#include "Covariances/CovAniso.hpp"
#include "gstlearn_export.hpp"
#include "geoslib_d.h"
#include "Basic/NamingConvention.hpp"
#include "Db/DbGrid.hpp"
#include "Matrix/MatrixRectangular.hpp"
#include "Model/Model.hpp"
#include "Model/Constraints.hpp"
#include "Model/Option_AutoFit.hpp"
#include "Model/Option_VarioFit.hpp"
#include "Stats/Selectivity.hpp"
#include "Variogram/DirParam.hpp"

Functions
GSTLEARN_EXPORT Vario *	variogram_pgs (Db db, const VarioParam varioparam, const RuleProp *ruleprop, int flag_rho=false, int opt_correl=2)

GSTLEARN_EXPORT int	model_auto_fit (Vario vario, Model model, bool verbose=false, const Option_AutoFit &mauto_arg=Option_AutoFit(), const Constraints &cons_arg=Constraints(), const Option_VarioFit &optvar_arg=Option_VarioFit())

GSTLEARN_EXPORT int	vmap_auto_fit (const DbGrid dbmap, Model model, bool verbose=false, const Option_AutoFit &mauto_arg=Option_AutoFit(), const Constraints &cons_arg=Constraints(), const Option_VarioFit &optvar_arg=Option_VarioFit())

GSTLEARN_EXPORT void	set_test_discrete (bool flag_discret)

GSTLEARN_EXPORT Vario *	model_pgs (Db db, const VarioParam varioparam, const RuleProp ruleprop, const Model model1, const Model *model2=nullptr)

GSTLEARN_EXPORT Cheb_Elem *	spde_cheb_manage (int mode, int verbose, double power, const VectorDouble &blin, MatrixSparse S, Cheb_Elem cheb_old)

GSTLEARN_EXPORT int	spde_chebychev_operate (MatrixSparse S, Cheb_Elem cheb_elem, const VectorDouble &lambda, const VectorDouble &x, VectorDouble &y)

GSTLEARN_EXPORT int	krigsum (Db dbin, Db dbout, Model model, ANeigh neigh, bool flag_positive=false, const NamingConvention &namconv=NamingConvention("KrigSum"))

GSTLEARN_EXPORT int	declustering (Db db, Model model, int method, ANeigh neigh=nullptr, DbGrid dbgrid=nullptr, const VectorDouble &radius=VectorDouble(), const VectorInt &ndisc=VectorInt(), int flag_sel=false, bool verbose=false)

GSTLEARN_EXPORT int	simpgs (Db dbin, Db dbout, RuleProp ruleprop, Model model1, Model model2=nullptr, ANeigh neigh=nullptr, int nbsimu=1, int seed=1321421, int flag_gaus=false, int flag_prop=false, int flag_check=false, int flag_show=false, int nbtuba=100, int gibbs_nburn=10, int gibbs_niter=100, double percent=5., const NamingConvention &namconv=NamingConvention("Facies", true, true, true, ELoc::fromKey("FACIES")))

GSTLEARN_EXPORT int	simbipgs (Db dbin, Db dbout, RuleProp ruleprop, Model model11, Model model12=nullptr, Model model21=nullptr, Model model22=nullptr, ANeigh neigh=nullptr, int nbsimu=1, int seed=43243, int flag_gaus=false, int flag_prop=false, int flag_check=false, int flag_show=false, int nbtuba=100, int gibbs_nburn=10, int gibbs_niter=100, double percent=5., const NamingConvention &namconv=NamingConvention("Facies", true, true, true, ELoc::fromKey("FACIES")))

GSTLEARN_EXPORT VectorDouble	simsph_mesh (MeshSpherical mesh, Model model, const SimuSphericalParam &sphepar, int seed=54523, int verbose=false)

GSTLEARN_EXPORT MatrixRectangular	fluid_extract (DbGrid *dbgrid, const String &name_facies, const String &name_fluid, const String &name_poro, const String &name_date, int nfacies, int nfluids, int facies0, int fluid0, int ntime, double time0, double dtime, bool verbose=false)

