Features

Invertible data curves:

• Love-wave phase and/or group velocity dispersion curves,

• Rayleigh-wave phase and/or group velocity dispersion curves,

• Rayleigh-wave ellipticity (experimental).

Installation

The recommended way to install evodcinv and all its dependencies is through the Python Package Index:

pip install evodcinv --user

Otherwise, clone and extract the package, then run from the package location:

pip install . --user

To test the integrity of the installed package, check out this repository and run:

pytest

Documentation

Refer to the online documentation for detailed description of the API and examples.

Alternatively, the documentation can be built using Sphinx:

pip install -r doc/requirements.txt
sphinx-build -b html doc/source doc/build

Usage

The following example inverts a Rayleigh-wave phase velocity dispersion curve (fundamental mode).

from evodcinv import EarthModel, Layer, Curve

# Initialize model
model = EarthModel()

# Build model search boundaries from top to bottom
# First argument is the bounds of layer's thickness [km]
# Second argument is the bounds of layer's S-wave velocity [km/s]
model.add(Layer([0.001, 0.1], [0.1, 3.0]))
model.add(Layer([0.001, 0.1], [0.1, 3.0]))

# Configure model
model.configure(
    optimizer="cpso",  # Evolutionary algorithm
    misfit="rmse",  # Misfit function type
    optimizer_args={
        "popsize": 10,  # Population size
        "maxiter": 100,  # Number of iterations
        "workers": -1,  # Number of cores
        "seed": 0,
    },
)

# Define the dispersion curves to invert
# period and velocity are assumed to be data arrays
curves = [Curve(period, velocity, 0, "rayleigh", "phase")]

# Run inversion
res = model.invert(curves)
print(res)

Expected output:

--------------------------------------------------------------------------------
Best model out of 501 models (1 run)

Velocity model                                    Model parameters
----------------------------------------          ------------------------------
         d        vp        vs       rho                   d        vs        nu
      [km]    [km/s]    [km/s]   [g/cm3]                [km]    [km/s]       [-]
----------------------------------------          ------------------------------
    0.0298    0.5033    0.2055    2.0000              0.0298    0.2055    0.4000
    1.0000    2.0586    0.9935    2.0000                   -    0.9935    0.3482
----------------------------------------          ------------------------------

Number of layers: 2
Number of parameters: 5
Best model misfit: 0.0038
--------------------------------------------------------------------------------

Contributing

Please refer to the Contributing Guidelines to see how you can help. This project is released with a Code of Conduct which you agree to abide by when contributing.