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:Friday, May 9. 2014madagascar1.6 released
The 1.6 stable release features fifteen new reproducible papers and multiple other enhancements including the addition of the seismic migration gallery.
According to the SourceForge statistics, the previous 1.5 stable distribution has been downloaded more than 5,000 times. The record number of downloads in September 2013 is probably due to the fact that Madagascar is being used for teaching at different universities. The top country (with 40% of all downloads) was China, followed by the US, Brazil, Mexico, and Australia. According to Ohloh.net, the year before the 1.6 release was the period of a high development activity, with 41 contributors (up 41% compared to the previous year) making 1,762 commits to the repository. Ohloh.net says that Madagascar "has a well established, mature codebase maintained by a very large development team with stable yearoveryear commits " and estimated 201 manyears of effort. Monday, April 28. 2014IWAVE update
The IWAVE package included in Madagascar went through a significant redesign. The current version is explained in the paper IWAVE Structure and Basic Use Cases by Bill Symes.
The IWAVE control structure facilitates construction of wave simulators with flexible specification of input and output. This document describes synthesis of seismograms and wavefield movies from initial data and from single and multiple sources (righthand sides), and linearized (``Born'') and linearized adjoint (reverse time migration) modeling. The choice of physical model and simulation method  constant density acoustics with Dirichlet boundary conditions and (2,2k) finite difference schemes  is the simplest possible, but the framework accommodates any regularly gridded stencilbased discretization of arbitrary wave physics in the same way. Saturday, April 26. 2014GPU implementation of RTM
A new paper is added to the collection of reproducible documents:
RTM using effective boundary saving: A staggered grid GPU implementation GPU has become a booming technology in reverse time migration (RTM) to perform the intensive computation. Compared with saving forward modeled wavefield on the disk, RTM via wavefield reconstruction using saved boundaries on device is a more efficient method because computation is much faster than CPUGPU data transfer. In this paper, we introduce the effective boundary saving strategy in backward reconstruction for RTM. The minimum storage requirement for regular and staggered grid finite difference is determined for perfect reconstruction of the source wavefield. Particularly, we implement RTM using GPU programming, combining staggered finite difference scheme with convolutional perfectly matched layer (CPML) boundary condition. We demonstrate the validity of the proposed approach and CUDA codes with numerical example and imaging of benchmark models. This paper is the first contribution from Xi'an Jiaotong University in China.
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