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Models average spectral power based curves based on a red-noise signal generated using the characteristics of an input signal.

Source: R/model_red_noise_wt.R
model_red_noise_wt.Rd

The model_red_noise_wt function is used to generate average spectral power curves based on and input signal and set wavelet settings.

Usage

model_red_noise_wt(
  wavelet = NULL,
  n_simulations = NULL,
  run_multicore = FALSE,
  n_cores = NULL,
  verbose = FALSE
)

Arguments

wavelet

Wavelet object, the output of analyse_wavelet. Its record (x, y), period range, dj and omega_nr are used: every simulation is red (AR1) noise with the mean, standard deviation, length and (randomly perturbed) lag-1 autocorrelation of the record, analysed with the same wavelet settings. Required (Default=NULL).

n_simulations

Number of red-noise simulations (integer, > 0; e.g. 1000 for stable percentiles); one output column per simulation. Required (Default=NULL).

run_multicore

Run the simulations in parallel on several cores (logical, TRUE or FALSE). Results do not depend on it. Default=FALSE.

n_cores

Number of cores used when run_multicore = TRUE (integer or NULL). NULL gives an automatic, safe number (physical cores minus one, at most 4, limited by the free memory); change this default with options(waverider.n_cores = ...). A value > 1 also switches on parallel computing. Results do not depend on the number of cores. Default=NULL.

verbose

Print the interpolation interval of the record and show a progress bar of the simulations (logical, TRUE or FALSE). Default=FALSE.

Value

Returns a matrix in which each column represents the average spectral power resulting from a red-noise run.

References

Angi Roesch and Harald Schmidbauer (2018). WaveletComp: Computational Wavelet Analysis. R package version 1.1. https://CRAN.R-project.org/package=WaveletComp

Gouhier TC, Grinsted A, Simko V (2021). R package biwavelet: Conduct Univariate and Bivariate Wavelet Analyses. (Version 0.20.21), https://github.com/tgouhier/biwavelet

Torrence, C., and G. P. Compo. 1998. A Practical Guide to Wavelet Analysis. Bulletin of the American Meteorological Society 79:61-78. https://paos.colorado.edu/research/wavelets/bams_79_01_0061.pdf

Morlet, Jean, Georges Arens, Eliane Fourgeau, and Dominique Glard. "Wave propagation and sampling theory—Part I: Complex signal and scattering in multilayered media. " Geophysics 47, no. 2 (1982): 203-221.

J. Morlet, G. Arens, E. Fourgeau, D. Giard; Wave propagation and sampling theory; Part II, Sampling theory and complex waves. Geophysics 1982 47 (2): 222–236.

Author

Code based on the "analyze.wavelet" function of the 'WaveletComp' R package and "wt" function of the 'biwavelet' R package which are based on the wavelet 'MATLAB' code written by Christopher Torrence and Gibert P. Compo (1998).

Examples

# \donttest{
#'#generate average spectral power curves based on red noise curves which are
# based on the magnetic susceptibility record of the Sullivan core of Pas et al., (2018)

mag_wt <- analyse_wavelet(data = mag,
dj = 1/100,
lowerPeriod = 0.1,
upperPeriod = 254,
verbose = FALSE,
omega_nr = 10)

#increase n_simulations to better define the red noise spectral power curve
mag_wt_red_noise <- model_red_noise_wt(wavelet=mag_wt,
n_simulations=10, # increase number for better constrained results
run_multicore=FALSE,
verbose=FALSE)
# }