2. Handling large datasets

This vignette teaches you how to handle large stress datasets and how to retrieve relative plate motions parameters from a set of plate motions.

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

Larger Data Sets

tectonicr also handles larger data sets. A subset of the World Stress Map data compilation (Heidbach et al. 2016) is included as an example data set and can be imported through:

data("san_andreas")
head(san_andreas)
#> Simple feature collection with 6 features and 9 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: -119.966 ymin: 32.94 xmax: -115.627 ymax: 35.714
#> Geodetic CRS:  WGS 84
#> # A tibble: 6 × 10
#>   id         lat   lon   azi   unc type  depth quality regime
#>   <chr>    <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <fct>   <chr> 
#> 1 wsm07786  35.7 -120.    36     1 HF      1.3 A       S     
#> 2 wsm11750  32.9 -116.     7     1 FMF     7   A       S     
#> 3 wsm11759  33.0 -116.   169     1 FMF     5   A       S     
#> 4 wsm11782  33.2 -116.     1     1 FMF     8   A       S     
#> 5 wsm11801  33.9 -116.    23     1 FMF     6   A       S     
#> 6 wsm11830  33.5 -116.   160     1 FMF     9   A       S     
#> # ℹ 1 more variable: geometry <POINT [°]>

Modeling the stress directions (wrt. to the geographic North pole) using the Pole of Oration (PoR) of the motion of North America relative to the Pacific Pate. We test the dataset against a right-laterally tangential displacement type.

data("nuvel1")
por <- subset(nuvel1, nuvel1$plate.rot == "na")
san_andreas.prd <- PoR_shmax(san_andreas, por, type = "right")

Combine the model results with the coordinates of the observed data

san_andreas.res <- data.frame(
  sf::st_drop_geometry(san_andreas),
  san_andreas.prd
)

Stress map

ggplot2::ggplot() can be used to visualize the results. The orientation of the axis can be displayed with the function geom_spoke(). The position argument position = "center_spoke" aligns the marker symbol at the center of the point. The deviation can be color coded. deviation_norm() yields the normalized value of the deviation, i.e. absolute values between 0 and 90\(^{\circ}\).

Also included are the plate boundary geometries after Bird (2003):

data("plates") # load plate boundary data set

Alternatively, there is also the NUVEL1 plate boundary model by DeMets et al. (1990) stored under data("nuvel1_plates").

First we create the predicted trajectories of \(\sigma_{Hmax}\) (more details in Article 3.):

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

Then we initialize the plot map…

map <- ggplot() +
  geom_sf(
    data = plates,
    color = "red",
    lwd = 2,
    alpha = .5
  ) +
  scale_color_continuous(
    type = "viridis",
    limits = c(0, 90),
    name = "|Deviation| in (\u00B0)",
    breaks = seq(0, 90, 22.5)
  ) +
  scale_alpha_discrete(name = "Quality rank", range = c(1, 0.4))

…and add the \(\sigma_{Hmax}\) trajectories and data points:

map +
  geom_sf(
    data = trajectories,
    lty = 2
  ) +
  geom_spoke(
    data = san_andreas.res,
    aes(
      x = lon,
      y = lat,
      angle = deg2rad(90 - azi),
      color = deviation_norm(dev),
      alpha = quality
    ),
    radius = 1,
    position = "center_spoke",
    na.rm = TRUE
  ) +
  coord_sf(
    xlim = range(san_andreas$lon),
    ylim = range(san_andreas$lat)
  )

The map shows generally low deviation of the observed \(\sigma_{Hmax}\) directions from the modeled stress direction using counter-clockwise 45\(^{\circ}\) loxodromes.

The normalized \(\chi^2\) test quantifies the fit between the modeled \(\sigma_{Hmax}\) direction the observed stress direction considering the reported uncertainties of the measurement.

norm_chisq(
  obs = san_andreas.res$azi.PoR,
  prd = 135,
  unc = san_andreas.res$unc
)
#> [1] 0.03296388

The value is \(\leq\) 0.15, indicating a significantly good fit of the model. Thus, the traction of the transform plate boundary explain the stress direction of the area.

Variation of the Direction of the Maximum Horizontal Stress wrt. to the Distance to the Plate Boundary

The direction of the maximum horizontal stress correlates with plate motion direction at the plate boundary zone. Towards the plate interior, plate boundary forces become weaker and other stress sources will probably dominate.

To visualize the variation of the \(\sigma_{Hmax}\) wrt. to the distance to the plate boundary, we need to transfer the direction of \(\sigma_{Hmax}\) from the geographic reference system (i.e. azimuth is the deviation of a direction from geographic North pole) to the Pole of Rotation (PoR) reference system (i.e. azimuth is the deviation from the PoR).

