Turning-bands conditioning, ensembles and diagnostics of simulations

ConditionalTurningBands: simple-kriging correction of an unconditional turning-bands realisation (Journel 1974). TbEnsemble: pointwise and realisation moments and the ensemble variogram against the model. TbBandConvergence: RMSE of the ensemble covariance against the model by number of bands. StandardiseRealisations: affine correction to a target mean and variance. DirectionalVariogram: directional semivariograms (degrees clockwise from north), as gstat::variogram with alpha and tol.hor. ClassProportions, BoundaryProbability, Connectivity and IndicatorVariogram: diagnostics of categorical realisations (row-major grids). Identical to the Python arm morie.fn.simdiag; cell indices are 1-based here.

Usage

ConditionalTurningBands(
  z,
  data_coords,
  coords,
  cov_model = "exponential",
  sill = 1,
  range_ = 1,
  nu = 0.5,
  mean = 0,
  n_bands = 64L,
  n_waves = 50L,
  seed = 1
)

TbEnsemble(
  coords,
  cov_model = "exponential",
  sill = 1,
  range_ = 1,
  nu = 0.5,
  nsim = 20L,
  n_bands = 64L,
  n_waves = 50L,
  seed = 1,
  bounds = NULL
)

TbBandConvergence(
  coords,
  cov_model = "exponential",
  sill = 1,
  range_ = 1,
  nu = 0.5,
  bands = c(4, 16, 64),
  nsim = 50L,
  n_waves = 50L,
  seed = 1
)

StandardiseRealisations(realisations, mean = 0, variance = 1)

DirectionalVariogram(values, coords, alphas, boundaries, tol = 22.5)

ClassProportions(realisations, classes, target = NULL)

BoundaryProbability(realisations, nrow, ncol, classes)

Connectivity(
  realisations,
  nrow,
  ncol,
  target,
  pairs = list(),
  neighbourhood = 4L
)

IndicatorVariogram(realisations, nrow, ncol, target, lags)

Arguments

z: Conditioning data.
data_coords, coords: Data and simulation coordinates.
cov_model, sill, range_, nu: Covariance (as TurningBands).
mean, variance: Target moments.
n_bands, n_waves, seed: Turning-bands settings.
nsim: Number of realisations.
bounds, boundaries: Distance class boundaries.
bands: Numbers of bands to compare.
realisations: List of realisations.
values: Values.
alphas: Directions in degrees clockwise from north.
tol: Angular tolerance in degrees.
classes: Class labels.
target: Target proportions or class.
nrow, ncol: Grid size.
pairs: List of cell-index pairs.
neighbourhood: 4 or 8.
lags: Integer lags.

Value

List.

References

Journel, A. G. (1974). Geostatistics for conditional simulation of ore bodies. Economic Geology 69, 673-687.

Emery, X. and Lantuejoul, C. (2006). TBSIM: a computer program for conditional simulation of three-dimensional Gaussian random fields via the turning bands method. Computers and Geosciences 32, 1615-1628.

Renard, P. and Allard, D. (2013). Connectivity metrics for subsurface flow and transport. Advances in Water Resources 51, 168-196.

Examples

ConditionalTurningBands(2, rbind(c(0, 0)), rbind(c(0, 0), c(3, 0)), mean = 1)$field
#> [1] 2.0000000 0.4652133
Connectivity(list(c(1, 1, 0, 1)), 1, 4, 1, pairs = list(c(1, 2), c(1, 4)))$pair_probability
#> [1] 1 0