tectonicr (version 0.4.6)

kernel_dispersion: Adaptive Kernel Dispersion

Description

Stress field and wavelength analysis using circular dispersion (or other statistical estimators for dispersion)

Usage

kernel_dispersion(
  x,
  stat = c("dispersion", "nchisq", "rayleigh"),
  grid = NULL,
  lon_range = NULL,
  lat_range = NULL,
  gridsize = 2.5,
  min_data = 3,
  threshold = 1,
  arte_thres = 200,
  dist_threshold = 0.1,
  R_range = seq(100, 2000, 100),
  ...
)
dispersion_grid(...)

Value

sf object containing

lon,lat

longitude and latitude in degree

stat

output of function defined in stat

R

The rearch radius in km.

mdr

Mean distance of datapoints per search radius

N

Number of data points in search radius

Arguments

x

sf object containing

azi

Azimuth in degree

unc

Uncertainties of azimuth in degree

prd

Predicted value for azimuth

stat

The measurement of dispersion to be calculated. Either "dispersion" (default), "nchisq", or "rayleigh" for circular dispersion, normalized Chi-squared test statistic, or Rayleigh test statistic.

grid

(optional) Point object of class sf.

lon_range, lat_range

(optional) numeric vector specifying the minimum and maximum longitudes and latitudes (are ignored if "grid" is specified).

gridsize

Numeric. Target spacing of the regular grid in decimal degree. Default is 2.5. (is ignored if "grid" is specified)

min_data

Integer. Minimum number of data per bin. Default is 3

threshold

Numeric. Threshold for stat value (default is 1)

arte_thres

Numeric. Maximum distance (in km) of the grid point to the next data point. Default is 200

dist_threshold

Numeric. Distance weight to prevent overweight of data nearby (0 to 1). Default is 0.1

R_range

Numeric value or vector specifying the (adaptive) kernel half-width(s) as search radius (in km). Default is seq(50, 1000, 50)

...

optional arguments to dist_greatcircle()

See Also

circular_dispersion(), norm_chisq(), rayleigh_test()

Examples

data("nuvel1")
PoR <- subset(nuvel1, nuvel1$plate.rot == "na")
san_andreas_por <- san_andreas
san_andreas_por$azi <- PoR_shmax(san_andreas, PoR, "right")$azi.PoR
san_andreas_por$prd <- 135
kernel_dispersion(san_andreas_por)