GSTLEARN_EXPORT int	simpgs_spde (Db dbin, Db dbout, RuleProp ruleprop, Model model1, Model *model2, const String &triswitch, const VectorDouble &gext, int flag_gaus, int flag_prop, int flag_check, int flag_show, int nfacies, int seed, int nbsimu, int gibbs_nburn, int gibbs_niter, int ngibbs_int, int verbose, double percent)

GSTLEARN_EXPORT int	db_proportion_estimate (Db dbin, DbGrid dbout, Model *model, int niter=100, bool verbose=false, const NamingConvention &namconv=NamingConvention("Prop", true, true, true, ELoc::fromKey("P")))

GSTLEARN_EXPORT int	gibbs_sampler (Db dbin, Model model, int nbsimu, int seed, int gibbs_nburn, int gibbs_niter, bool flag_moving, bool flag_norm, bool flag_multi_mono, bool flag_propagation, bool flag_sym_neigh, int gibbs_optstats, double percent, bool flag_ce, bool flag_cstd, bool verbose=false, const NamingConvention &namconv=NamingConvention("Gibbs"))

Function Documentation

◆ db_proportion_estimate()

GSTLEARN_EXPORT int db_proportion_estimate	(	Db *	dbin,
		DbGrid *	dbout,
		Model *	model,
		int	niter,
		bool	verbose,
		const NamingConvention &	namconv
	)

Standard Kriging

Returns: Error return code

Parameters

[in]	dbin	Input Db structure
[in]	dbout	Output Db structure
[in]	model	Model structure
[in]	niter	Number of iterations
[in]	verbose	Verbose flag
[in]	namconv	Naming convention

Remarks: The procedure uses the FIRST covariance of the Model; to describe the spatial structure

◆ declustering()

GSTLEARN_EXPORT int declustering	(	Db *	dbin,
		Model *	model,
		int	method,
		ANeigh *	neigh,
		DbGrid *	dbgrid,
		const VectorDouble &	radius,
		const VectorInt &	ndiscs,
		int	flag_sel,
		bool	verbose
	)

Perform the Declustering task

Returns: Error return code

Parameters

[in]	dbin	input Db structure
[in]	model	Model structure
[in]	method	Method for declustering
[in]	neigh	ANeigh structure
[in]	dbgrid	Grid auxiliary Db structure
[in]	radius	Array of neighborhood radius
[in]	ndiscs	Array of discretization
[in]	flag_sel	1 to mask off samples with zero weight
[in]	verbose	Verbose option

◆ fluid_extract()

GSTLEARN_EXPORT MatrixRectangular fluid_extract	(	DbGrid *	dbgrid,
		const String &	name_facies,
		const String &	name_fluid,
		const String &	name_poro,
		const String &	name_date,
		int	nfacies,
		int	nfluids,
		int	facies0,
		int	fluid0,
		int	ntime,
		double	time0,
		double	dtime,
		bool	verbose
	)

Extract time charts from the fluid propagation block

Returns: The returned matrix

Parameters

[in]	dbgrid	Db grid structure
[in]	name_facies	Name of variable containing Facies
[in]	name_fluid	Name of variable containing Fluid
[in]	name_poro	Name of variable containing Porosity (optional)
[in]	name_date	Name of variable containing Date
[in]	nfacies	number of facies (facies 0 excluded)
[in]	nfluids	number of fluids
[in]	facies0	Value of the target facies
[in]	fluid0	Value of the target fluid
[in]	ntime	Number of Time intervals
[in]	time0	Starting time
[in]	dtime	Time interval
[in]	verbose	1 for a verbose option

◆ gibbs_sampler()

GSTLEARN_EXPORT int gibbs_sampler	(	Db *	dbin,
		Model *	model,
		int	nbsimu,
		int	seed,
		int	gibbs_nburn,
		int	gibbs_niter,
		bool	flag_moving,
		bool	flag_norm,
		bool	flag_multi_mono,
		bool	flag_propagation,
		bool	flag_sym_neigh,
		int	gibbs_optstats,
		double	percent,
		bool	flag_ce,
		bool	flag_cstd,
		bool	verbose,
		const NamingConvention &	namconv
	)

