Processing

SeisBandPass

# Seismic.SeisBandPass — Function.

SeisBandPass(d ; <keyword arguments>)

Apply a bandpass filter to a 2D array input. Input and output data is in tx domain. Filter is applied in fx domain.

Arguments

d: Input 2D data array in tx domain.

Keyword arguments

dt=0.001: time sampling interval.
fa=0,fb=0,fc=60,fd=80: corner frequencies in Hz.

Output

d: Filtered 2d data arrayn in tx domain.

Example

```julia julia> d = SeisLinearEvents(d); SeisPlot(d,plot_type="Amplitude",dy=0.004,fmax=125); julia> d_filter = SeisBandPass(d;dt=0.004,fc=15,fd=35); SeisPlot(d_filter,plot_type="Amplitude",dy=0.004,fmax=125);

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SeisDecimate

# Seismic.SeisDecimate — Function.

SeisDecimate(d ; <keyword arguments>)

Decimate a multidimensional array input. Input and output have the same dimension

Arguments

d: Input data. Between 2D and 5D

Keyword arguments

mode=random: Data is decimated randomly. Otherwise it is regularly decimated.
perc=50: percentage of traces to be decimated from original array
incx1=1,incx2=1,incx3=1,incx4=1: traces to decimate in case of regular decimation

Output

out: Decimated data.

Example

```julia julia> d = SeisLinearEvents(d); SeisPlot(d) julia> d_dec = SeisDecimate(d); SeisPlot(d_dec)

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SeisDelay

# Seismic.SeisDelay — Function.

SeisDelay(d ; <keyword arguments>)

Apply a time delay to a 2D array input.

Arguments

d: Input 2D data array in tx domain.

Keyword arguments

delay=0.1: time delay in seconds.
dt=0.001: time sampling

Output

d2: Delayed data in time domain

Example

```julia julia> d = SeisLinearEvents(d); SeisPlot(d); julia> d2 = SeisDelay(d;dt=0.004); SeisPlot(d);

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SeisEnvelope

# Seismic.SeisEnvelope — Function.

SeisEnvelope(d)

Calculate the envelope attribute of an input trace

Arguments

d: Input data.

Output

out: Envelope of input data

Example

```julia julia> d = SeisLinearEvents(d); SeisPlot(d) julia> out = SeisEnvelope(d); SeisPlot(d_dec)

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SeisFKFilter

# Seismic.SeisFKFilter — Function.

SeisFKFilter(d ; <keyword arguments>)

Decimate a multidimensional array input. Input and output have the same dimension

Arguments

d: 2D Input data.

Keyword arguments

dt=0.002: time sampling interval.
dx=10: space sampling interval.
va=-2000,vb=-3000,vc=3000,vd=2000: corner velocities to be filtered

Output

out: Filtered data.

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SeisGain

# Seismic.SeisGain — Function.

SeisGain(d, t; <keyword arguments>)

Gain a group of traces.

Arguments

d::Array{Real,2}: two dimensional data.

Keyword arguments

dt::Real=0.002: sampling interval in secs.
kind::AbstractString="time": if kind="time", gain = t.^a . * exp(-bt); if kind="agc", automatic gain control is applied.
param::Vector{Real}=[2.0,0.0]: if kind="time", param = [a,b]; if kind="agc", param = [agc_gate]
norm::Int=0: norm=0 no normalization; norm=1 normalize each trace by amplitude; norm=2 normalize each trace by rms value/

Output

d::Array{Real, 2}`: gained two dimensional data.

Example

julia> d, extent = SeisHypEvents();
       dout = SeisGain(d, kind="agc", param=[0.05]);
       SeisPlot([d dout], extent);

Credits: Juan I. Sabbione, Aaron Staton, Mauricio D. Sacchi, 2016

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SeisKolmogoroff

# Seismic.SeisKolmogoroff — Function.

SeisKolmogoroff(w)

Kolmogoroff factorization. Transform a wavelet into its minimum phase equivalent.

Arguments

w::Real: input wavelet.

Example

julia> w = Ricker()
julia> wmin = SeisKolmogoroff(w)
julia> plot(w); plot(wmin)

Reference

Claerbout, Jon F., 1976, Fundamentals of geophysical data processing.

McGraw-Hill Inc.

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SeisMWNI

# Seismic.SeisMWNI — Function.

SeisMWNI

Minimum Weighted Norm Interpolation of seismic records.