The PoR coordinate reference system is the oblique transformation of the geographical coordinate system with the PoR coordinates being the the translation factors.

The azimuth in the PoR reference system \(\alpha_{PoR}\) is the angular difference between the azimuth in geographic reference system \(\alpha_{geo}\) and the (initial) bearing of the great circle that passes through the data point and the PoR \(\theta\).

To calculate the distance to the plate boundary, both the plate boundary geometries and the data points (in geographical coordinates) will be transformed in to the PoR reference system. In the PoR system, the distance is the latitudinal or longitudinal difference between the data points and the inward/outward or tangential moving plate boundaries, respectively.

This is done with the function distance_from_pb(), which returns the angular distances.

plate_boundary <- subset(plates, plates$pair == "na-pa")
san_andreas.res$distance <-
  distance_from_pb(
    x = san_andreas,
    PoR = por,
    pb = plate_boundary,
    tangential = TRUE
  )

Finally, we visualize the \(\sigma_{Hmax}\) direction wrt. to the distance to the plate boundary:

azi_plot <- ggplot(san_andreas.res, aes(x = distance, y = azi.PoR)) +
  coord_cartesian(ylim = c(0, 180)) +
  labs(x = "Distance from plate boundary (\u00B0)", y = "Azimuth in PoR (\u00B0)") +
  geom_hline(yintercept = c(0, 45, 90, 135, 180), lty = 3) +
  geom_pointrange(
    aes(
      ymin = azi.PoR - unc, ymax = azi.PoR + unc,
      color = san_andreas$regime, alpha = san_andreas$quality
    ),
    size = .25
  ) +
  scale_y_continuous(
    breaks = seq(-180, 360, 45),
    sec.axis = sec_axis(
      ~.,
      name = NULL,
      breaks = c(0, 45, 90, 135, 180),
      labels = c("Outward", "Tan (L)", "Inward", "Tan (R)", "Outward")
    )
  ) +
  scale_alpha_discrete(name = "Quality rank", range = c(1, 0.1)) +
  scale_color_manual(name = "Tectonic regime", values = stress_colors(), breaks = names(stress_colors()))
print(azi_plot)

Adding a rolling statistics (e.g. weighted mean and 95% confidence interval) of the transformed azimuth:

san_andreas.res_roll <- san_andreas.res[order(san_andreas.res$distance), ]

san_andreas.res_roll$r_mean <- roll_circstats(
  san_andreas.res_roll$azi.PoR,
  w = 1 / san_andreas.res_roll$unc,
  FUN = circular_mean, width = 51
)

san_andreas.res_roll$r_conf95 <- roll_confidence(
  san_andreas.res_roll$azi.PoR,
  w = 1 / san_andreas.res_roll$unc,
  width = 51
)

azi_plot +
  geom_step(
    data = san_andreas.res_roll,
    aes(distance, r_mean - r_conf95),
    lty = 2
  ) +
  geom_step(
    data = san_andreas.res_roll,
    aes(distance, r_mean + r_conf95),
    lty = 2
  ) +
  geom_step(
    data = san_andreas.res_roll,
    aes(distance, r_mean)
  )

Close to the dextral plate boundary, the majority of the stress data have a strike-slip fault regime and are oriented around 135\(^{\circ}\) wrt. to the PoR. Thus, the date are parallel to the predicted stress sourced by a right-lateral displaced plate boundary. Away from the plate boundary, the data becomes more noisy.

This azimuth (PoR) vs. distance plot also allows to identify whether a less known plate boundary represents a inward, outward, or tangential displaced boundary.

The relationship between the azimuth and the distance can be better visualized by using the deviation (normalized by the data precision) from the the predicted stress direction, i.e. the normalized \(\chi^2\):

# Rolling norm chisq:
san_andreas.res_roll$roll_nchisq <- roll_normchisq(
  san_andreas.res_roll$azi.PoR,
  san_andreas.res_roll$prd,
  san_andreas.res_roll$unc,
  width = 51
)

# plotting:
ggplot(san_andreas.res, aes(x = distance, y = nchisq)) +
  coord_cartesian(ylim = c(0, 1)) +
  labs(x = "Distance from plate boundary (\u00B0)", y = expression(Norm ~ chi^2)) +
  geom_hline(yintercept = c(0.15, .33, .7), lty = 3) +
  geom_point(aes(color = san_andreas$regime)) +
  scale_y_continuous(sec.axis = sec_axis(
    ~.,
    name = NULL,
    breaks = c(.15 / 2, .33, .7 + 0.15),
    labels = c("Good fit", "Random", "Systematic\nmisfit")
  )) +
  scale_color_manual(name = "Tectonic regime", values = stress_colors(), breaks = names(stress_colors())) +
  geom_step(
    data = san_andreas.res_roll,
    aes(distance, roll_nchisq)
  )

We can see that the data in fact starts to scatter notably beyond a distance of 3.8\(^{\circ}\) and becomes random at 7\(^{\circ}\) away from the plate boundary. Thus, the North American-Pacific plate boundary zone at the San Andreas Fault is approx. 4–7\(^{\circ}\) (ca. 380–750 km) wide.

