Example 22 - Coal Measures#

This example will show how to convert the geological map below using GemGIS to a GemPy model. This example is based on digitized data. The area is 6875 m wide (W-E extent) and 9954 m high (N-S extent). The vertical model extent varies 500 m and 1250 m. The model represents coal measures which were mapped on the surface and at depth using boreholes.

The map has been georeferenced with QGIS. The stratigraphic boundaries were digitized in QGIS. Strikes lines were digitized in QGIS as well and will be used to calculate orientations for the GemPy model. The contour lines were also digitized and will be interpolated with GemGIS to create a topography for the model.

Map Source: An Introduction to Geological Structures and Maps by G.M. Bennison

[1]:

import matplotlib.pyplot as plt
import matplotlib.image as mpimg
img = mpimg.imread('../images/cover_example22.png')
plt.figure(figsize=(10, 10))
imgplot = plt.imshow(img)
plt.axis('off')
plt.tight_layout()
../../_images/getting_started_example_example22_2_0.png

Licensing#

Computational Geosciences and Reservoir Engineering, RWTH Aachen University, Authors: Alexander Juestel. For more information contact: alexander.juestel(at)rwth-aachen.de

This work is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)

Import GemGIS#

If you have installed GemGIS via pip or conda, you can import GemGIS like any other package. If you have downloaded the repository, append the path to the directory where the GemGIS repository is stored and then import GemGIS.

[2]:

import warnings
warnings.filterwarnings("ignore")
import gemgis as gg

Importing Libraries and loading Data#

All remaining packages can be loaded in order to prepare the data and to construct the model. The example data is downloaded from an external server using pooch. It will be stored in a data folder in the same directory where this notebook is stored.

[3]:

import geopandas as gpd
import rasterio

[4]:

file_path = 'data/example22/'
gg.download_gemgis_data.download_tutorial_data(filename="example22_coal_measures.zip", dirpath=file_path)

Downloading file 'example22_coal_measures.zip' from 'https://rwth-aachen.sciebo.de/s/AfXRsZywYDbUF34/download?path=%2Fexample22_coal_measures.zip' to 'C:\Users\ale93371\Documents\gemgis\docs\getting_started\example\data\example22'.

Creating Digital Elevation Model from Contour Lines#

The digital elevation model (DEM) will be created by interpolating contour lines digitized from the georeferenced map using the SciPy Radial Basis Function interpolation wrapped in GemGIS. The respective function used for that is gg.vector.interpolate_raster().

[5]:

import matplotlib.pyplot as plt
import matplotlib.image as mpimg
img = mpimg.imread('../images/dem_example22.png')
plt.figure(figsize=(10, 10))
imgplot = plt.imshow(img)
plt.axis('off')
plt.tight_layout()
../../_images/getting_started_example_example22_10_0.png 
[6]:

topo = gpd.read_file(file_path + 'topo22.shp')
topo.head()

[6]:
id Z geometry
0 None 950 LINESTRING (6001.096 12.580, 6136.515 195.703,...
1 None 900 LINESTRING (5508.664 11.041, 5542.518 117.222,...
2 None 850 LINESTRING (3151.144 4.886, 3160.377 221.864, ...
3 None 950 LINESTRING (1889.286 18.735, 1878.514 141.843,...
4 None 1000 LINESTRING (21.120 7446.770, 227.327 7482.163,...

Interpolating the contour lines#

[7]:

topo_raster = gg.vector.interpolate_raster(gdf=topo, value='Z', method='rbf', res=10)

Plotting the raster#

[8]:

import matplotlib.pyplot as plt

from mpl_toolkits.axes_grid1 import make_axes_locatable

fix, ax = plt.subplots(1, figsize=(10, 10))
topo.plot(ax=ax, aspect='equal', column='Z', cmap='gist_earth')
im = ax.imshow(topo_raster, origin='lower', extent=[0, 6875, 0, 9954], cmap='gist_earth')
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cbar = plt.colorbar(im, cax=cax)
cbar.set_label('Altitude [m]')
ax.set_xlabel('X [m]')
ax.set_ylabel('Y [m]')
ax.set_xlim(0, 6875)
ax.set_ylim(0, 9954)

