Wednesday, August 20. 2014Tutorial on data slicing
The example in rsf/tutorials/slicing reproduces the tutorial from Evan Bianco of simple data slicing.
See also: Madagascar users are encouraged to try improving the results. Iterative deblending using shaping regularization
A new paper is added to the collection of reproducible documents:
Iterative deblending of simultaneoussource seismic data using seisletdomain shaping regularization We introduce a novel iterative estimation scheme for separation of blended seismic data from simultaneous sources. The scheme is based on an augmented estimation problem, which can be solved by iteratively constraining the deblended data using shaping regularization in the seislet domain. We formulate the forward modeling operator in the common receiver domain, where two sources are assumed to be blended using a random timeshift dithering approach. The nonlinear shapingregularization framework offers some freedom in designing a shaping operator to constrain the model in an underdetermined inverse problem. We design the backward operator and the shaping operator for the shaping regularization framework. The backward operator can be optimally chosen as a half of the identity operator in the twosource case, and the shaping operator can be chosen as coherencypromoting operator. Three numerically blended synthetic datasets and one numerically blended field dataset demonstrate the highperformance deblending effect of the proposed iterative framework. Compared with alternative fk domain thresholding and fx predictive filtering, seisletdomain soft thresholding exhibits the most robust behavior. Tuesday, August 5. 2014Second Madagascar Working Workshop
"Working workshops" as opposed to "talking workshops" are meetings where the participants work together (possibly divided into pairs or small teams) to develop new software code or to conduct computational experiments addressing a particular problem. Working workshops are a cross between scientific workshops and coding sprints or hackathons common among opensource software communities.
26 participants from 11 different organizations gathered at Rice University at the end of July and beginning of August for the Second Madagascar Working Workshop, hosted by The Rice Inversion Project. The topic of the workshop was parallel highperformance computing. The participants divided into teams of 23 people by pairing experienced Madagascar developers with novice users. Each team worked on a small project, creating examples of parallel computing or improving generalpurpose tools such as sfmpi, sfomp, and (newly created) sfbatch. The participants used Stampede, the world's seventh most powerful supercomputer, provided by the Texas Advanced Computing Center, for their computational experiments. Sunday, July 13. 2014Program of the month: sfltft
sfltft (Local TimeFrequency Transform) decomposes input data into frequency components. The algorithm is described in the paper Seismic data analysis using local timefrequency decomposition and is based on regularized nonstationary regression.
The following example from tccs/ltft/timefreq shows 1D synthetic data composed of two chirp signals and the magnitude of coefficients in its timefrequency decomposition: The frequency sampling in the output of sfltft is controled by nw=, w0=, and dw=. By default, these parameters correspond to the sampling of the discrete Fourier transform. The critical parameters for regularized regression are rect= (smoothing radius in time, in samples) and niter= (number of iterations). To output the details of iterative regularization, use verb=y. The frequency sampling and the rect= parameter provide explicit controls on timefrequency resolution. In the example above, rect=7. It is possible to change smoothing radius with frequency by using alpha= parameter. The iterative inversion can be controlled additionally by specifying a data weight (with mask=) or a model weight (with weight=). Optionally, the Fourier basis used in the decomposition can be extracted from the program by specifying basis= file. To perform the inverse transform from timefrequency back to time domain, use inv=y. sfltft takes realvalued input and produces complexvalued output. An analogous program for transforming complexvalued data is sfcltft. A related program is sftimefreq described in Timefrequency analysis of seismic data using local attributes. 10 previous programs of the month:Thursday, July 10. 2014Madagascar in the cloud
SageMathCloud is a free cloud computing platform for computational mathematics created by William Stein, the leader of the Sage project.
SageMathCloud provides a rich environment, which allows one, for example, to easily install Madagascar and to access it interactively through its Python interface. The example above shows Madagascar running interactively in the cloud using an IPython notebook hosted by SageMathCloud. Support for interactive widgets is a new feature in IPython version 2 released earlier this year. See also Tuesday, July 1. 2014Making a wedge
The example in rsf/tutorials/wedge reproduces the example from Evan Bianco of simple convolution modeling with a wedge model.
See also: Madagascar users are encouraged to try improving the results. Tuesday, June 24. 2014Fast elastic mode separation in anisotropic media
A new paper is added to the collection of reproducible documents:
Fast algorithms for elasticwavemode separation and vector decomposition using lowrank approximation for anisotropic media Wave mode separation and vector decomposition are significantly more expensive than wavefield extrapolation and are the computational bottleneck for elastic reversetime migration (ERTM) in heterogeneous anisotropic media. We express elastic wave mode separation and vector decomposition for anisotropic media as spacewavenumberdomain operations in the form of Fourier integral operators, and develop fast algorithms for their implementation using their lowrank approximations. Synthetic data generated from 2D and 3D models demonstrate that these methods are accurate and efficient. Thursday, June 12. 2014How do I do interactive picking?
While interactive picking is generally discouraged because of its nonreproducibility, occasionally it might be useful.