Perform the Gibbs sampler

Returns: Error return code

Parameters

[in]	dbin	Db structure
[in]	model	Model structure
[in]	nbsimu	Number of simulations
[in]	seed	Seed for random number generator
[in]	gibbs_nburn	Initial number of iterations for bootstrapping
[in]	gibbs_niter	Maximum number of iterations
[in]	flag_moving	1 for Moving
[in]	flag_norm	1 if the Model must be normalized
[in]	flag_multi_mono	1 for the Multi_mono algorithm
[in]	flag_propagation	1 for the propagation algorithm
[in]	flag_sym_neigh	Deprecated argument
[in]	gibbs_optstats	0: No stats - 1: Print - 2: Save Neutral file
[in]	percent	Amount of nugget effect added to too continuous model (expressed in percentage of total variance)
[in]	flag_ce	1 if the conditional expectation should be returned instead of simulations
[in]	flag_cstd	1 if the conditional standard deviation should be returned instead of simulations
[in]	verbose	Verbose flag
[in]	namconv	Naming convention

◆ krigsum()

GSTLEARN_EXPORT int krigsum	(	Db *	dbin,
		Db *	dbout,
		Model *	model,
		ANeigh *	neigh,
		bool	flag_positive,
		const NamingConvention &	namconv
	)

Punctual Multivariate Kriging under a constraint

Returns: Error return code

Parameters

[in]	dbin	input Db structure
[in]	dbout	output Db structure
[in]	model	Model structure (univariate)
[in]	neigh	ANeigh structure
[in]	flag_positive	1 for a positive constraints
[in]	namconv	Naming convention

Remarks: All the variables are estimated using the same model; In this procedure, we assume that:; - the problem is multivariate ("z" variables); - the constraints is stored in "sum" (only used in dbout)

◆ model_auto_fit()

GSTLEARN_EXPORT int model_auto_fit	(	Vario *	vario,
		Model *	model,
		bool	verbose,
		const Option_AutoFit &	mauto_arg,
		const Constraints &	cons_arg,
		const Option_VarioFit &	optvar_arg
	)

Automatic model fitting

Returns: Error returned code

Parameters

[in]	vario	Vario structure containing the exp. variogram
[in]	model	Model structure containing the basic structures
[in]	verbose	Verbose flag
[in]	mauto_arg	Option_AutoFit structure
[in]	cons_arg	Constraints structure
[in]	optvar_arg	Opt_Vario structure

◆ model_pgs()

GSTLEARN_EXPORT Vario* model_pgs	(	Db *	db,
		const VarioParam *	varioparam,
		const RuleProp *	ruleprop,
		const Model *	model1,
		const Model *	model2
	)

Evaluate the experimental variogram of indicators in PluriGaussian case

Returns: Error return code

Parameters

[in]	db	Db descriptor
[in]	varioparam	VarioParam structure
[in]	ruleprop	RuleProp structure
[in]	model1	First Model structure
[in]	model2	Second Model structure (optional)

◆ set_test_discrete()

GSTLEARN_EXPORT void set_test_discrete ( bool flag_discret )

◆ simbipgs()

GSTLEARN_EXPORT int simbipgs	(	Db *	dbin,
		Db *	dbout,
		RuleProp *	ruleprop,
		Model *	model11,
		Model *	model12,
		Model *	model21,
		Model *	model22,
		ANeigh *	neigh,
		int	nbsimu,
		int	seed,
		int	flag_gaus,
		int	flag_prop,
		int	flag_check,
		int	flag_show,
		int	nbtuba,
		int	gibbs_nburn,
		int	gibbs_niter,
		double	percent,
		const NamingConvention &	namconv
	)

Perform the conditional or non-conditional Bi Pluri-gaussian simulations

Returns: Error return code

Parameters

[in]	dbin	Input Db structure (optional)
[in]	dbout	Output Db structure
[in]	ruleprop	Ruleprop definition
[in]	model11	First Model structure for First Lithotype Rule
[in]	model12	Second Model structure for First Lithotype Rule
[in]	model21	First Model structure for Second Lithotype Rule
[in]	model22	Second Model structure for Second Lithotype Rule
[in]	neigh	ANeigh structure
[in]	nbsimu	Number of simulations
[in]	seed	Seed for random number generator
[in]	flag_gaus	1 gaussian results; otherwise facies
[in]	flag_prop	1 for facies proportion
[in]	flag_check	1 if the facies at data must be checked against the closest simulated grid node
[in]	flag_show	1 if the grid node which coincides with the data should be represented with the data facies (only if flag_cond && !flag_gaus)
[in]	nbtuba	Number of turning bands
[in]	gibbs_nburn	Number of bootstrap iterations
[in]	gibbs_niter	Maximum number of iterations
[in]	percent	Amount of nugget effect added to too continuous model (expressed in percentage of the total variance)
[in]	namconv	Naming convention