The normalized \(\chi^2\) vs. distance plot allows to specify the width of the plate boundary zone.

R base plots for quick analysis

The data deviation map can also be build using base R’s plotting engine:

# Setup the colors for the deviation
cols <- tectonicr.colors(
  deviation_norm(san_andreas.res$dev),
  categorical = FALSE
)

# Setup the legend
col.legend <- data.frame(col = cols, val = names(cols)) |>
  dplyr::mutate(val2 = gsub("\\(", "", val), val2 = gsub("\\[", "", val2)) |>
  unique() |>
  dplyr::arrange(val2)

# Initialize the plot
plot(
  san_andreas$lon, san_andreas$lat,
  cex = 0,
  xlab = "PoR longitude", ylab = "PoR latitude",
  asp = 1
)

# Plot the axis and colors
axes(
  san_andreas$lon, san_andreas$lat, san_andreas$azi,
  col = cols, add = TRUE
)

# Plot the plate boundary
plot(sf::st_geometry(plates), col = "red", lwd = 2, add = TRUE)

# Plot the trajectories
plot(sf::st_geometry(trajectories), add = TRUE, lty = 2)

# Create the legend
graphics::legend(
  "bottomleft",
  title = "|Deviation| in (\u00B0)",
  inset = .05, cex = .75,
  legend = col.legend$val, fill = col.legend$col
)

A quick analysis the results can be obtained stress_analysis() that returns a list. The transformed coordinates and azimuths as well as the deviations can be viewed by:

results <- stress_analysis(san_andreas, por, "right", plate_boundary, plot = FALSE)
#> Reject Null Hypothesis
head(results$result)
#>    azi.PoR prd        dev       nchisq       cdist  lat.PoR   lon.PoR
#> 1 162.2848 135  27.284805 0.0919087122 0.210143277 56.93219 -82.67182
#> 2 137.7446 135   2.744584 0.0009299678 0.002292849 58.01202 -74.48278
#> 3 119.5840 135 -15.416011 0.0293399256 0.070663207 57.93239 -74.80319
#> 4 131.1908 135  -3.809243 0.0017913992 0.004413592 57.83707 -75.51138
#> 5 152.3277 135  17.327717 0.0370678728 0.088706703 58.17336 -76.62093
#> 6 109.7786 135 -25.221400 0.0785332091 0.181575875 57.84485 -76.16849
#>      distance
#> 1 -0.15965337
#> 2  0.08688785
#> 3  0.14986772
#> 4  0.38296977
#> 5 -0.04424539
#> 6  0.33684309

Statistical parameters describing the distribution of the transformed azimuths can be displayed by

results$stats
#>                    [,1]
#> mean       138.89370624
#> sd          37.12227404
#> var          0.18932742
#> dispersion   0.09840187
#> conf95       4.97276197
#> norm_chisq   0.03296388

Statistical test results are shown by

results$test
#> $statistic
#> [1] 0.8031963
#> 
#> $p.value
#> [1] 3.607527e-47

… and the associated plots can be displayed by setting plot = TRUE:

stress_analysis(san_andreas, por, "right", plate_boundary, plot = TRUE)

References

Bird, Peter. 2003. “An Updated Digital Model of Plate Boundaries” Geochemistry, Geophysics, Geosystems 4 (3). doi: 10.1029/2001gc000252.

DeMets, C., R. G. Gordon, D. F. Argus, and S. Stein. 1990. “Current Plate Motions” Geophysical Journal International 101 (2): 425–78. doi: 10.1111/j.1365-246x.1990.tb06579.x.

Heidbach, Oliver, Mojtaba Rajabi, Karsten Reiter, Moritz Ziegler, and WSM Team. 2016. “World Stress Map Database Release 2016. V. 1.1.” GFZ Data Services. doi: 10.5880/WSM.2016.001.

Tobias Stephan

2024-04-09

Larger Data Sets

Stress map

Variation of the Direction of the Maximum Horizontal Stress wrt. to the Distance to the Plate Boundary

R base plots for quick analysis

References