swavepy 0.0.2

seismic wave modelling

SWavePy - Seismic Wave Propagation Simulation

Usage

Installing with pip

pip install swavepy

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Original tutorial taken from Nienke Blom, see this link. Updated by Deborah Wehner for use in Cambridge practicals, and updated again here to ensure animated plots work properly, and compiled into a pip-installable module.

Changes made:

• Plotting code rewritten - ensuring that animated plots work properly in updated binder environments
• Small changes to instructions to reflect this
• Branch version which turns the repo into a pip package, for use on colab

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Basic seismic wave propagation code for teaching purposes (python based). With this code, you can:

• run elastic seismic wavefield simulations
• select data windows
• compute sensitivity kernels

Visualisation functionality

At each stage, visualisation functionality is included.

Visualising seismograms

If (after a forward simulation) a seismogram has been attached to a receiver, this can be visualised with:

rec.plot_seismogram()

Visualising window picks

Equally, window picks (and the resulting adjoint sources) can be visualised with

pick = {}
pick['component'] = ['x']   # 'x' or 'z'
pick['times'] = [3.5, 7.5]  # seconds
print('window goes from {} to {} s'.format(pick['times'][0], pick['times'][1]))

receivers_Pwave = waveprop.make_adjoint_source(receivers, pick, plot=3)

Visualising sensitivity kernels

Once sensitivity kernels have been computed, these can be visualised in different parametrisations and as absolute, or relative to a background model. For simple direct waves, it can be instructive to compare the sensitivity kernel to the Fresnel zone. This Fresnel zone can be visualised by setting plot_Fresnel_zone to True.

kernels..plot_kernels(
    parametrisation='rhovsvp' # could be 'rhomulambda'
    mode='relative',          # could be 'absolute'
    model=model,              # necessary for all kernels other than absolute rhomulambda
    source=src, receiver=receivers[0], 
    plot_Fresnel_zone=True,
)