[8]:

(0.0, 9954.0)
../../_images/getting_started_example_example22_15_1.png

Saving the raster to disc#

After the interpolation of the contour lines, the raster is saved to disc using gg.raster.save_as_tiff(). The function will not be executed as a raster is already provided with the example data.

gg.raster.save_as_tiff(raster=topo_raster, path=file_path + 'raster22.tif', extent=[0, 6875, 0, 9954], crs='EPSG:4326', overwrite_file=True)

Opening Raster#

The previously computed and saved raster can now be opened using rasterio.

[9]:

topo_raster = rasterio.open(file_path + 'raster22.tif')

Interface Points of stratigraphic boundaries#

The interface points will be extracted from LineStrings digitized from the georeferenced map using QGIS. It is important to provide a formation name for each layer boundary. The vertical position of the interface point will be extracted from the digital elevation model using the GemGIS function gg.vector.extract_xyz(). The resulting GeoDataFrame now contains single points including the information about the respective formation.

[10]:

import matplotlib.pyplot as plt
import matplotlib.image as mpimg
img = mpimg.imread('../images/interfaces_example22.png')
plt.figure(figsize=(10, 10))
imgplot = plt.imshow(img)
plt.axis('off')
plt.tight_layout()
../../_images/getting_started_example_example22_21_0.png 
[11]:

interfaces = gpd.read_file(file_path + 'interfaces22.shp')
interfaces.head()

[11]:
id formation geometry
0 None MiddleCoal LINESTRING (7.271 6412.662, 159.617 6449.594, ...
1 None MiddleCoal LINESTRING (2429.423 7811.477, 2543.298 7865.3...
2 None MiddleCoal LINESTRING (2314.009 2839.449, 2423.267 2800.9...
3 None MiddleCoal LINESTRING (6865.930 1990.004, 6756.672 1866.8...
4 None MiddleCoal LINESTRING (824.401 4356.757, 964.436 4259.809...

Extracting Z coordinate from Digital Elevation Model#

[12]:

interfaces_coords = gg.vector.extract_xyz(gdf=interfaces, dem=topo_raster)
interfaces_coords = interfaces_coords.sort_values(by='formation', ascending=False)
interfaces_coords.head()

[12]:
formation geometry X Y Z
0 MiddleCoal POINT (7.271 6412.662) 7.27 6412.66 951.72
205 MiddleCoal POINT (6536.616 5044.623) 6536.62 5044.62 945.45
223 MiddleCoal POINT (1772.333 4008.976) 1772.33 4008.98 1008.61
222 MiddleCoal POINT (1675.385 3935.111) 1675.39 3935.11 1004.46
221 MiddleCoal POINT (1603.059 3916.645) 1603.06 3916.65 1000.21 
[13]:

interfaces_lowercoal = gpd.read_file(file_path + 'interfaces22a.shp')
interfaces_lowercoal['Z'] = interfaces_lowercoal['Z']+600
interfaces_lowercoal.head()

[13]:
id formation Z geometry
0 None LowerCoal 700 LINESTRING (1544.583 9919.704, 1381.465 9491.9...
1 None LowerCoal 650 LINESTRING (2147.813 9941.247, 2003.161 9708.8...
2 None LowerCoal 750 LINESTRING (16.504 3161.069, 290.419 3342.654,...
3 None LowerCoal 800 LINESTRING (3540.473 5306.228, 3508.157 5141.5...
4 None LowerCoal 700 LINESTRING (3365.044 11.041, 3417.365 180.315,... 
[14]:

interfaces_coords_lowercoal = gg.vector.extract_xy(gdf=interfaces_lowercoal)
interfaces_coords_lowercoal = interfaces_coords_lowercoal.sort_values(by='formation', ascending=False)
interfaces_coords_lowercoal.head()