Using interact= option with xtpen outputs mouseclick coordinates in a text file. However, they are Vplot coordinates, not easily related to physical coordinates of the image. Joe Dellinger has a more comprehensive plan for adding interactivity to Vplot graphics. sfipick is a simple Tkinter script which allows for interactive picking. The interface is straightforward. Use leftbutton mouse clicks to add picks, rightbutton mouse clicks to remove wrong picks, middlebutton to drag picks. The picks are written in a plain text file and can be processed later. See also: Wednesday, June 11. 2014Program of the month: sfeikonal
sfeikonal solves the eikonal equation using the Fast Marching Method. This computation produces firstarrival traveltimes on a fixed grid.
The following example from sep/fmeiko/fmarch shows traveltime contours for a point source inside the SEG/EAGE salt model. The point source can be specified by its coordinates xshot=, yzhot=, and zshot= (note that the depth coordinate zshot corresponds to the first axis). In a small box around the source, the solution is computed analytically to avoid errors from the pointsource singularity. The size of the box can be specified in samples (b1=, b2=, and b3=) or in physical dimensions (br1=, br2=, and br3=). For a planewave source instead of a point source, use plane1=y, plane2=y, or plane3=y. The plane is assumed to be aligned with the grid. For computing a traveltime table with multiple sources, the source coordinates can be specified in a file given by shotfile=. The order of accuracy in the finitedifference scheme is specified by order= parameter. The following plot from sep/fmsec/cvel shows the error difference between the first and secondorder computations in a constantvelocity medium. sfeikonal computes isotropic traveltimes. For an extension to VTI anisotropy, see sfeikonalvti. 10 previous programs of the month:Monday, June 2. 2014Lowrank on a staggered grid
A new paper is added to the collection of reproducible documents:
Lowrank seismic wave extrapolation on a staggered grid We propose a new spectral method and a new finitedifference method for seismic wave extrapolation in time. Using staggered temporal and spatial grids, we derive a wave extrapolation operator using a lowrank decomposition for a firstorder system of wave equations and design the corresponding finitedifference scheme. The proposed methods extend previously proposed lowrank and lowrank finitedifference wave extrapolation methods from the cases of constant density to those of variable density. Dispersion analysis demonstrates that the proposed methods have high accuracy for a wide wavenumber range and significantly reduce the numerical dispersion. The method of manufactured solutions coupled with mesh refinement is used to verify each method and to compare numerical errors. 2D synthetic examples demonstrate that the proposed method is highly accurate and stable. The proposed methods can be used for seismic modeling or reverse time migration. Sunday, June 1. 2014Second working workshop
Registration is open for Madagascar's Second Working Workshop.
As a reminder, Working Workshops as opposed to "talking workshops" are meetings where the participants work together (possibly divided into pairs or small teams) to develop new software code or to conduct computational experiments addressing a particular problem. The First Working Workshop took place last summer in Austin. This year's workshop will take place on at Rice University in Houston, Texas, on July 31  August 2, 2014. The topic of this year's workshop is parallel and highperformance computing. The objective is
Registration is free by an application is required. If you are interested in participating in this workshop, please fill an application form. Friday, May 30. 2014Tutorial on wave propagation
A new paper is added to the collection of reproducible documents:
Pengliang Yang from Xi'an Jiaotong University contributes A numerical tour of wave propagation This tutorial is written for beginners as an introduction to basic wave propagation using finite difference method, from acoustic and elastic wave modeling, to reverse time migration and full waveform inversion. Most of the theoretical delineations summarized in this tutorial have been implemented in Madagascar with Matlab, C and CUDA programming, which will benefit readers' further study. Tuesday, May 27. 2014Erupting Mount St. Helens
The example in rsf/tutorials/sthelens reproduces the analysis by Evan Bianco of the digital elevation data from Mount St. Helens before and after its catastrophic eruption.
Madagascar users are encouraged to try improving the results. Thursday, May 15. 2014Light Bartlein color palette
The orangewhitepurple diverging color palette was suggested in the article
Light, A and P.J. Bartlein (2004) The end of the rainbow? Color schemes for improved data graphics. EOS Transactions of the American Geophysical Union 85(40):385 Matteo Niccoli recommends it for seismic data as a replacement for the familiar redwhiteblue pallete (color=g in Madagascar). Now the LightBartlein palette is available to Madagascar plotting programs, such as sfgrey, as color=lb. See the following example from rsf/rsf/sfgrey: More information: Tuesday, May 13. 2014Program of the math: sfhelicon
sfhelicon performs multidimensional convolution and inverse convolution (recursive filtering) using the helix transform.
The theory behind helical convolution is explained by Jon Claerbout in the paper Claerbout, J., 1998, Multidimensional recursive filters via a helix: Geophysics, 63, 15321541. and in the corresponding chapter in his book Image Estimation by Example. The following example from gee/hlx/helicon illustrates inverse convolution on a helix. sfhelicon works in N dimensions. The filter for is supplied by filt= parameter, which points to a realvalued RSF file with filter coefficents. The filter lags on a helix can be stored in a separate integervalue RSF file specified wih lag= parameter (which can be optionally contained inside the file with filter coefficients). The dimensions used for specifying the filter lags are not necessarily the same as the dimensions of the input data and can be specified with n= parameter (which can be optionally contained inside the file with filter lags). The choice between forward and inverse convolution is controlled by div= parameter. The adjoint flag is supplied by adj= parameter. The following figure illustrates the act of the adjoint convolution and inverse convolution on a helix. For an example of leastsquares inversion with sfhelicon using the conjugategradient algorithm, see the documentation for sfconjgrad. 10 previous programs of the month:
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