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Rigid Grain Net (RGN)

Source: R/strain_analysis.R
RGN_plot.Rd

The rigid grain net after (Jessup et al. 2007) plots the distribution the strain ratio (R) of orientation (phi) of porphyroclast over the theoretical distribution of tailless clasts. The plot estimates the critical shape factor Rc marking the transition between the stable-sink position and infinitely rotating porphyroclasts. This critical shape factor can be interpreted as the the mean kinmatic vorticity number. Here the Rc is estimated using the bootstrap method described in Stephan et al. (2025).

Usage

RGN_plot(
  r,
  theta,
  angle_error = 3,
  boot = 100L,
  probs = 0.972,
  grid = 0.05,
  main = "Rigid-Grain-Net",
  ...
)

Arguments

r

numeric. The porphyroclast aspect ratio (long axis/short axis)

theta

numeric. Angle between long axis and foliation (in degrees)

angle_error

numeric. Uncertainty of angle measurement. 3 by default.

boot

integer. Number of bootstrap resamples

probs

integer. Probability with values in \([0, 1]\) to estimate critical shape factor, i.e. the largest shape factor of measurements outside of critical hyperbole.

grid

numeric. Spacing of hyperboles.

main

character. The main title (on top).

...

plotting arguments passed to graphics::points()

Value

a plot or a list of the calculated B (shape factor) and theta values, and the bootstrapped confidence interval of the critical B value (Rc_CI).

References

Jessup, Micah J., Richard D. Law, and Chiara Frassi. "The rigid grain net (RGN): an alternative method for estimating mean kinematic vorticity number (Wm)." Journal of Structural Geology 29.3 (2007): 411-421. doi: 10.1016/j.jsg.2006.11.003

Stephan, Tobias, et al. "Going with the flow—Changes of vorticity control gold enrichment in Archean shear zones (Shebandowan Greenstone Belt, Superior Province, Canada)." Journal of Structural Geology (2025): 105542. doi: 10.1016/j.jsg.2025.105542

Examples

data(ramsay)
set.seed(20250411)

# assuming the mean orientation is the foliation:
theta <- tectonicr::circular_mean(ramsay[, 2]) - ramsay[, 2]

RGN_plot(ramsay[, "R"], theta, col = "darkred")
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced