GEONE - DEESSE - Custom search neighborhood

Main points addressed

• setting custom search ellipsoid

Import what is required

import numpy as np
import matplotlib.pyplot as plt
import time
import os

# import package 'geone'
import geone as gn

# Show version of python and version of geone
import sys
print(sys.version_info)
print('geone version: ' + gn.__version__)

sys.version_info(major=3, minor=13, micro=7, releaselevel='final', serial=0)
geone version: 1.3.0

Training image (TI)

# Read file
data_dir = 'data'
filename = os.path.join(data_dir, 'ti.txt')
ti = gn.img.readImageTxt(filename)

# Values in the TI
ti.get_unique()

array([0., 1., 2.])

# Setting for categories / colors
categ_val = [0, 1, 2]
categ_col = ['lightblue', 'darkgreen', 'orange']

plt.figure(figsize=(5,5))
gn.imgplot.drawImage2D(ti, categ=True, categVal=categ_val, categCol=categ_col, title='TI')
plt.show()

Custom search ellipsoid

Class geone.deesseinterface.SearchNeighborhoodParameters

To simulate a cell in the simulation grid, the pattern made up of (at most) the nneighboringNode informed cells the closest to the simulated cell is retrieved. Only the cells within a search ellipsoid centered at the simulated cell are considered. The search ellipsoid is defined by

• rx, ry, rz: radii in each direction, in number of cells,

• ax, ay, az: anisotropy ratios (or inverse units): the distance to the central node is the Euclidean distance with cell unit: 1/ax by 1/ay by 1/az

• angle1, angle2, angle3: azimuth, dip, and plunge angles in degrees (default:0, 0, 0) to re-orient the search ellipsoid

• power: a power (default: 0) at which the distance to the central cell is raised, to get the weight of the pattern cells

Several modes are available to set the radii (radiusMode), in particular:

• large_default (default): large radii automatically computed according to the size of the simulation grid and the TI

• manual: the parameters rx, ry, rz are required (set manually)

Several modes are available to set the anisotropy ratios (anisotropyRatioMode), in particular:

• one (default): anisotropy ratios automatically set to ax=ay=az=1.0

• radius: anisotropy ratios automatically set to the corresponding radius, ax=rx, ay=ry, az=rz, so that the distance on the border of the search ellipsoid is equal to one

• manual: ax, ay, az are required (set manually)

Notes:

• It can be useful to limit the size of the search ellipsoid (by setting the radii) in presence of a small hard data set, in order to avoid realizations with too poor variability.

• It can be useful to set the anisotropy ratios manually to preferentially search in some direction(s) when retrieving the pattern for simulating a cell.

Examples using different search neigbhorhood ellipsoids

Simulation with default search ellipsoid

# Default search ellipsoid
deesse_input1 = gn.deesseinterface.DeesseInput(
    nx=100, ny=100, nz=1,
    nv=1, varname='code',
    TI=ti,
    distanceType='categorical',
    nneighboringNode=24,
    distanceThreshold=0.02,
    maxScanFraction=0.25,
    npostProcessingPathMax=1,
    seed=444,
    nrealization=1)

# Run deesse
t1 = time.time() # start time
deesse_output1 = gn.deesseinterface.deesseRun(deesse_input1)
t2 = time.time() # end time
print(f'Elapsed time: {t2-t1:.2g} sec')

deesseRun: DeeSse running... [VERSION 3.2 / BUILD NUMBER 20230914 / OpenMP 19 thread(s)]
deesseRun: DeeSse run complete
Elapsed time: 0.29 sec

Simulation with small search ellipsoid by setting the radii manually

# Search ellipsoid with small radii
snp = gn.deesseinterface.SearchNeighborhoodParameters(
    radiusMode='manual', rx=10, ry=10, rz=0
)

deesse_input2 = gn.deesseinterface.DeesseInput(
    nx=100, ny=100, nz=1,
    nv=1, varname='code',
    TI=ti,
    distanceType='categorical',
    searchNeighborhoodParameters = snp, # set search neighborhood parameters (ellipsoid)
    nneighboringNode=24,
    distanceThreshold=0.02,
    maxScanFraction=0.25,
    npostProcessingPathMax=1,
    seed=444,
    nrealization=1)

# Run deesse
t1 = time.time() # start time
deesse_output2 = gn.deesseinterface.deesseRun(deesse_input2, verbose=2)
t2 = time.time() # end time
print(f'Elapsed time: {t2-t1:.2g} sec')

deesseRun: DeeSse running... [VERSION 3.2 / BUILD NUMBER 20230914 / OpenMP 19 thread(s)]
deesseRun: DeeSse run complete
Elapsed time: 0.16 sec

Simulation with anisotropic search ellipsoid (preferential search in one direction)

# Anisotropic search ellipsoid with preferential search in x-direction
snp = gn.deesseinterface.SearchNeighborhoodParameters(
    anisotropyRatioMode='manual', ax=20, ay=1, az=1
)

deesse_input3 = gn.deesseinterface.DeesseInput(
    nx=100, ny=100, nz=1,
    nv=1, varname='code',
    TI=ti,
    distanceType='categorical',
    searchNeighborhoodParameters = snp, # set search neighborhood parameters (ellipsoid)
    nneighboringNode=24,
    distanceThreshold=0.02,
    maxScanFraction=0.25,
    npostProcessingPathMax=1,
    seed=444,
    nrealization=1)

# Run deesse
t1 = time.time() # start time
deesse_output3 = gn.deesseinterface.deesseRun(deesse_input3)
t2 = time.time() # end time
print(f'Elapsed time: {t2-t1:.2g} sec')

deesseRun: DeeSse running... [VERSION 3.2 / BUILD NUMBER 20230914 / OpenMP 19 thread(s)]
deesseRun: DeeSse run complete
Elapsed time: 0.24 sec

Results and display

# Retrieve the realizations
sim1 = deesse_output1['sim']
sim2 = deesse_output2['sim']
sim3 = deesse_output3['sim']

# Display
plt.subplots(1, 3, figsize=(17,5), sharey=True) # 1 x 3 sub-plots

plt.subplot(1, 3, 1)
gn.imgplot.drawImage2D(sim1[0], categ=True, categVal=categ_val, categCol=categ_col,
                       title='Sim, default search ellipsoid')

plt.subplot(1, 3, 2)
gn.imgplot.drawImage2D(sim2[0], categ=True, categVal=categ_val, categCol=categ_col,
                       title='Sim, small search ellipsoid')

plt.subplot(1, 3, 3)
gn.imgplot.drawImage2D(sim3[0], categ=True, categVal=categ_val, categCol=categ_col,
                       title='Sim, anisotropic search ellipsoid')

plt.show()