Madagascar is an open-source software package for multidimensional data analysis and reproducible computational experiments. Its mission is to provide
- a convenient and powerful environment
- a convenient technology transfer tool
for researchers working with digital image and data processing in geophysics and related fields. Technology developed using the Madagascar project management system is transferred in the form of recorded processing histories, which become "computational recipes" to be verified, exchanged, and modified by users of the system.
Madagascar is a modern package. Started in 2003 and publicly released in 2006, it was developed almost entirely from scratch. Being a relatively new package, it follows modern software engineering practices such as module encapsulation and test-driven development. A rapid development of a project of this scope (more than 1,000 main programs and more than 5,000 tests) would not be possible without standing on the shoulders of giants and learning from the 30 years of previous experience in open packages such as SEPlib and Seismic Unix. We have borrowed and reimplemented functionality and ideas from these other packages.
Madagascar is a test-driven package. Test-driven development is not only an agile software programming practice but also a way of bringing scientific foundation to geophysical research that involves numerical experiments. Bringing reproducibility and peer review, the backbone of any real science, to the field of computational geophysics is the main motivation for Madagascar development. The package consists of two levels: low-level main programs (typically developed in the C programming language and working as data filters) and high-level processing flows (described using the Python programming language) that combine main programs and completely document data processing histories for testing and reproducibility. Experience shows that high-level programming is easily mastered even by beginning students who have no previous programming experience.
Madagascar is an open-source package. It is distributed under the standard GPL open-source license, which places no restriction on the usage and modification of the code. Moreover, access to modifying the source repository is not controlled by one organization but shared equally among different developers. This enables an open collaboration among different groups spread all over the world, in the true spirit of the open-source movement.
Madagascar uses a simple, flexible, and universal data format that can handle very large datasets but is not tied specifically to seismic data or data of any other particular kind. This "regularly sampled" format is borrowed from the traditional SEPlib. A universal data format allows us to share general-purpose data processing tools with scientists from other disciplines such as petroleum engineers working on large-scale reservoir simulations.
- A new paper is added to the collection of reproducible documents: A probabilistic approach to seismic diffraction imaging We propose and demonstrate a probabilistic method for imaging seismic diffr...
- 2022-04-29 23:39:15
- A new paper is added to the collection of reproducible documents: Noniterative f-x-y streaming prediction filtering for random noise attenuation on seismic data Random noise is unavoidable in seism...
- 2022-04-25 13:54:48
- A new paper is added to the collection of reproducible documents: Seismic data interpolation using streaming prediction filter in the frequency domain Surface conditions and economic factors restri...
- 2022-04-25 13:49:25
- A new paper is added to the collection of reproducible documents: Seismic data interpolation without iteration using t-x-y streaming prediction filter with varying smoothness Although there is an i...
- 2022-04-25 13:37:26
- A new paper is added to the collection of reproducible documents: Nonstationary pattern-based signal-noise separation using adaptive prediction-error filter Complex field conditions always create d...
- 2022-04-25 12:30:48
- A new paper is added to the collection of reproducible documents: Automatic channel detection using deep learning We propose a method based on an encoder-decoder convolutional neural network for au...
- 2022-04-24 17:50:55
- A new paper is added to the collection of reproducible documents: Quantifying and correcting residual azimuthal anisotropic moveout in image gathers using dynamic time warping We propose and demons...
- 2021-10-25 21:15:44