[14]:
formation Z geometry X Y
0 LowerCoal 700.00 POINT (1544.583 9919.704) 1544.58 9919.70
206 LowerCoal 700.00 POINT (3951.346 2165.433) 3951.35 2165.43
213 LowerCoal 700.00 POINT (4414.541 3553.476) 4414.54 3553.48
212 LowerCoal 700.00 POINT (4332.981 3356.503) 4332.98 3356.50
211 LowerCoal 700.00 POINT (4245.267 3102.593) 4245.27 3102.59 
[15]:

import pandas as pd
interfaces_coords = pd.concat([interfaces_coords, interfaces_coords_lowercoal])
interfaces_coords = interfaces_coords[interfaces_coords['formation'].isin(['MiddleCoal', 'LowerCoal'])].reset_index()
interfaces_coords

[15]:
index formation geometry X Y Z
0 0 MiddleCoal POINT (7.271 6412.662) 7.27 6412.66 951.72
1 205 MiddleCoal POINT (6536.616 5044.623) 6536.62 5044.62 945.45
2 223 MiddleCoal POINT (1772.333 4008.976) 1772.33 4008.98 1008.61
3 222 MiddleCoal POINT (1675.385 3935.111) 1675.39 3935.11 1004.46
4 221 MiddleCoal POINT (1603.059 3916.645) 1603.06 3916.65 1000.21
... ... ... ... ... ... ...
635 104 LowerCoal POINT (5250.137 7492.935) 5250.14 7492.94 750.00
636 103 LowerCoal POINT (5247.059 7556.028) 5247.06 7556.03 750.00
637 102 LowerCoal POINT (5205.510 7612.966) 5205.51 7612.97 750.00
638 101 LowerCoal POINT (5156.267 7637.587) 5156.27 7637.59 750.00
639 312 LowerCoal POINT (6862.853 886.647) 6862.85 886.65 750.00

640 rows × 6 columns

Plotting the Interface Points#

[16]:

fig, ax = plt.subplots(1, figsize=(10, 10))

interfaces.plot(ax=ax, column='formation', legend=True, aspect='equal')
interfaces_lowercoal.plot(ax=ax, column='formation', legend=True, aspect='equal')
interfaces_coords.plot(ax=ax, column='formation', legend=True, aspect='equal')
plt.grid()
ax.set_xlabel('X [m]')
ax.set_ylabel('Y [m]')
ax.set_xlim(0, 6875)
ax.set_ylim(0, 9954)

[16]:

(0.0, 9954.0)
../../_images/getting_started_example_example22_29_1.png

Orientations from Strike Lines#

Strike lines connect outcropping stratigraphic boundaries (interfaces) of the same altitude. In other words: the intersections between topographic contours and stratigraphic boundaries at the surface. The height difference and the horizontal difference between two digitized lines is used to calculate the dip and azimuth and hence an orientation that is necessary for GemPy. In order to calculate the orientations, each set of strikes lines/LineStrings for one formation must be given an id number next to the altitude of the strike line. The id field is already predefined in QGIS. The strike line with the lowest altitude gets the id number 1, the strike line with the highest altitude the the number according to the number of digitized strike lines. It is currently recommended to use one set of strike lines for each structural element of one formation as illustrated.

[17]:

import matplotlib.pyplot as plt
import matplotlib.image as mpimg
img = mpimg.imread('../images/orientations_example22.png')
plt.figure(figsize=(10, 10))
imgplot = plt.imshow(img)
plt.axis('off')
plt.tight_layout()
../../_images/getting_started_example_example22_31_0.png 
[18]:

strikes = gpd.read_file(file_path + 'strikes22.shp')
strikes.head()

[18]:
id formation Z geometry
0 4 MiddleCoal1 1050 LINESTRING (5399.405 6655.800, 4311.437 5714.023)
1 3 MiddleCoal1 1000 LINESTRING (5844.133 5890.991, 3757.451 4279.814)
2 2 MiddleCoal1 950 LINESTRING (6116.510 5133.876, 3277.330 3110.287)
3 1 MiddleCoal1 900 LINESTRING (6231.924 4242.882, 3554.323 2383.949)