Remarks: When conditional, the two first variables in the input Db; should correspond to the two facies indices (starting from 1); The argument 'dbin' is optional: it must be defined only for; conditional simulations; The proportions (nfac1 * nfac2) must be ordered as follows:; f1af2a, f1bf2a, f1cf2a, ..., f1bf2a, f1bf2b, ..., f1nf2m

◆ simpgs()

GSTLEARN_EXPORT int simpgs	(	Db *	dbin,
		Db *	dbout,
		RuleProp *	ruleprop,
		Model *	model1,
		Model *	model2,
		ANeigh *	neigh,
		int	nbsimu,
		int	seed,
		int	flag_gaus,
		int	flag_prop,
		int	flag_check,
		int	flag_show,
		int	nbtuba,
		int	gibbs_nburn,
		int	gibbs_niter,
		double	percent,
		const NamingConvention &	namconv
	)

Perform the conditional or non-conditional Pluri-gaussian simulations

Returns: Error return code

Parameters

[in]	dbin	Input Db structure (optional)
[in]	dbout	Output Db structure
[in]	ruleprop	RuleProp structure
[in]	model1	First Model structure
[in]	model2	Second Model structure (optional)
[in]	neigh	ANeigh structure
[in]	nbsimu	Number of simulations
[in]	seed	Seed for random number generator
[in]	flag_gaus	1 if results must be gaussian; otherwise facies
[in]	flag_prop	1 for facies proportion
[in]	flag_check	1 if the facies at data must be checked against the closest simulated grid node
[in]	flag_show	1 if the grid node which coincides with the data should be represented with the data facies (only if flag_cond && !flag_gaus)
[in]	nbtuba	Number of turning bands
[in]	gibbs_nburn	Number of bootstrap iterations
[in]	gibbs_niter	Maximum number of iterations
[in]	percent	Amount of nugget effect added to too much continous model (expressed in percentage of the total variance)
[in]	namconv	Naming convention

Remarks: When conditional, the unique variable in the input Db structure; should correspond to the facies index (starting from 1); The argument 'dbin' is optional: it must be defined only for; conditional simulations

◆ simpgs_spde()

GSTLEARN_EXPORT int simpgs_spde	(	Db *	dbin,
		Db *	dbout,
		RuleProp *	ruleprop,
		Model *	model1,
		Model *	model2,
		const String &	triswitch,
		const VectorDouble &	gext,
		int	flag_gaus,
		int	flag_prop,
		int	flag_check,
		int	flag_show,
		int	nfacies,
		int	seed,
		int	nbsimu,
		int	gibbs_nburn,
		int	gibbs_niter,
		int	ngibbs_int,
		int	verbose,
		double	percent
	)

Perform the conditional Pluri-gaussian simulations using spde

Returns: Error return code

Parameters

[in]	dbin	Input Db structure (optional)
[in]	dbout	Output Db structure
[in]	ruleprop	RuleProp definition
[in]	model1	First Model structure
[in]	model2	Second Model structure (optional)
[in]	triswitch	Meshing option
[in]	gext	Array of domain dilation
[in]	flag_gaus	1 if results must be gaussian; otherwise facies
[in]	flag_prop	1 for facies proportion
[in]	flag_check	1 if the facies at data must be checked against the closest simulated grid node
[in]	flag_show	1 if the grid node which coincides with the data should be represented with the data facies
[in]	nfacies	Number of facies
[in]	seed	Seed for random number generator
[in]	nbsimu	Number of simulations
[in]	gibbs_nburn	Number of iterations (Burning step)
[in]	gibbs_niter	Maximum number of iterations
[in]	ngibbs_int	Number of iterations internal to Gibbs (SPDE)
[in]	verbose	Verbose flag
[in]	percent	Amount of nugget effect added to too continous model (expressed in percentage of the total variance)

