Tuesday, March 3. 2015CiSE Paper on Madagascar Community
The paper Reproducible Research as a Community Effort: Lessons from the Madagascar Project was published in the January/February 2015 issue of Computing in Science and Engineering, a special issue on Scientific Software Communities.
Reproducible research is the discipline of attaching software code and data to publications, which enables the reader to reproduce, verify, and extend published computational experiments. Instead of being the responsibility of an individual author, computational reproducibility should become the responsibility of open source scientificsoftware communities. A dedicated community effort can keep a body of computational research alive by actively maintaining its reproducibility. The Madagascar open source software project offers an example of such a community. Sunday, March 1. 2015Program of the month: sfhistogram
sfthistogram computes a histogram for distribution of values in the input dataset.
The following example from rsf/rsf/sfnoise plots the histogram of a normallydistributed random noise: The output of sfhistogram contains integer values arranged in a onedimensional array. The sampling is specified by n1=, d1=, and o1= parameters. 10 previous programs of the month:Saturday, January 31. 2015Acoustic staggered grid in IWAVE
A new paper is added to the collection of reproducible documents:
Acoustic staggered grid modeling in IWAVE IWAVE is a framework for timedomain regular grid finite difference and finite element methods. The IWAVE package includes source code for infrastructure component, and implementations of several wave physics modeling categories. This paper presents two sets of examples using IWAVE acoustic staggered grid modeling. The first set illustrates the effectiveness of a simple version of Perfectly Matched Layer absorbing boundary conditions. The second set reproduce illustrations from a recent paper on error propagation for heterogeneous medium simulation using finite differences, and demostrate the interface error effect which renders all FD methods effectively firstorder accurate. The source code for these examples is packaged with the paper source, and supports the user in duplicating the results presented here and using IWAVE in other settings. Friday, January 30. 2015Program of the month: sfmf
sfmf applies a median filter on the first dimension of the input.
The size of the filter window is controlled by nfw= parameter. The following example from tccs/medianfilter/dragon shows field seismic data before and after 11point (nfw=11) median filtering combined with bandpass filtering. For timevariant median filtering, see sftvmf. To simply output the median of the first axis, use sfmedian. 10 previous programs of the month:Tuesday, December 16. 2014NMO with super resolution
Another old paper is added to the collection of reproducible documents:
A prospect for super resolution Wouldn't it be great if I could take signals of 1030 Hz bandwidth from 100 different offsets and construct a zerooffset trace with 5100 Hz bandwidth? This would not violate Shannon's sampling theorem which theoretically allows us to have a transform from 100 signals of 20 Hz bandwidth to one signal at 2000 Hz bandwidth. The trouble is that simple NMO is not such a transformation. Nevertheless, if the different offsets really did give us any extra information, we should be able to put the information into extra bandwidth. Let us consider noise free synthetic data and see if we can come up with a model where this could happen. Wednesday, December 10. 2014Earthquake stacks
Another old paper is added to the collection of reproducible documents:
Earthquake stacks at constant offset I show Shearer's earthquake stacks over all sourcereceiver locations at constant offset and compare them to exploration seismic data. This electronic document simply reads the stacks and plots them. Sunday, December 7. 2014TXY adaptive filtering for random noise attenuation
A new paper is added to the collection of reproducible documents:
Adaptive prediction filtering in txy domain for random noise attenuation using regularized nonstationary autoregression Many natural phenomena, including geologic events and geophysical data, are fundamentally nonstationary. They may exhibit stationarity on a short timescale but eventually alter their behavior in time and space. We propose a 2D tx adaptive prediction filter (APF) and further extend this to a 3D txy version for random noise attenuation based on regularized nonstationary autoregression (RNA). Instead of using patching, a popular method for handling nonstationarity, we obtain smoothly nonstationary APF coefficients by solving a global regularized leastsquares problem. We use shaping regularization to control the smoothness of the coefficients of APF. 3D spacenoncausal txy APF uses neighboring traces around the target traces in the 3D seismic cube to predict noisefree signal, so it provides more accurate prediction results than the 2D version. In comparison with other denoising methods, such as frequencyspace deconvolution, timespace prediction filter, and frequencyspace RNA, we test the feasibility of our method in reducing seismic random noise on three synthetic datasets. Results of applying the proposed method to seismic field data demonstrate that nonstationary txy APF is effective in practice. This reproducible paper is the first direct contribution from Jilin University, China. Friday, December 5. 2014SCons is “Community Choice” Project of the Month
The favorite tool of all Madagascar users, SCons, is featured as the December 2014 “Community Choice” Project of the Month at SourceForge.
