sum_power_sedrate {WaverideR}

Calculate sum of maximum spectral power for sedimentation rates for a wavelet spectra

Description

The sum_power_sedrate function is used calculate the sum of maximum spectral power for a list of astronomical cycles from a wavelet spectra. The data is first normalized using the average spectral power curves for a given percentile based on results of the model_red_noise_wt function

Usage

sum_power_sedrate(
  red_noise = NULL,
  wavelet = NULL,
  percentile = NULL,
  sedrate_low = NULL,
  sedrate_high = NULL,
  spacing = NULL,
  cycles = c(NULL),
  x_lab = "depth",
  y_lab = "sedrate",
  run_multicore = FALSE,
  genplot = FALSE,
  plot_res = 1,
  keep_editable = FALSE,
  palette_name = "rainbow",
  color_brewer = "grDevices",
  verbose = FALSE
)

Arguments

red_noise

Red noise curves generated using the model_red_noise_wt function

wavelet

Wavelet object created using the analyze_wavelet function

percentile

Percentile value (0-1) of the rednoise runs which is used to normalize the data for. To account for the distribution/distortion of the spectral power distribution based on the analytical technique and random red-noise the data is normalized against a percentile based red-noise curve which is the results of the 'model_red_noise_wt modelling runs.

sedrate_low

Minimum sedimentation rate (cm/kyr)for which the sum of maximum spectral power is calculated for.

sedrate_high

Maximum sedimentation rate (cm/kyr) for which the sum of maximum spectral power is calculated for.

spacing

Spacing (cm/kyr) between sedimentation rates

cycles

Astronomical cycles (in kyr) for which the combined sum of maximum spectral power is calculated for

x_lab

label for the y-axis Default="depth"

y_lab

label for the y-axis Default="sedrate"

run_multicore

run simulation using multiple cores Default=FALSE the simulation is run at x-2 cores to allow the 2 remaining processes to run background processes

genplot

Generate plot Default="FALSE"

plot_res

plot options are 1: sum max power or 2: nr of components Default=2

keep_editable

Keep option to add extra features after plotting Default=FALSE

palette_name

Name of the color palette which is used for plotting. The color palettes than can be chosen depends on which the R package is specified in the color_brewer parameter. The included R packages from which palettes can be chosen from are; the 'RColorBrewer', 'grDevices', 'ColorRamps' and 'Viridis' R packages. There are many options to choose from so please read the documentation of these packages Default=rainbow. The R package 'viridis' has the color palette options: “magma”, “plasma”, “inferno”, “viridis”, “mako”, and “rocket” and “turbo” To see the color palette options of the The R pacakge 'RColorBrewer' run the RColorBrewer::brewer.pal.info() function The R package 'colorRamps' has the color palette options:"blue2green", "blue2green2red", "blue2red", "blue2yellow", "colorRamps", "cyan2yellow", "green2red", "magenta2green", "matlab.like", "matlab.like2" and "ygobb" The R package 'grDevices' has the built in palette options:"rainbow", "heat.colors", "terrain.colors","topo.colors" and "cm.colors" To see even more color palette options of the The R pacakge 'grDevices' run the grDevices::hcl.pals() function

color_brewer

Name of the R package from which the color palette is chosen from. The included R packages from which palettes can be chosen are; the RColorBrewer, grDevices, ColorRamps and Viridis R packages. There are many options to choose from so please read the documentation of these packages. "Default=grDevices

verbose

Print text Default=FALSE.

Value

Returns a list which contains 4 elements element 1: sum of maximum spectral power element 2: number of cycles used in the sum of maximum spectral power element 3: y-axis values of the matrices which is sedimentation rate element 4: x-axis values of the matrices which is depth

If Default="TRUE" a plot is created with 3 subplots. Subplot 1 is plot in which the the sum of maximum spectral power for a given sedimentation rate or nr of cycles is plotted for each depth given depth. Subplot 2 is a plot in which the average sum of maximum spectral power is plotted fro each sedimentation Subplot 3 is a color scale for subplot 1.

Author(s)

Based on the asm and eAsm functions of the 'astrochron' R package and the 'eCOCO' and 'COCO' functions of the 'Acycle' software

References

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

Acycle: Time-series analysis software for paleoclimate research and education, Mingsong Li, Linda Hinnov, Lee Kump, Computers & Geosciences,Volume 127,2019,Pages 12-22,ISSN 0098-3004, <doi:10.1016/j.cageo.2019.02.011>

Tracking variable sedimentation rates and astronomical forcing in Phanerozoic paleoclimate proxy series with evolutionary correlation coefficients and hypothesis testing, Mingsong Li, Lee R. Kump, Linda A. Hinnov, Michael E. Mann, Earth and Planetary Science Letters,Volume 501, T2018,Pages 165-179,ISSN 0012-821X,<doi:10.1016/j.epsl.2018.08.041>

Examples

#estimate sedimentation rate for the the magnetic susceptibility record
# of the Sullivan core of Pas et al., (2018).

mag_wt <- analyze_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,
run_multicore=FALSE,
verbose=FALSE)

sedrates <- sum_power_sedrate(red_noise=mag_wt_red_noise,
wavelet=mag_wt,
percentile=0.75,
sedrate_low = 0.5,
sedrate_high = 4,
spacing = 0.05,
cycles = c(2376,1600,1180,696,406,110),
x_lab="depth",
y_lab="sedrate",
run_multicore=FALSE,
genplot = FALSE,
plot_res=1,
keep_editable=FALSE,
palette_name = "rainbow",
color_brewer="grDevices",
verbose=FALSE)