signal_spectrum: Calculate the spectrum of a time series

Description

The power spectral density estimate of the time series is calculated using different approaches.

Usage

signal_spectrum(data, dt, method = "periodogram", n, res, log = FALSE, ...)

Value

Data frame with frequency and power vector

Arguments

data: eseis object, numeric vector or list of objects, data set to be processed.
dt: Numeric value, sampling period. If omitted, dt is set to 1/200. Only needed if data is no eseis object.
method: Character value, calculation method. One out of "periodogram" and "autoregressive". default is "periodogram".
n: Numeric value, optional number of samples in running window used for smoothing the spectrogram. Only applied if a number is provided. Smoothing is performed as running mean.
res: Numeric value, optional resolution of the spectrum, i.e. the number of power and frequency values. If omitted, the full resolution is returned. If used, a spline interpolation is performed.
log: Logical value, option to interpolate the spectrum with log spaced frequency values. Default is FALSE.
...: Additional arguments passed to the function.

Author

Michael Dietze

Details

If the res option is used, the frequency and power vectors will be interpolated using a spline interpolator, using equally spaced frequency values. If desired, the additional option log = TRUE can be used to interpolate with log spaced frequency values.

Examples

Run this code


## load example data set
data(rockfall)

## calculate spectrum with standard setup
s <- signal_spectrum(data = rockfall_eseis)

## plot spectrum
plot_spectrum(data = s)

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