disba is a computationally efficient Python library for the modeling of surface wave dispersion that implements a subset of codes from Computer Programs in Seismology (CPS) in Python compiled just-in-time with numba. Such implementation alleviates the usual prerequisite for a Fortran compiler needed by other libraries also based on CPS (e.g. pysurf96, srfpython and PyLayeredModel) which often leads to further installation troubleshooting, especially on Windows platform.

disba aims to be lightweight and portable without compromising on the performance. For instance, it yields similar speed compared to CPS's surf96 program compiled with f2py for Rayleigh-wave but is significantly faster for Love-wave with increasing number of layers. disba also implements the fast delta matrix algorithm for Rayleigh-wave which, albeit ironically slower, is more robust and handles better reversion of phase velocity caused by low velocity zones.

Forward modeling:

• Compute Rayleigh-wave phase or group dispersion curves using Dunkin's matrix or fast delta matrix algorithms,
• Compute Love-wave phase or group dispersion curves using Thomson-Haskell method,
• Compute Rayleigh-wave ellipticity.

Eigenfunctions and sensitivity kernels:

• Compute Rayleigh- and Love- wave eigenfunctions,
• Compute Rayleigh- and Love- wave phase or group velocity, and Rayleigh-wave ellipticity sensitivity kernels with respect to layer thickness, P- and S- wave velocities, and density.

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

pip install disba[full] --user

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

pip install .[full] --user

The following example computes the Rayleigh- and Love- wave phase velocity dispersion curves for the 3 first modes.

import numpy
from disba import PhaseDispersion

# Velocity model
# thickness, Vp, Vs, density
# km, km/s, km/s, g/cm3
velocity_model = numpy.array([
   [10.0, 7.00, 3.50, 2.00],
   [10.0, 6.80, 3.40, 2.00],
   [10.0, 7.00, 3.50, 2.00],
   [10.0, 7.60, 3.80, 2.00],
   [10.0, 8.40, 4.20, 2.00],
   [10.0, 9.00, 4.50, 2.00],
   [10.0, 9.40, 4.70, 2.00],
   [10.0, 9.60, 4.80, 2.00],
   [10.0, 9.50, 4.75, 2.00],
])

# Periods must be sorted starting with low periods
t = numpy.logspace(0.0, 3.0, 100)

# Compute the 3 first Rayleigh- and Love- wave modal dispersion curves
# Fundamental mode corresponds to mode 0
pd = PhaseDispersion(*velocity_model.T)
cpr = [pd(t, mode=i, wave="rayleigh") for i in range(3)]
cpl = [pd(t, mode=i, wave="love") for i in range(3)]

# pd returns a namedtuple (period, velocity, mode, wave, type)

Likewise, GroupDispersion can be used for group velocity.

disba's API is consistent across all its classes which are initialized and called in the same fashion. Thus, eigenfunctions are calculated as follow:

from disba import EigenFunction

eigf = EigenFunction(*velocity_model.T)
eigr = eigf(20.0, mode=0, wave="rayleigh")
eigl = eigf(20.0, mode=0, wave="love")

# eigf returns a namedtuple
#  - (depth, ur, uz, tz, tr, period, mode) for Rayleigh-wave
#  - (depth, uu, tt, period, mode) for Love-wave

Phase velocity sensitivity kernels (GroupSensitivity for group velocity):

from disba import PhaseSensitivity

ps = PhaseSensitivity(*velocity_model.T)
parameters = ["thickness", "velocity_p", "velocity_s", "density"]
skr = [ps(20.0, mode=0, wave="rayleigh", parameter=parameter) for parameter in parameters]
skl = [ps(20.0, mode=0, wave="love", parameter=parameter) for parameter in parameters]

# ps returns a namedtuple (depth, kernel, period, velocity, mode,wave, type, parameter)

Ellipticity and ellipticity sensitivity kernels:

from disba import Ellipticity, EllipticitySensitivity

ell = Ellipticity(*velocity_model.T)
rel = ell(t, mode=0)

# ell returns a namedtuple (period, ellipticity, mode)

es = EllipticitySensitivity(*velocity_model.T)
ek = [es(20.0, mode=0, parameter=parameter) for parameter in parameters]

# es returns a namedtuple (depth, kernel, period, velocity, mode,wave, type, parameter)

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.