VINEDA -- Volcanic Infrasound Explosions Detector Algorithm

In this repository, we include the code implementation of a multi-step detection algorithm for infrasonic explosions. The algorithm is fully described in the following manuscript:

Vineda: Volcanic Infrasound Explosions Detector Algorithm. Frontiers in Earth Science (2019). Bueno, A., Diaz-Moreno, A., Alvarez, I., De la Torre, A., Lamb, O.D., Zuccarello, L. and De Angelis, S.

If used as part of any research work or data processing pipeline, we would appreciate if the work is cited.

Installation & Features (Python)

• VINEDA runs entirely in Python (2.7 and 3.0) and Matlab 2018. In Python, the library can be imported as an independent Python package, or interfaced with conda / docker environments.

• Dependencies:

  • Numpy >= 1.10.0
  • Scipy >= 0.19.1
  • Obspy >= 1.1.0

How to use

A working example is provided with an infrasound signal in ".mat" format. Additionally, from a terminal, just run.

python vineda.py

Notice that the implementation of the algorithm is defined within detect_explosions() function, with the arguments:

• x: A numpy array containing the data samples. From an obspy stream, we can forward a numpy array as stream[0].data
• fs: Original sampling rate of the infrasound signal (Hz)
• flow: Lower cut-off frequency of the bandpass filter (Hz)
• fhigh: Upper cut-off frequency of the bandpass filter (Hz)
• nfb: Number of frequency bands.
• dmin: Minimum duration of the explosions to be detected (s)
• dmax: Maximum duration of the explosions to be detected (s)
• ndb: Number of duration bands.
• beta: Factor to reduce non-stationary noises.

As a result, a tuple containing the CF and the sampling frequency is returned. VINEDA can be incorporated in any data processing workflow by calling this function detect_explosions() with proper parameter configuration. For a practical example, please refer to the jupyter implementation.