SCons is a software construction tool (build tool, or make tool) implemented in Python, which uses Python scripts as “configuration files” for software builds. It is an easier, more reliable, and faster way to build software, solving a number of problems associated with other build tools, especially including the classic and ubiquitous make itself. Back in 2006, when Madagascar became an opensource project, SourceForge was the dominant platform for such projects. Since then, it has remained a highly useful resource but has lost its popularity to GitHub. Madagascar developers have not yet seen a compelling need to migrate the Madagascar repository from SourceForge to GitHub or to switch from Subversion (SVN) to Git, but will keep all options open. Wednesday, December 3. 2014TX AMO
Another old paper is added to the collection of reproducible documents:
The time and space formulation of azimuth moveout Azimuth moveout (AMO) transforms 3D prestack seismic data from one common azimuth and offset to different azimuths and offsets. AMO in the timespace domain is represented by a threedimensional integral operator. The operator components are the summation path, the weighting function, and the aperture. To determine the summation path and the weighting function, we derive the AMO operator by cascading dip moveout (DMO) and inverse DMO for different azimuths in the timespace domain. To evaluate the aperture, we apply a geometric approach, defining AMO as the result of cascading prestack migration (inversion) and modeling. The aperture limitations provide a consistent description of AMO for small azimuth rotations (including zero) and justify the economic efficiency of the method. Monday, December 1. 2014Program of the month: sfbin
sfbin bins traces with irregular spatial sampling to a regularly sampled 3D cube.
The following example from sep/precon/cube shows the output of binning a commonoffset seismic cube which corresponds to the following distribution of common midpoints Optionally, sfbin can also output the fold map (using fold= parameter). The fold map shows the number of input traces in each output bin. Parameters that control output grid sampling are nx=, dx=, x0= (for the second axis), ny=, dy=, y0= (for the third axis). Alternatively, one can specify the range values xmin=, xmax=, ymin=, ymax=. By default, the range is determined from the input trace coordinates. The input trace coordinates can be specified in an auxiliary trace header file (head= parameter), where x and y coordinates are given in keys number xkey= and ykey= (0 and 1 by default). By default, nearestneighbor binning is applied. Alternatively, it is possible to use median binning by specifying interp=0 or bilinearinterpolation binning by specifying interp=2. BY default, the output values are normalized by the fold. To switch foldnormalization off, use norm=n. 10 previous programs of the month:Wednesday, November 26. 2014Madagascar Virtual Machine Released
As an alternative to installing Madagascar, you can now run a Crunchbang (Debian) virtual machine (VM) with it preinstalled. Just download, unzip, and run the file with Oracle VirtualBox (free software). Detailed instructions for running the VM for the first time or installing VirtualBox can be found in the readme.
Downloads: README.txt MadagascarVM.zip (~3.0 GB) MadagascarVM.7z (~2.1 GB, but requires 7zip to unpack) Wednesday, November 19. 2014Talitrus saltator
In the excellent reproducible science tutorial at SciPy2014, a reproducible data processing example involved segmenting the eye in an image of Talitrus saltator.
The example is reproduced, with modifications, in rsf/tutorials/talitrus. Madagascar users are encouraged to try improving the results. Thursday, November 13. 2014Madagascar school in Harbin
A Madagascar school will take place on January 78, 2015, in Harbin, China, and will be hosted by the Harbin Institute of Technology (HIT) in conjunction with the International Workshop on Mathematical Geophysics.
More information will be available soon on the school webpage. Wednesday, November 12. 2014Program of the month: sfthreshold
sfthreshold filters the input by soft thresholding (shrinkage).
Soft thresholding is a pointbypoint operation, which can be described mathematically as $\begin{array}{cc}\phantom{\rule{6.0em}{0ex}}& {T}_{\mu}\left[u\right]=\{\begin{array}{ccc}\hfill u\mu \phantom{\rule{0.167em}{0ex}}\text{sign}\left(u\right)& \hfill \phantom{\rule{1.00em}{0ex}}\hfill & \text{if}\phantom{\rule{0.278em}{0ex}}\leftu\right>\mu \hfill \\ \hfill 0& \hfill \phantom{\rule{1.00em}{0ex}}\hfill & \text{if}\phantom{\rule{0.278em}{0ex}}\leftu\right\le \mu \hfill \end{array}\phantom{\}}\hfill \end{array}$ Soft thresholding was analyzed by Donoho (1995) and became particularly popular thanks to the iterative shrinkagethresholding algorithm by Daubechies et al. (2004). Donoho, D. L. (1995). Denoising by softthresholding. Information Theory, IEEE Transactions on, 41(3), 613627. Daubechies, I., Defrise, M., & De Mol, C. (2004). An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Communications on pure and applied mathematics, 57(11), 14131457. The following example from tccs/seislet/lena shows an image (Seismic Lena) and its reconstruction after soft thresholding in the seislet domain using 5% thresholding (pclip=5). sfthreshold uses percentage parameter pclip= to set thresholding at the corresponding quantile of the data values. To do soft or hard thresholding with a fixed threshold, use sfthr. An alternative thresholdinglike operation is provided by sfsharpen. 10 previous programs of the month:Seismic data analysis using SSWT
A new paper is added to the collection of reproducible documents:
Timefrequency analysis of seismic data using synchrosqueezing wavelet transform Timefrequency (TF) decomposition is used for characterizing the nonstationary relation between time and instantaneous frequency, which is very important in the processing and interpretation of seismic data. The conventional timefrequency analysis approaches suffer from the contradiction between time resolution and frequency resolution. A new timefrequency analysis approach is proposed based on the synchrosqueezing wavelet transform (SSWT). The SSWT is an empiricalmodedecompositionlike tool but uses a different approach in constructing the components. With the help of the synchrosqueezing techniques, the SSWT can obtain obvious higher time and frequency resolution. Synthetic examples show that the SSWT based TF analysis can exactly capture the variable frequency components. Field data tests show the potential of the proposed approach in detecting anomalies of highfrequency attenuation and detecting the deeplayer weak signal.
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