MSNoise 1.5 released !

Dear community & friends,

About 1 year after the last major release (MSNoise 1.4) we are proud to announce the new MSNoise 1.5. It is a major release, with a massive amount of work since the last one: in GitHub numbers , it’s over 120 commits and over 2500 lines of code and documentation changed or added!

MSNoise 1.5 introduces a series of new features :

  • We have started to move core math functions to ObsPy, currently the only one ready is linear_regression, a function I wrote to remove the dependency to statsmodels, required to move mwcs to ObsPy later.
  • The preprocessing routine has been isolated, rewritten and optimized. It is now a standalone script, callable by plugins. It returns a Stream object with all the data needed for the analysis.
  • This change in preprocessing was done mostly to allow cross-component, auto- correlation and cross-correlation, with or without rotation, to be done with the same code. CC, SC and AC are now supported in MSNoise with proper whitening (possible to disable spectral whitening for specific cases).
  • This documentation is now available in PDF too (easier for offline usage) and it also includes a new tutorial for setting up the MySQL server and Workbench.
  • Last but not least: MSNoise is “tested” automatically on Linux (thanks to TravisCI) & Windows (thanks to Appveyor), for Python versions 2.7 and 3.5. With MSNoise 1.5 we also added the MacOSX tests on TravisCI. With these tests, we can guarantee MSNoise works on different platforms and Anaconda (or miniconda) python versions.

This version has benefited from outputs/ideas/pull requests/questions from several users/friends (listed alphabetically):

  • Raphael De Plaen
  • Clare Donaldson
  • Robert Green
  • Aurelien Mordret
  • Lukas Preiswerk
  • The participants to the NERC MSNoise Liverpool Workshop in January 2017
  • all others (don’t be mad 🙂 )

Thanks to all for using MSNoise, and please, let us know why/how you use it (and please cite it!)!

To date, we found/are aware of 25 publications using MSNoise ! That’s the best validation of our project ever ! See the full list on the MSNoise website.

Thomas, Corentin and others

PS: if you use MSNoise for your research and prepare publications, please consider citing it:

Lecocq, T., C. Caudron, et F. Brenguier (2014), MSNoise, a Python Package for Monitoring Seismic Velocity Changes Using Ambient Seismic Noise, Seismological Research Letters, 85(3), 715‑726, doi:10.1785/0220130073.