5. Spatial interpolation

This vignette teaches you how to spatially interpolate stress fields and display the lateral patterns of stress anomalies.

library(tectonicr)
library(ggplot2) # load ggplot library

data("san_andreas")

data("cpm_models")
por <- cpm_models |>
  subset(model == "NNR-MORVEL56") |>
  equivalent_rotation("na", "pa")

Interpolation

Geographic coordinate system

Spatial interpolation of stress data is based on the aforementioned metrics (the algorithm is a modified version of the MATLAB script ‘stress2grid’ by Ziegler and Heidbach (2017).

mean_SH <- stress2grid(san_andreas, gridsize = 1, R_range = seq(50, 350, 100))

The default settings apply quality and inverse distance weighting of the mean, as well as a 25% cut-off for the standard deviation.

The data can now be visualized:

trajectories <- eulerpole_loxodromes(x = por, n = 40, cw = FALSE)

ggplot(mean_SH) +
  geom_sf(data = trajectories, lty = 2) +
  geom_spoke(data = san_andreas, aes(lon, lat, angle = deg2rad(90 - azi)), radius = .5, color = "grey30", position = "center_spoke") +
  geom_spoke(aes(lon, lat, angle = deg2rad(90 - azi), alpha = sd, color = mdr), radius = 1, position = "center_spoke", lwd = 1) +
  coord_sf(xlim = range(san_andreas$lon), ylim = range(san_andreas$lat)) +
  scale_alpha(name = "Standard deviation", range = c(1, .25)) +
  scale_color_viridis_c(
    "Wavelength\n(R-normalized mean distance)",
    limits = c(0, 1),
    breaks = seq(0, 1, .25)
  ) +
  facet_wrap(~R)

PoR coordinate system

The interpolated direction of far apart data points will suffer from distortions due to the underlying projection. In order to prevent such effects, the interpolation can be done in the PoR reference frame where the direction stays constant no matter the distance between the data points. Assuming that the stress field is sourced by the plate boundary force, the model-based interpolation allows more reliable results for areas close to plate boundaries.

mean_SH_PoR <- PoR_stress2grid(san_andreas, PoR = por, gridsize = 1, R_range = seq(50, 350, 100))

ggplot(mean_SH_PoR) +
  geom_sf(data = trajectories, lty = 2) +
  geom_spoke(data = san_andreas, aes(lon, lat, angle = deg2rad(90 - azi)), radius = .5, color = "grey30", position = "center_spoke") +
  geom_spoke(aes(lon, lat, angle = deg2rad(90 - azi), alpha = sd, color = mdr), radius = 1, position = "center_spoke", lwd = 1) +
  coord_sf(xlim = range(san_andreas$lon), ylim = range(san_andreas$lat)) +
  scale_alpha(name = "Standard deviation", range = c(1, .25)) +
  scale_color_viridis_c(
    "Wavelength\n(R-normalized mean distance)",
    limits = c(0, 1),
    breaks = seq(0, 1, .25)
  ) +
  facet_wrap(~R)

Rasterize the interpolation

The function compact_grid() selects only data with the minimum search radius from interpolated layers with different search radii. Since the interpolation was performed in the PoR CRS, the interpolated azimuths are additionally given in the transformed azimuths. This allows to easily calculate misfits from predicted directions:

mean_SH_PoR_reduced <- mean_SH_PoR |>
  compact_grid() |>
  dplyr::mutate(cdist = circular_distance(azi.PoR, 135))

Using circular_distance() in the example above, we can display the spatial patterns of the misfits of the stress direction from the predicted direction of the plate boundary force. Since the interpolation was performed in the PoR CRS, the grid is not composed of equally spaced grid cells in the geographic CRS. To rasterize such grids, we can, e.g., use Voronoi cells from the ggforce package.

ggplot(mean_SH_PoR_reduced) +
  ggforce::geom_voronoi_tile(
    aes(lon, lat, fill = cdist),
    max.radius = .7, normalize = FALSE
  ) +
  scale_fill_viridis_c("Angular distance", limits = c(0, 1)) +
  geom_sf(data = trajectories, lty = 2) +
  geom_spoke(
    aes(lon, lat, angle = deg2rad(90 - azi), alpha = sd),
    radius = .5, position = "center_spoke", lwd = .2, colour = "white"
  ) +
  scale_alpha("Standard deviation", range = c(1, .25)) +
  coord_sf(xlim = range(san_andreas$lon), ylim = range(san_andreas$lat))

The map highlights stress anomalies which show misfits to the direction of tested plate boundary force.

Kernel dispersion

Another way to analyse spatial misfits is the kernel dispersion, i.e. the local dispersion within a user-defined window (kernel). The kernel´s half width can be a single number (km) or a range of widths. The latter requires to compact the grid result (x) to find the smallest kernel size containing the the least dispersion (compact_grid(x, 'dispersion')).

It is recommended to calculate the kernel dispersion on PoR transformed data to avoid angle distortions due to projections.

san_andreas_por <- san_andreas
san_andreas_por$azi <- PoR_shmax(san_andreas, por, "right")$azi.PoR # transform to PoR azimuth
san_andreas_por$prd <- 135 # test direction
san_andreas_kdisp <- kernel_dispersion(san_andreas_por, gridsize = 1, R_range = seq(50, 350, 100))
san_andreas_kdisp <- compact_grid(san_andreas_kdisp, "dispersion")

ggplot(san_andreas_kdisp) +
  ggforce::geom_voronoi_tile(
    aes(lon, lat, fill = stat),
    max.radius = .7, normalize = FALSE
  ) +
  scale_fill_viridis_c("Dispersion", limits = c(0, 1)) +
  geom_sf(data = trajectories, lty = 2) +
  geom_spoke(
    data = san_andreas,
    aes(lon, lat, angle = deg2rad(90 - azi), alpha = unc),
    radius = .5, position = "center_spoke", lwd = .2, colour = "white"
  ) +
  scale_alpha("Standard deviation", range = c(1, .25)) +
  coord_sf(xlim = range(san_andreas$lon), ylim = range(san_andreas$lat))

References

Mardia, K. V., and Jupp, P. E. (Eds.). (1999). “Directional Statistics” Hoboken, NJ, USA: John Wiley & Sons, Inc. doi: 10.1002/9780470316979.

Ziegler, Moritz O., and Oliver Heidbach. 2017. “Manual of the Matlab Script Stress2Grid” GFZ German Research Centre for Geosciences; World Stress Map Technical Report 17-02. doi: 10.5880/wsm.2017.002.