fast_matched_filter (FMF)

An efficient seismic matched-filter search for both CPU and GPU architectures. Documentation at https://ebeauce.github.io/FMF_documentation/.

If you use FMF in research to be published, please reference the following article: Beaucé, Eric, W. B. Frank, and Alexey Romanenko (2017). Fast matched-filter (FMF): an efficient seismic matched-filter search for both CPU and GPU architectures. Seismological Research Letters, doi: 10.1785/0220170181

FMF is available at https://github.com/beridel/fast_matched_filter and can be downloaded with:

git clone https://github.com/beridel/fast_matched_filter.git

Required software/hardware

• A C compiler that supports OpenMP (default Mac OS compiler clang does not support OpenMP; gcc can be easily downloaded via homebrew)
• CPU version: either Python (v2.7 or 3.x) or Matlab
• GPU version: Python (v2.7 or 3.x) and a discrete Nvidia graphics card that supports CUDA C with CUDA toolkit installed

Installation

From source

A simple make + whichever implementation does the trick. Possible make commands are:

make all
make python_cpu
make python_gpu
make python_remove_data_mean_short_P_gpu
make matlab

NB: Matlab compiles via mex, which needs to be setup before running. Any compiler can be chosen during the setup of mex, because it will be bypassed by the CC environment variable in the Makefile. Therefore CC must be set to an OpenMP-compatible compiler.

Using pip

Installation as a Python module is possible via pip (which supports clean uninstalling):

python setup.py build_ext
pip install .

or simply:

pip install git+https://github.com/beridel/fast_matched_filter

Running

Python: Both CPU and GPU versions are called with the matched_filter function.
If FMF was installed with pip:

    import fast_matched_filter as fmf

Matlab: The CPU version is called with the fast_matched_filter function