Calculate Orientations for each formation#

[19]:

orientations_coal1 = gg.vector.calculate_orientations_from_strike_lines(gdf=strikes[strikes['formation'] == 'MiddleCoal1'].sort_values(by='Z', ascending=True).reset_index())
orientations_coal1

[19]:
dip azimuth Z geometry polarity formation X Y
0 3.79 144.85 925.00 POINT (4795.021 3717.749) 1.00 MiddleCoal1 4795.02 3717.75
1 4.24 143.74 975.00 POINT (4748.856 4603.742) 1.00 MiddleCoal1 4748.86 4603.74
2 3.59 141.62 1025.00 POINT (4828.107 5635.157) 1.00 MiddleCoal1 4828.11 5635.16 
[20]:

gradients = gpd.read_file(file_path + 'gradients22.shp')
gradients.head()

[20]:
id formation dZ geometry
0 None LowerCoal 50 LINESTRING (1199.880 9087.185, 1618.448 8957.922)
1 None LowerCoal 50 LINESTRING (664.360 7856.104, 1307.600 7699.141)
2 None LowerCoal 50 LINESTRING (119.607 4772.246, 1193.725 6431.128)
3 None LowerCoal 50 LINESTRING (5207.049 8991.776, 4653.062 9558.074)
4 None LowerCoal 50 LINESTRING (3668.198 7622.199, 3181.921 8173.107) 
[21]:

orientations_lowercoal = gg.vector.extract_orientations_from_map(gdf=gradients)
orientations_lowercoal = gg.vector.extract_xyz(gdf=orientations_lowercoal, dem=topo_raster)
orientations_lowercoal

[21]:
formation geometry azimuth dip X Y polarity Z
0 LowerCoal POINT (1409.164 9022.553) 107.16 6.51 1409.16 9022.55 1.00 973.93
1 LowerCoal POINT (985.980 7777.623) 103.71 4.32 985.98 7777.62 1.00 981.51
2 LowerCoal POINT (656.666 5601.687) 32.92 1.45 656.67 5601.69 1.00 945.90
3 LowerCoal POINT (4930.056 9274.925) 315.63 3.61 4930.06 9274.92 1.00 1090.31
4 LowerCoal POINT (3425.059 7897.653) 318.57 3.89 3425.06 7897.65 1.00 995.92
5 LowerCoal POINT (2487.899 6960.493) 314.81 4.42 2487.90 6960.49 1.00 1019.35
6 LowerCoal POINT (3529.701 7108.222) 315.90 5.13 3529.70 7108.22 1.00 1067.49
7 LowerCoal POINT (2172.434 5789.427) 316.92 4.88 2172.43 5789.43 1.00 1062.76
8 LowerCoal POINT (2386.335 5018.463) 313.43 3.99 2386.33 5018.46 1.00 1034.22
9 LowerCoal POINT (1443.019 3995.127) 309.88 4.69 1443.02 3995.13 1.00 989.47
10 LowerCoal POINT (1890.825 3479.611) 40.77 0.00 1890.82 3479.61 1.00 997.89
11 LowerCoal POINT (4346.831 6503.454) 36.23 1.02 4346.83 6503.45 1.00 1051.44
12 LowerCoal POINT (5197.816 5730.951) 113.56 3.41 5197.82 5730.95 1.00 1057.67
13 LowerCoal POINT (4383.763 4315.208) 122.29 3.91 4383.76 4315.21 1.00 1047.62
14 LowerCoal POINT (3226.547 3827.392) 121.01 3.92 3226.55 3827.39 1.00 976.97
15 LowerCoal POINT (2777.203 3124.137) 115.19 3.33 2777.20 3124.14 1.00 939.69
16 LowerCoal POINT (3598.949 2710.186) 114.22 2.93 3598.95 2710.19 1.00 924.46
17 LowerCoal POINT (3004.953 1058.999) 113.13 2.27 3004.95 1059.00 1.00 859.77
18 LowerCoal POINT (4319.132 1906.906) 103.48 3.39 4319.13 1906.91 1.00 890.47
19 LowerCoal POINT (4683.839 3113.365) 108.71 3.47 4683.84 3113.36 1.00 965.82
20 LowerCoal POINT (5265.525 4350.601) 123.11 4.23 5265.53 4350.60 1.00 1004.28
21 LowerCoal POINT (6033.412 5581.682) 118.78 3.50 6033.41 5581.68 1.00 962.60
22 LowerCoal POINT (6448.902 6720.432) 115.24 3.20 6448.90 6720.43 1.00 1044.93
23 LowerCoal POINT (5911.843 3310.338) 116.91 0.00 5911.84 3310.34 1.00 908.53
24 LowerCoal POINT (6308.866 2057.713) 316.05 4.03 6308.87 2057.71 1.00 858.20
25 LowerCoal POINT (5600.995 1343.686) 327.04 4.28 5600.99 1343.69 1.00 844.53
26 LowerCoal POINT (6331.949 934.352) 311.05 3.35 6331.95 934.35 1.00 931.46
27 LowerCoal POINT (5867.216 477.313) 320.58 3.76 5867.22 477.31 1.00 915.96
28 LowerCoal POINT (2760.276 9053.330) 116.16 0.00 2760.28 9053.33 1.00 1003.53

