extract_power {WaverideR}

Extract power from a wavelet spectra

Description

Extracts the spectral power from a wavelet spectra in the depth domain using a traced period and boundaries surround the traced period. The extraction of spectral is useful for cyclostratigraphic studies because the spectral power of an astronomical cycle is modulated by higher order astronomical cycles. The spectral power record from an astronomical cycle can thus be used as a proxy for amplitude modulating cycles The traced period result from the track_period_wavelet function with boundaries is used to extract spectral power in the depth domain from a wavelet spectra.

Usage

extract_power(
  completed_series = NULL,
  wavelet = NULL,
  period_up = 1.2,
  period_down = 0.8,
  tracked_cycle_period = NULL,
  extract_cycle_power = NULL
)

Arguments

completed_series

Traced period result from the track_period_wavelet function completed using the completed_series. The input can be pre-smoothed using the the loess_auto function.

wavelet

Wavelet object created using the analyze_wavelet function.

period_up

Upper period as a factor of the to be extracted power Default=1.2.

period_down

Lower period as a factor of the to be extracted power Default=0.8.

tracked_cycle_period

Period of the tracked cycle (make sure that tracked_cycle_period) and extract_cycle_power) are of the same unit (either depth or time domain).

extract_cycle_power

Period of the cycle for which the power will be extracted (make sure that extract_cycle_power) and tracked_cycle_period) are of the same unit (either depth or time domain).

Value

Returns a matrix with 3 columns. The first column is depth/time. The second column is extracted power. The third column is extracted power/total power.

Author(s)

Code based on the reconstruct function of the 'WaveletComp' R package which is based on the wavelet 'MATLAB' code written by Christopher Torrence and Gibert P. Compo. The assignment of the standard deviation of the uncertainty of the wavelet is based on the work of Gabor (1946) and Russell et al., (2016) The functionality of this function is is inspired by the integratePower function of the 'astrochron' R package.

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

Routines for astrochronologic testing, astronomical time scale construction, and time series analysis <doi:10.1016/j.earscirev.2018.11.015>

Examples

#Extract the power of the 405 kyr eccentricity cycle from the the magnetic
# susceptibility data set of De pas et al., (2018)
#Perform the CWT on the magnetic susceptibility data set of Pas et al., (2018)

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

#Track the 405 kyr eccentricity cycle in a wavelet spectra

#mag_track <- track_period_wavelet(astro_cycle = 405,
#                                   wavelet=mag_wt,
#                                   n.levels = 100,
#                                   periodlab = "Period (metres)",
#                                   x_lab = "depth (metres)")

#Instead of tracking, the tracked solution data set mag_track_solution
#is used
mag_track <- mag_track_solution

mag_track_complete <- completed_series(
  wavelet = mag_wt,
  tracked_curve = mag_track,
  period_up = 1.2,
  period_down = 0.8,
  extrapolate = TRUE,
  genplot = FALSE
)

#Smooth the completed tracking of the 405 kyr eccentricity cycle in the wavelet spectra

mag_track_complete <- loess_auto(time_series = mag_track_complete,
genplot = FALSE, print_span = FALSE)

#extract the spectral power of the 405 kyr eccentricity cycle
mag_power <- extract_power(
completed_series = mag_track_complete,
wavelet = mag_wt,
period_up = 1.2,
period_down = 0.8,
tracked_cycle_period = 405,
extract_cycle_power = 405
)