Jeff Godwin

October 18, 2021 Celebration No comments

A new inductee in the Madagascar Hall of Fame is Jeff Godwin.

You can read Jeff’s story here.

Yang Liu

September 27, 2021 Celebration No comments

A new inductee in the Madagascar Hall of Fame is Yang Liu.

You can read Yang’s story here.

Vladimir Bashkardin

September 23, 2021 Celebration No comments

A new inductee in the Madagascar Hall of Fame is Vladimir Bashkardin.

You can read Vladimir’s story here.

Tutorial on time-frequency analysis

September 15, 2021 Examples No comments

The example in rsf/tutorials/timefreq reproduces the tutorial from Matt Hall on time-frequency decomposition.

The tutorial was published in the June 2018 issue of The Leading Edge.

The Madagascar version was created by Sarah Greer. Madagascar users are encouraged to try improving the results.

Hall of Fame

August 31, 2021 Celebration No comments

A new wiki page, Hall of Fame, honors those individuals who made particularly important contributions to Madagascar and the Madagascar community.

The first two honorees are Nick Vlad and Pengliang Yang.

Please submit nominations for other major contributors.

Online Conference

May 5, 2021 Celebration No comments

The first ever worldwide Madagascar conference will take place on June 21-27, 2021. The participation is free of charge.

The conference program will be announced later. Meanwhile, please indicate the level of your interest in participation by filling a form on the website.

Enhancements to Python interface

April 9, 2021 Systems No comments

Several enhancements have been added to Madagascar’s Python interface.

Behind the scene, temporary files are created, and Madgascar programs run in the usual way, but, for the user, they appears like native Python functions. This way, the full power of Madagascar becomes available to people who prefer to work on data analysis projects in a Python environment.

  • However, there is no good reason to abandon Madagascar’s use of SCons for managing data analysis workflows even when working in a Python framework. Because SConstruct scripts are written in Python, they are easy to adapt for including Python functions in place of command-line instructions. See an example of using Keras with SCons or an example of using PyTorch with SCons.

In deep learning projects, the training data, the neural-network model, and the testing data can be treated as files and handled effectively through SCons workflows while mixing with Madagascar commands and workflows.

  • Plotting with Matplotlib may offer some advanced functionality in comparison with Vplot, such as the possibility of using $\LaTeX$ code in figure labels. It is now possible to use Matplotlib plots in papers reproducible with Madagascar through an application of sfmatplotlib. The figures will be saved in the PDF format and included in reproducible papers in the usual way. See an example.

The main advantage of continuing to use Vplot is the availability of sfvplotdiff, a key tool for reproducibility testing and continuous integration.

Madagascar users are invited to try the new functionality and contribute to its further development.

Diffraction imaging using anisotropic smoothing

February 24, 2021 Documentation No comments

A new paper is added to the collection of reproducible documents: Least-squares diffraction imaging using shaping regularization by anisotropic smoothing

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We use least-squares migration to emphasize edge diffractions. The inverted forward modeling operator is the chain of three operators: Kirchhoff modeling, azimuthal plane-wave destruction and path-summation integral filter. Azimuthal plane-wave destruction removes reflected energy without damaging edge diffraction signatures. Path-summation integral guides the inversion towards probable diffraction locations. We combine sparsity constraints and anisotropic smoothing in the form of shaping regularization to highlight edge diffractions. Anisotropic smoothing enforces continuity along edges. Sparsity constraints emphasize diffractions perpendicular to edges and have a denoising effect. Synthetic and field data examples illustrate the effectiveness of the proposed approach in denoising and highlighting edge diffractions, such as channel edges and faults.

Legislating reproducibility

January 23, 2021 Celebration No comments

American Innovation and Competitiveness Act was adopted unanimously by the U.S. Congress and signed into law by president Obama in January 2017.

The law contains a section called Research Reproducibility and Replication, which asked the Director of the National Science Foundation in agreement with the National Research Council to prepare a report on issues related to research reproducibility and “to make recommendations for improving rigor and transparency in scientific research”.

To fulfill this requirement, a consensus report of the National Academies of Sciences, Engineering, and Medicine was published in 2019. The report is summarized in the special issue of Harvard Data Science Review in December 2020.

Among the recommendations:

All researchers should include a clear, specific, and complete description of how the reported results were reached. Reports should include details appropriate for the type of research, including:

  • a clear description of all methods, instruments, materials, procedures, measurements, and other variables involved in the study;
  • a clear description of the analysis of data and decisions for exclusion of some data or inclusion of other;
  • for results that depend on statistical inference, a description of the analytic decisions and when these decisions were made and whether the study is exploratory or confirmatory;
  • a discussion of the expected constraints on generality, such as which methodological features the authors think could be varied without affecting the result and which must remain constant;
  • reporting of precision or statistical power; and
  • discussion of the uncertainty of the measurements, results, and inferences.

Funding agencies and organizations should consider investing in research and development of open-source, usable tools and infrastructure that support reproducibility for a broad range of studies across different domains in a seamless fashion. Concurrently, investments would be helpful in outreach to inform and train researchers on best practices and how to use these tools.

Journals should consider ways to ensure computational reproducibility for publications that make claims based on computations, to the extent ethically and legally possible.

Full waveform inversion and joint migration inversion with an automatic directional total variation constraint

December 7, 2020 Documentation No comments

A new paper is added to the collection of reproducible documents: Full waveform inversion and joint migration inversion with an automatic directional total variation constraint


As full waveform inversion (FWI) is a non-unique and typically ill-posed inversion problem, it needs proper regularization to avoid cycle-skipping. To reduce the non-linearity of FWI, Joint Migration Inversion (JMI) is proposed as an alternative, explaining the reflection data with decoupled velocity and reflectivity parameters. However, the velocity update may also suffer from being trapped in local minima. To optimally include geologic information, we propose FWI/JMI with directional total variation as an L1-norm regularization on the velocity. We design the directional total variation operator based on the local dip field, instead of ignoring the local structural direction of the subsurface and only using horizontal- and vertical- gradients in the traditional TV. The local dip field is estimated using plane-wave destruction based on a raw reflectivity model, which is usually calculated from the initial velocity model. With two complex synthetic examples, based on the Marmousi model, we demonstrate that the proposed method is much more effective compared to both FWI/JMI without regularization and FWI/JMI with the conventional TV regularization. In the JMI-based example, we also show that L1 directional TV works better than L2 directional Laplacian smoothing. In addition, by comparing these two examples, it can be seen that the impact of regularization is larger for FWI than for JMI, because in JMI the velocity model only explains the propagation effects and, thereby, makes it less sensitive to the details in the velocity model.