pick_correlation function

Signal correlation based event picking

The function picks (identifies) events from continuous data by comparing the data patterns against a template signal using Pearson's correlation coefficient, defining an event when that coefficient is above a threshold value.

pick_correlation(data, on, template, dur_min, time, dt)

Arguments

• data: eseis object, numeric vector or list of objects, data set to be processed.
• on: Numeric value, minimum correlation coefficient to define event occurence.
• template: eseis object or signal vector, template event with which the data set is correlated.
• dur_min: Numeric value, minimum duration of the event. This is required as the routine tends to identify multipe picks with similarly high correlation coefficients due to autocorrelation effects. If omitted, dur_min is set to 0, i.e., all picks are returned.
• time: POSIXct vector, time vector of the signal(s). If not provided, a synthetic time vector will be created.
• dt: Numeric value, sampling period. If omitted, either estimated from time or set to 0.01 s (i.e., f = 100 Hz).

Returns

data.frame, picked events.

Examples

## create synthetic event signal
p <- sin(seq(0, 10 * pi, by = 0.35)) * 0.2 * 
  (1 + sin(seq(0, pi, length.out = 90)))^5

## show event signal
plot(p, type = "l")

## create synthetic noise signal
x <- runif(n = 1000, min = -1, max = 1)
t <- seq(from = Sys.time(), length.out = length(x), by = 1/200)
ii <- floor(runif(n = 3, min = 100, max = 900))

## add events to noise
for(k in 1:length(ii)) {
  
  nn <- ii[k]:(ii[k] + 89)
  x[nn] <- x[nn] + p
}

## show resulting time series
plot(x = t, y = x, type = "l")

## pick events based on template
picks <- eseis::pick_correlation(data = x, 
                                 on = 0.8, 
                                 template = p, 
                                 time = t, 
                                 dt = 1/200)
                                 
## show result
print(picks)

Author(s)

Michael Dietze