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Helper functions

JUDI provides numerous helper and utility functions need for seismic modeling and inversion.

Ricker wavelet

Create a 1D Ricker wavelet:

JUDI.ricker_wavelet — Method
source(tmax, dt, f0)

Create seismic Ricker wavelet of length tmax (in milliseconds) with sampling interval dt (in milliseonds)
and central frequency f0 (in kHz).

source

Compute CFL time stepping interval

Calculate the time stepping interval based on the CFL condition

calculate_dt

Compute number of computational time steps

Estimate the number of computational time steps. Required for calculating the dimensions of the matrix-free linear modeling operators:

JUDI.get_computational_nt — Function
get_computational_nt(srcGeometry, recGeoemtry, model; dt=nothing)

Estimate the number of computational time steps. Required for calculating the dimensions
of the matrix-free linear modeling operators. srcGeometry and recGeometry are source
and receiver geometries of type Geometry and model is the model structure of type
Model.

source
get_computational_nt(Geoemtry, model; dt=nothing)

Estimate the number of computational time steps. Required for calculating the dimensions
of the matrix-free linear modeling operators. srcGeometry and recGeometry are source
and receiver geometries of type Geometry and model is the model structure of type
Model.

source

Set up 3D acquisition grid

Helper function to create a regular acquisition grid for a 3D survey.

setup_3D_grid

Data interpolation

Time interpolation for source/receiver data using splines. For modeling, the data is interpolated automatically onto the computational time axis, so generally, these functions are not needed for users.

JUDI.time_resample — Function
time_resample(data, geometry_in, dt_new)

Resample the input data with sinc interpolation from the current time sampling (geometrtyin) to the new time sampling `dtnew`.

Parameters

  • data: Data to be reampled. If data is a matrix, resamples each column.
  • geometry_in: Geometry on which data is defined.
  • dt_new: New time sampling rate to interpolate onto.
source
time_resample(data, dt_in, dt_new)

Resample the input data with sinc interpolation from the current time sampling dtin to the new time sampling `dtnew`.

Parameters

  • data: Data to be reampled. If data is a matrix, resamples each column.
  • dt_in: Time sampling of input
  • dt_new: New time sampling rate to interpolate onto.
source
time_resample(data, dt_in, geometry_in)

Resample the input data with sinc interpolation from the current time sampling (dtin) to the new time sampling `geometryout`.

Parameters

  • data: Data to be reampled. If data is a matrix, resamples each column.
  • geometry_out: Geometry on which data is to be interpolated.
  • dt_in: Time sampling rate of the data.
source

Generate and sample from frequency distribution

Create a probability distribution with the shape of the source spectrum from which we can draw random frequencies.

JUDI.generate_distribution — Function
generate_distribution(x; src_no=1)

Generates a probability distribution for the discrete input judiVector x.

Parameters

  • x: judiVector. Usualy a source with a single trace per source position.
  • src_no: Index of the source to select out of x
source

We can draw random samples from dist by passing it values between 0 and 1:

# Draw a single random frequency
f = dist(rand(1))

# Draw 10 random frequencies
f = dist(rand(10))

Alternatively, we can use the function:

f = select_frequencies(dist; fmin=0f0, fmax=Inf, nf=1)

to draw nf number of frequencies for a given distribution dist in the frequency range of fmin to fmax (both in kHz).

Read data from out of core container

In the case where a judiVector is out of core (points to a segy file) it is possible to convert it or part of it into an in core judiVecor with the get_data function.

d_ic = get_data(d_ooc, inds)

where inds is either a single index, a list of index or a range of index.