Remarks: When conditional, the unique variable in the input Db structure; should correspond to the facies index (starting from 1)

◆ simsph_mesh()

GSTLEARN_EXPORT VectorDouble simsph_mesh	(	MeshSpherical *	mesh,
		Model *	model,
		const SimuSphericalParam &	sphepar,
		int	seed,
		int	verbose
	)

Simulates the random function on the sphere

Returns: The Vector simulated values

Parameters

[in]	mesh	MeshSpherical object
[in]	model	Model (defined in Euclidean space) to be used
[in]	sphepar	SimuSphericalParam structure
[in]	seed	Seed for random number generation
[in]	verbose	Verbose flag

◆ spde_cheb_manage()

GSTLEARN_EXPORT Cheb_Elem* spde_cheb_manage	(	int	mode,
		int	verbose,
		double	power,
		const VectorDouble &	blin,
		MatrixSparse *	S,
		Cheb_Elem *	cheb_old
	)

Manage Cheb_Elem structure

Returns: Error return code

Parameters

[in]	mode	1 for allocation; -1 for deallocation
[in]	verbose	Verbose flag
[in]	power	Parameter passed to Chebychev function
[in]	blin	Array of coefficients for Linear combinaison
[in]	S	Shift operator
[in]	cheb_old	Cheb_Elem to be freed (only for mode=-1)

Remarks: Arguments 'power', 'nblin', 'blin' and 'B' are used if mode=1; Argument 'cheb_old' is used if mode=-1

◆ spde_chebychev_operate()

GSTLEARN_EXPORT int spde_chebychev_operate	(	MatrixSparse *	S,
		Cheb_Elem *	cheb_elem,
		const VectorDouble &	lambda,
		const VectorDouble &	x,
		VectorDouble &	y
	)

Perform the Chebychev polynomial procedure on an input vector

Returns: Error return code

Parameters

[in]	S	Shift operator
[in]	cheb_elem	Cheb_Elem structure
[in]	lambda	Scaling vector
[in]	x	Input array (Dimension: nvertex)
[out]	y	Output array (Dimension: nvertex)

◆ variogram_pgs()

GSTLEARN_EXPORT Vario* variogram_pgs	(	Db *	db,
		const VarioParam *	varioparam,
		const RuleProp *	ruleprop,
		int	flag_rho,
		int	opt_correl
	)

Calculate the Gaussian variograms

Returns: Error return code

Parameters

[in]	db	Db structure
[in]	varioparam	VarioParam structure for the GRFs
[in]	ruleprop	RuleProp structure
[in]	flag_rho	1 if the correlation coefficient must be regressed
[in]	opt_correl	0 full model; 1 symmetrical; 2 residuals

Remarks: This is simply a routine dispatching between the stationary function; and the non-stationary one

◆ vmap_auto_fit()

GSTLEARN_EXPORT int vmap_auto_fit	(	const DbGrid *	dbmap,
		Model *	model,
		bool	verbose,
		const Option_AutoFit &	mauto_arg,
		const Constraints &	cons_arg,
		const Option_VarioFit &	optvar_arg
	)

Automatic model fitting

Returns: Error returned code

Parameters

[in]	dbmap	Db Grid structure containing the Vmap
[in]	model	Model structure containing the basic structures
[in]	verbose	Verbose flag
[in]	mauto_arg	Option_AutoFit structure
[in]	cons_arg	Constraints structure
[in]	optvar_arg	Opt_Vario structure

Functions

Function Documentation

◆ db_proportion_estimate()

◆ declustering()

◆ fluid_extract()

◆ gibbs_sampler()

◆ krigsum()

◆ model_auto_fit()

◆ model_pgs()

◆ set_test_discrete()

◆ simbipgs()

◆ simpgs()

◆ simpgs_spde()

◆ simsph_mesh()

◆ spde_cheb_manage()

◆ spde_chebychev_operate()

◆ variogram_pgs()

◆ vmap_auto_fit()