inparam.output.yaml

station-wise

list_of_station_groups:
    - global_seismic_network_GSN:
        locations:
            station_file: GSN.txt
            horizontal_x1_x2: LATITUDE_LONGITUDE
            ellipticity: true
            undulated_geometry: true
        wavefields:
            coordinate_frame: RTZ
            medium: SOLID
            channels: [U]
        temporal:
            time_window: FULL
        file_options:
            format: ASCII_STATION
            buffer_size: 1000
            flush: true

Here, you need to set the station(s) that you want the displacement or velocity recorded at. list_of_station_groups can take as many networks (groups of stations) as you want.

We tend to keep global_seismic_network_GSN, as it is a useful set of stations all around the world with good data, for testing purposes. If you are doing something specific though, like array beamforming, you will want to add your own networks.

station_file is the name of the text file within the input folder that contains the station coordinates. Subsequent options like horizontal_x1_x2 and vertical_x3 set what the columns in the station_file text file correspond to. For the GSN, these are LATITUDE_LONGITUDE and DEPTH (so in your file GSN.txt, the columns are name, network, latitude, longitude, elevation, depth – note that elevation is not used in AxiSEM3D but it is there so that you can swap files with SPECFEM).

You also need to set the output coordinates using coordinate_frame and channels. We suggest that you leave these as RTZ and [U] (where [U] = [U1, U2, U3]) for our purposes.

The big choice that you need to make is whether to save your outputs as text files (ASCII_STATION or ASCII_CHANNEL depending on what you’re doing), or netcdf files (netcdf). netcdf is a lot more efficient, but a little harder to use, especially if you have not used this file type before. ASCII has the advantage that you can just open the output and look at whether it makes sense (if the ground velocity is 10e10 m/s after 10 seconds, it is probably wrong…) but it is a lot less space-efficient.

In the processing examples, we will stick to ASCII for ease of use but if you are doing more complex runs or lots of them we would suggest using NetCDF.

element-wise

list_of_element_groups: []