Merging Orientations#

[22]:

import pandas as pd
orientations = pd.concat([orientations_coal1]).reset_index()
orientations['formation'] = ['MiddleCoal', 'MiddleCoal', 'MiddleCoal']
orientations = pd.concat([orientations, orientations_lowercoal]).reset_index()
orientations = orientations[orientations['formation'].isin(['LowerCoal', 'MiddleCoal'])]
orientations.head()

[22]:
level_0 index dip azimuth Z geometry polarity formation X Y
0 0 0.00 3.79 144.85 925.00 POINT (4795.021 3717.749) 1.00 MiddleCoal 4795.02 3717.75
1 1 1.00 4.24 143.74 975.00 POINT (4748.856 4603.742) 1.00 MiddleCoal 4748.86 4603.74
2 2 2.00 3.59 141.62 1025.00 POINT (4828.107 5635.157) 1.00 MiddleCoal 4828.11 5635.16
3 0 NaN 6.51 107.16 973.93 POINT (1409.164 9022.553) 1.00 LowerCoal 1409.16 9022.55
4 1 NaN 4.32 103.71 981.51 POINT (985.980 7777.623) 1.00 LowerCoal 985.98 7777.62

Plotting the Orientations#

[23]:

fig, ax = plt.subplots(1, figsize=(10, 10))

interfaces.plot(ax=ax, column='formation', legend=True, aspect='equal')
interfaces_lowercoal.plot(ax=ax, column='formation', legend=True, aspect='equal')
interfaces_coords.plot(ax=ax, column='formation', legend=True, aspect='equal')
orientations.plot(ax=ax, color='red', aspect='equal')
plt.grid()
ax.set_xlabel('X [m]')
ax.set_ylabel('Y [m]')
ax.set_xlim(0, 6875)
ax.set_ylim(0, 9954)

[23]:

(0.0, 9954.0)
../../_images/getting_started_example_example22_40_1.png

GemPy Model Construction#

The structural geological model will be constructed using the GemPy package.

[24]:

import gempy as gp

WARNING (theano.configdefaults): g++ not available, if using conda: `conda install m2w64-toolchain`
WARNING (theano.configdefaults): g++ not detected ! Theano will be unable to execute optimized C-implementations (for both CPU and GPU) and will default to Python implementations. Performance will be severely degraded. To remove this warning, set Theano flags cxx to an empty string.
WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.

