Last updated on August 12th 2016.

Note

The following links shows the example project as it should be just before step 9. You can use this to check your progress or restart the tutorial at this very point.

After Step 9: Defining a new Iteration

10. Data PreprocessingΒΆ

Note

You do not actually need to do this for the tutorial as the proprocessed data has already been downloaded but this will be required for real inversions.

Data preprocessing is an essential step if one wants to compare data and seismograms. It serves several purposes:

  • Ensure a similar spectral bandwidth between observed and synthetic data to enable a meaningful comparison.
  • Removing the instrument response and converting the data to the same units used for the synthetics (usually m/s).
  • Removal of any linear trends and static offset.
  • Interpolations to sample observed and synthetic data at exactly the same points in time.

The goal of the preprocessing within LASIF is to create data that is directly comparable to simulated data without any more processing.

While the raw unprocessed data are stored in a folder {{EVENT}}/raw, the preprocessed data will be stored in a separate directory within each event, identified via the name (values are valid for the tutorial):

preprocessed_hp_0.01000_lp_0.02500_npts_2000_dt_0.300000

Or in Python terms:

highpass = 1.0 / 100.0
lowpass = 1.0 / 40.0
npts = 2000
dt = 0.3

processing_tag = ("preprocessed_hp_{highpass:.5f}_lp_{lowpass:.5f}_"
                  "npts_{npts}_dt_{dt:5f}").format(highpass=highpass, lowpass=lowpass,
                                                   npts=npts, dt=dt)

If you feel that additional identifiers are needed to uniquely identify the applied processing (in the limited setting of being useful for the here performed waveform inversion) please contact the LASIF developers.

Although in principle you can use any processing tool you like, the simplest option is probably to make use of LASIF‘s built-in preprocessing. Using it is trivial: just launch the preprocess_data command together with the iteration name.

$ lasif preprocess_data 1

or (this is faster as -n determines the number of processors it will run on):

$ mpirun -n 4 lasif preprocess_data 1

This will start a fully parallelized preprocessing run for all data required for the specified iteration. If you repeat the command, it will only process data not already processed. An advantage is that you can cancel the processing at any time and then later on just execute the command again to continue where you left off. This usually only needs to be done every couple of iterations when you decide to go to higher frequencies or add new data.

The preprocessed data will automatically be put in the correct folder.

Note

You can use any processing tool you want, but you have to adhere to the directory structure – otherwise LASIF will not be able to work with the data. It is also important that the processed filenames are identical to the unprocessed ones.