Sequential Gaussian Simulations¶

Dataset¶

The 2D Walker Lake dataset will be used for demonstration purposes.

In [1]:

import numpy as np
import geolime as geo
import plotly.io as pio
pio.renderers.default = "sphinx_gallery"

import numpy as np import geolime as geo import plotly.io as pio pio.renderers.default = "sphinx_gallery"

Dataset preprocessing¶

In [2]:

data = geo.datasets.load("walker_lake/walker_lake_sample.csv")
data.head()

data = geo.datasets.load("walker_lake/walker_lake_sample.csv") data.head()

Out[2]:

	x	y	z	v	u	t
0	11.0	8.0	0.0	0.0	NaN	2
1	8.0	30.0	0.0	0.0	NaN	2
2	9.0	48.0	0.0	224.4	NaN	2
3	8.0	69.0	0.0	434.4	NaN	2
4	9.0	90.0	0.0	412.1	NaN	2

In [3]:

point_cloud = geo.PointCloud(name='WalkerLake', xyz=data[['x', 'y', 'z']])
point_cloud.set_property(name='V', data=data['v'])

point_cloud = geo.PointCloud(name='WalkerLake', xyz=data[['x', 'y', 'z']]) point_cloud.set_property(name='V', data=data['v'])

In [4]:

vx = geo.Voxel(
    name='WalkerLake',
    shape=[26, 30],
    axis=[[10., 0., 0.], [0., 10., 0.]],
)

vx = geo.Voxel( name='WalkerLake', shape=[26, 30], axis=[[10., 0., 0.], [0., 10., 0.]], )

Simulations¶

Parameters¶

In [5]:

covariance = (
    7000 * geo.Nugget(dimension=2) 
    + 88000 * geo.Spherical(
        dimension=2, 
        metric=geo.Metric(angles=160,scales=[45,45])
    )
)
neighborhood = geo.MinMaxPointsNeighborhood(
    dimension=2, 
    min_n=1, 
    max_n=25, 
    metric=geo.Metric(angles=90,scales=[40.,40.])
)

covariance = ( 7000 * geo.Nugget(dimension=2) + 88000 * geo.Spherical( dimension=2, metric=geo.Metric(angles=160,scales=[45,45]) ) ) neighborhood = geo.MinMaxPointsNeighborhood( dimension=2, min_n=1, max_n=25, metric=geo.Metric(angles=90,scales=[40.,40.]) )

Initialize Solver¶

In [6]:

sgs_estimator = geo.SGS(
    covariance_model=covariance, 
    neighborhood_model=neighborhood, 
    axes = [geo.Coord.U, geo.Coord.V]
)

sgs_estimator = geo.SGS( covariance_model=covariance, neighborhood_model=neighborhood, axes = [geo.Coord.U, geo.Coord.V] )

Solving¶

In [7]:

sgs_estimator.solve(
    obj=point_cloud, 
    obj_region=None, 
    obj_attribute='V', 
    support=vx, 
    support_region=None, 
    support_attribute='sgs', 
    simulations_number=400
)

sgs_estimator.solve( obj=point_cloud, obj_region=None, obj_attribute='V', support=vx, support_region=None, support_attribute='sgs', simulations_number=400 )

Simulating grade: 100%|██████████████████████████████████| Time: 0:00:12

Plotting Results¶

Mean of the results is store as an attribute and can be display.

In [8]:

geo.plot(vx, property="sgs_mean", agg_method="mean", width=600, height=650)

geo.plot(vx, property="sgs_mean", agg_method="mean", width=600, height=650)

Separate realization can also be displayed.

In [9]:

geo.plot(vx, property="sgs_1", agg_method="mean", width=600, height=650)

geo.plot(vx, property="sgs_1", agg_method="mean", width=600, height=650)

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Last update: 2023-10-30