IN

d_in: input data that can have up to 5 dimensions
dt=0.001 sampling rate along the time axis (in seconds)
fmax=99999. maximum temporal frequency to process.
padt=2 padding to use for the time axis
padx=1 padding to use for the spatial axes
Niter_internal=10 number of internal iterations for Conjugate Gradients
Niter_external=3 number of external iterations for iterative reweighting

OUT

d_out: interpolated data

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SeisPOCS

# Seismic.SeisPOCS — Function.

SeisPOCS

Projection Onto Convex Sets interpolation of seismic records.

IN

d_in: input data that can have up to 5 dimensions
p=1., exponent for thresholding (1 is equivalent to soft thres. high number is equivalent to hard thresholding)
alpha=1 add-back ratio for imputation step. Use 1 for noise free data, and < 1 for denoising of original traces.
dt=0.001 sampling rate along the time axis (in seconds)
fmax=99999. maximum temporal frequency to process.
padt=2 padding to use for the time axis
padx=1 padding to use for the spatial axes
Niter=100 number of iterations

OUT

d_out: interpolated data

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SeisRadonFreqFor

# Seismic.SeisRadonFreqFor — Function.

SeisRadonFreqFor(m, nt; <keyword arguments>)

Transform a tau-p gather to a time-offset gather using a frequency domain forward parabolic or linear Radon operator.

Arguments

m::Array{T<:Real,2}: 2D Radon panel, m[1:ntau,1:np], where ntau is the

number of intercept times and np the number of curvatures or ray parameters.

nt::Int: number of time samples in the data domain.

Keyword arguments

order::AbstractString="parab": "parab" for parabolic transform, "linear"

for linear transform.

dt::Real=0.004: sampling interval in seconds.
h::Vector{Real}=collect(0.0:20.0:1000.0): offset vector; h[1:nh].
href::Real=0.0: reference offset for parabolic Radon Transform. If the

defautl value href=0.0 is given, href is set to max(abs(h)).

p::Vector{Real}=collect(-0.05:0.01:2.2): p[1:np]. If order="parab", p

is a vector of residual moveout ("curvatures") at reference offset href in seconds; if order=linear, p is a vector of ray parameters in s/m.

flow::Real=0.0: minimum frequency in the data in Hz.
fhigh::Real=125.0: maximum frequency in the data in Hz.

Output

d: data synthetized via forward Radon modeling, d[1:nt, 1:nh].

References

Hampson, D., 1986, Inverse velocity stacking for multiple elimination:

Canadian Journal of Exploration Geophysics, 22, 44-55.

Sacchi, M.D. and Ulrych, T.J., 1995, High-resolution velocity gathers and

offset space reconstruction: Geophysics, 60, 1169-1177.

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SeisRadonFreqInv

# Seismic.SeisRadonFreqInv — Function.

SeisRadonFreqInv(d; <keyword arguments>)

Transform a CMP Gather time-offset gather to tau-p gather using a frequency domain inverse parabolic or linear Radon operator via least-squares inversion.

Arguments

d::Array{T<:Real,2}: 2D data, d[1:nt,1:nh], where nt is number of

time samples and nh the number of receivers.

Keyword arguments

order::AbstractString="parab": "parab" for parabolic transform, "linear"

for linear transform.

dt::Real=0.004: sampling interval in seconds.
h::Vector{Real}=collect(0.0:20.0:1000.0): offset vector h[1:nh].
href::Real=0.0: reference offset for parabolic Radon Transform. If the

defautl value href=0.0 is given, href is set to max(abs(h)).

p::Vector{Real}=collect(-0.05:0.01:2.2): p[1:np]. If order="parab", p

is a vector of residual moveout ("curvatures") at reference offset href in seconds; if order=linear, p is a vector of ray parameters in s/m.

flow::Real=0.0: minimum frequency in the data in Hz.
fhigh::Real=125.0: maximum frequency in the data in Hz.
mu::Real=0.001: trade off parameter or damping for the L.S. inversion.

Output

m: inverted Radon panel m[1:ntau, 1:np].

References

Hampson, D., 1986, Inverse velocity stacking for multiple elimination:

Canadian Journal of Exploration Geophysics, 22, 44-55.

Sacchi, M.D. and Ulrych, T.J., 1995, High-resolution velocity gathers and

offset space reconstruction: Geophysics, 60, 1169-1177.

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