sflpad
sflpad pads the input data by inserting zero traces and zero planes between traces and planes in the input.
The following example from gee/lal/lace shows a classic example of interpolation beyond aliasing, which appears on the cover of Jon Claerbout's book Processing Versus Inversion: 10 previous programs of the month:Friday, March 7. 2014Reproducibility workshop @ XSEDE
XSEDE (Extreme Science and Engineering Discovery Environment) is hosting a workshop on reproducibility as a fullday event on Monday, July 14, 2014, during the XSEDE conference in Atlanta, Georgia. The workshop promises to address the issue of the reproducibility crisis in computational science addresses during the Data and Code Sharing Roundtable at Yale in 2009.
XSEDE is the world's largest, most comprehensive distributed cyberinfrastructure for open scientific research, which integrates highperformance computers and other facilities around the US. Saturday, February 15. 2014Local skewness attribute
A new paper is added to the collection of reproducible documents:
Local skewness attribute as a seismic phase detector We propose a novel seismic attribute, local skewness, as an indicator of localized phase of seismic signals. The proposed attribute appears to have a higher dynamical range and a better stability than the previously used local kurtosis. Synthetic and real data examples demonstrate the effectiveness of local skewness in detecting and correcting timevarying, locallyobserved phase of seismic signals. Tuesday, February 11. 2014Madagascar school in St. Petersburg
A Madagascar school will take place on April 11, 2014, in St. Petersburg, Russia, at a workshop during the EAGE convention. The workshop was proposed by Paul Sava.
See the workshop page for more information. Thursday, February 6. 2014Program of the month: sfdipfilter
sfdipfilter filters input data based on a range of dips (slopes).
The following example from rsf/su/rsfdipfilt (borrowed from one of Seismic Unix demos) shows a synthetic dataset with three events before and after dip filtering: sfdipfilter operates in 2D or 3D Fourier transform domain, with the dimensionality specified by dim=. The dip range is specified either by angles (if angle=y) or by velocities (if angle=n). The four parameters (either ang1=, ang2=, ang3=, ang4= or v1=, v2=, v3=, v4=) specify the range. If pass=y, the range between v2 and v3 is passed, and the range below v1 or above v4 is rejected. If pass=n, the range between v2 and v3 is rejected, and the range below v1 or above v4 is passed. The transition between pass and reject regions is implemented with a sine tapering. 10 previous programs of the month:Thursday, January 9. 2014Program of the month: sfinttest1
sfinttest1 performs forward interpolation from a regular grid to irregular locations (in 1D).
The following example from sep/forwd/chirp shows regularly sampled values of a variablefrequency signal and the error of its interpolation using linear and cubicconvolution interpolators. The irregular coordinates for interpolation are supplied in a file specified by coord=. The type of the interpolator is specified by interp=. The currently implemented types are Lagrange (including linear and nearestneighbor interpolation), cubic convolution, weighted sinc interpolation (with Kaiser, Lanczos, cosine, and Welsh windows), Bspline, and MOM (slightly improved Bspline). The size of the interpolation filter is given by nw=. The Kaiserwindow interpolator requires an additional parameter, which is specified by kai=. An alternative (using invertible cubic spline interpolation) is sfiwarp. A 2D version is sfinttest2. The following example from sep/forwd/chirp2 compares the error of Kaiserwindowed 8point sinc interpolation and 8point Bspline interpolator applied to interpolating a 2D signal. 10 previous programs of the month:Saturday, December 7. 2013Structural uncertainty in time migration
A new paper is added to the collection of reproducible documents:
Structural uncertainty of timemigrated seismic images Structural information in seismic images is uncertain. The main cause of this uncertainty is uncertainty in velocity estimation. We adopt the technique of velocity continuation for estimating velocity uncertainties and corresponding structural uncertainties in timemigrated images. Data experiments indicate that structural uncertainties can be significant even when both structure and velocity variations are mild. Friday, December 6. 2013Parallel sweeping preconditioner for 3D Helmholtz
A new paper is added to the collection of reproducible documents:
A parallel sweeping preconditioner for heterogeneous 3D Helmholtz equations A parallelization of a sweeping preconditioner for 3D Helmholtz equations without large cavities is introduced and benchmarked for several challenging velocity models. The setup and application costs of the sequential preconditioner are shown to be and , where denotes the modestly frequencydependent number of grid points per Perfectly Matched Layer. Several computational and memory improvements are introduced relative to using blackbox sparsedirect solvers for the auxiliary problems, and competitive runtimes and iteration counts are reported for highfrequency problems distributed over thousands of cores. Two opensource packages are released along with this paper: Parallel Sweeping Preconditioner (PSP) and the underlying distributed multifrontal solver, Clique. Sunday, December 1. 2013Program of the month: sfcausint
sfcausint implements an operation of causal numerical integration.
This is a simple operation, which mathematically amounts to recursion ${y}_{n}={y}_{n1}+{x}_{n}$ or to inversion of a simple bidiagonal matrix. See Geophysical Image Estimation by Example for more explanation. The only parameter in sfcausint is adj=, the flag for adjoint computation. The adjoint operation applies recursion backwards ${x}_{n1}={x}_{n}+{y}_{n1}$. The following example from gee/ajt/causint illustrates forward and ajoint causal integration with sfcausint: 10 previous programs of the month:Thursday, November 21. 2013Madagascar paper
A paper describing Madagascar has been published in the Journal of Open Research Software (JORS), a new peerreviewed openaccess journal, which features papers describing research software with high reuse potential.
This paper should become a standard reference for those who use Madagascar in their research and wish to reference it in scientific publications. Following a recommendation of Robin Wilson, a file called CITATION.txt is placed in the top Madagascar directory to provide reference information. Here are the current contents of this file:
It is hard to give proper credit to everyone who contributed to such as collaborative project, as Madagascar. Even the smallest contribution can be crucially important. The five authors of the paper are the five most active alltime contributors to Madagascar by the number of commits to the repository at the time of the paper submission. Monday, November 18. 2013Geophysical Image Estimation by Example
Inspired by projects such as the Khan Academy, Jon Claerbout is recording short videos narrating his book, Geophysical Image Estimation by Example. You can reproduce computational examples from the book (translated from Ratfor to C) by following its Madagascar version.
Wednesday, November 13. 20133D random noise attenuation using fxy NRNA
A new paper is added to the collection of reproducible documents:
Noncausal fxy regularized nonstationary prediction filtering for random noise attenuation on 3D seismic data Seismic noise attenuation is very important for seismic data analysis and interpretation, especially for 3D seismic data. In this paper, we propose a novel method for 3D seismic random noise attenuation by applying noncausal regularized nonstationary autoregression (NRNA) in fxy domain. The proposed method, 3D NRNA (fxy domain) is the extended version of 2D NRNA (fx domain). fxy NRNA can adaptively estimate seismic events of which slopes vary in 3D space. The key idea of this paper is to consider that the central trace can be predicted by all around this trace from all directions in 3D seismic cube, while the 2D fx NRNA just considers the middle trace can be predicted by adjacent traces along one space direction. 3D fxy NRNA uses more information from circumjacent traces than 2D fx NRNA to estimate signals. Shaping regularization technology guarantees the nonstationary autoregression problem can be realizable in mathematics with high computational efficiency. Synthetic and field data examples demonstrate that, compared with fx NRNA method, fxy NRNA can be more effective in suppressing random noise and improve tracebytrace consistency, which are useful in conjunction with interactive interpretation and autopicking tools such as automatic event tracking.
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