dsmpy

Python package for computation of synthetic waveforms in spherically homogeneous transversely isotropic (VTI) media using the Direct Solution Method (DSM; Kawai et al. 2006).
The original DSM softwares can be found on the Github page of UT Global Seismology.

The documentation for dsmpy with several usage examples can be found here.

INSTALLATION

A method that works with mac M1 (thanks to Kurama Okubo, 20/07/2023)

1. Clone the dsmpy repository

conda update --all
git clone https://github.com/seismobassoon/dsmpy

2. Install conda-build in base

conda install conda-build -y

3. Create the dsm conda environment using the environment.yml YAML file:

cd /path/to/dsmpy/
conda env create -n dsmpy-py38 -f environment.yml

4. Activate your environment:

conda activate dsmpy-py38

5. Build your dsmpy:

conda develop -n dsmpy-py38 -b .

5b) OK if your conda cannot do this by reasons XYZ, try this:

python setup.py install

6. Run tests:

pytest

EXAMPLES

1. Running dsmpy using an input file (run on multiple CPUs). A template input file is in <path_of_pydsm_folder>/dsmpy/tests/input_files/template.txt:

sac_files ~/git/dsmpy/tests/sac_files/*T
output_folder ~/git/dsmpy/tests/sac_files
# duration of synthetics (in seconds)
tlen 3276.8
# number of points of frequency-domain synthetics
# minimum period Tmin = tlen / nspc (s)
nspc 256 
# sampling frequency for time-domain synthetics
sampling_hz 20
# prem, ak135
seismic_model prem 
# 0: P-SV+SH, 1: P-SV, 2: SH (default: 0)
mode 0
# 0: quiet, 1: talkative, 2: debug (default: 0)
verbose 0

To run this input file on 2 CPUs:

1. open a Terminal
2. change the current directory to the dsmpy directory
3. paste:

mpiexec -n 2 python pydsm/main.py tests/input_files/template.txt

2. Running dsmpy from a python script. Below is an example of python script using dsmpy to compute synthetics:

from dsmpy import dsm, seismicmodel
from dsmpy.event import Event
from dsmpy.station import Station
from dsmpy.utils.cmtcatalog import read_catalog
import matplotlib.pyplot as plt

# load gcmt catalog
catalog = read_catalog()
# get event from catalog
event = Event.event_from_catalog(
    catalog, '200707211534A')
# define station FCC
stations = [
    Station(
        name='FCC', network='CN',
        latitude=58.7592, longitude=-94.0884), 
    ]
# load (anisotropic) PREM model
seismic_model = seismicmodel.SeismicModel.prem()
tlen = 3276.8 # duration of synthetics (s)
nspc = 256 # number of points in frequency domain
sampling_hz = 20 # sampling frequency for sythetics
# create input parameters for pydsm
input = dsm.PyDSMInput.input_from_arrays(
    event, stations, seismic_model, tlen, nspc, sampling_hz)
# compute synthetics in frequency domain calling DSM Fortran
output = dsm.compute(input)
output.to_time_domain() # perform inverse FFT
output.filter(freq=0.04) # apply a 25 seconds low-pass filter
us = output.us # synthetics. us.shape = (3,nr,tlen)
ts = output.ts # time points [0, tlen]
# brackets can be used to access component and station
u_Z_FCC = output['Z', 'FCC_CN']
# to plot a three-component record section, use
output.plot()
plt.show()
# to write synthetics to SAC files, use
output.write(root_path='.', format='sac')

3. Running dsmpy using a (Fortran) DSM input file. Methods are provided to run dsmpy using an input file for the (Fortran) DSM:

from pydsm import dsm, rootdsm_sh
parameter_file = rootdsm_sh + 'AK135_SH.inf'
inputs = dsm.PyDSMInput.input_from_file(parameter_file, mode=2)
outputs = dsm.compute(inputs, mode=2)
outputs.to_time_domain()
us = outputs.us    # us.shape = (3,nr,tlen)
ts = outputs.ts    # len(ts) = tlen
stations = outputs.stations        # len(stations) = nr
components = outputs.components    # len(components) = 3