Creating new Model#

[25]:

geo_model = gp.create_model('Model22')
geo_model

[25]:

Model22  2022-04-05 11:20

Initiate Data#

[26]:

gp.init_data(geo_model, [0, 6875, 0, 9954, 500, 1250], [100, 100, 100],
             surface_points_df=interfaces_coords[interfaces_coords['Z'] != 0],
             orientations_df=orientations,
             default_values=True)

Active grids: ['regular']

[26]:

Model22  2022-04-05 11:20

Model Surfaces#

[27]:

geo_model.surfaces

[27]:
surface series order_surfaces color id
0 MiddleCoal Default series 1 #015482 1
1 LowerCoal Default series 2 #9f0052 2

Mapping the Stack to Surfaces#

[28]:

gp.map_stack_to_surfaces(geo_model,
                         {
                          'Strata1': ('MiddleCoal', 'LowerCoal'),
                         },
                         remove_unused_series=True)
geo_model.add_surfaces('Basement')

[28]:
surface series order_surfaces color id
0 MiddleCoal Strata1 1 #015482 1
1 LowerCoal Strata1 2 #9f0052 2
2 Basement Strata1 3 #ffbe00 3

Adding additional Orientations#

[29]:

geo_model.add_orientations(X=380, Y=9550, Z=1025, surface='MiddleCoal', orientation=[107, 15, 1])
geo_model.add_orientations(X=1700, Y=9775, Z=1025, surface='MiddleCoal', orientation=[107, 15, 1])

[29]:
X Y Z G_x G_y G_z smooth surface
0 4795.02 3717.75 925.00 0.04 -0.05 1.00 0.01 MiddleCoal
1 4748.86 4603.74 975.00 0.04 -0.06 1.00 0.01 MiddleCoal
2 4828.11 5635.16 1025.00 0.04 -0.05 1.00 0.01 MiddleCoal
32 380.00 9550.00 1025.00 0.25 -0.08 0.97 0.01 MiddleCoal
33 1700.00 9775.00 1025.00 0.25 -0.08 0.97 0.01 MiddleCoal
3 1409.16 9022.55 973.93 0.11 -0.03 0.99 0.01 LowerCoal
4 985.98 7777.62 981.51 0.07 -0.02 1.00 0.01 LowerCoal
5 656.67 5601.69 945.90 0.01 0.02 1.00 0.01 LowerCoal
6 4930.06 9274.92 1090.31 -0.04 0.05 1.00 0.01 LowerCoal
7 3425.06 7897.65 995.92 -0.04 0.05 1.00 0.01 LowerCoal
8 2487.90 6960.49 1019.35 -0.05 0.05 1.00 0.01 LowerCoal
9 3529.70 7108.22 1067.49 -0.06 0.06 1.00 0.01 LowerCoal
10 2172.43 5789.43 1062.76 -0.06 0.06 1.00 0.01 LowerCoal
11 2386.33 5018.46 1034.22 -0.05 0.05 1.00 0.01 LowerCoal
12 1443.02 3995.13 989.47 -0.06 0.05 1.00 0.01 LowerCoal
13 1890.82 3479.61 997.89 0.00 0.00 1.00 0.01 LowerCoal
14 4346.83 6503.45 1051.44 0.01 0.01 1.00 0.01 LowerCoal
15 5197.82 5730.95 1057.67 0.05 -0.02 1.00 0.01 LowerCoal
16 4383.76 4315.21 1047.62 0.06 -0.04 1.00 0.01 LowerCoal
17 3226.55 3827.39 976.97 0.06 -0.04 1.00 0.01 LowerCoal
18 2777.20 3124.14 939.69 0.05 -0.02 1.00 0.01 LowerCoal
19 3598.95 2710.19 924.46 0.05 -0.02 1.00 0.01 LowerCoal
20 3004.95 1059.00 859.77 0.04 -0.02 1.00 0.01 LowerCoal
21 4319.13 1906.91 890.47 0.06 -0.01 1.00 0.01 LowerCoal
22 4683.84 3113.36 965.82 0.06 -0.02 1.00 0.01 LowerCoal
23 5265.53 4350.60 1004.28 0.06 -0.04 1.00 0.01 LowerCoal
24 6033.41 5581.68 962.60 0.05 -0.03 1.00 0.01 LowerCoal
25 6448.90 6720.43 1044.93 0.05 -0.02 1.00 0.01 LowerCoal
26 5911.84 3310.34 908.53 0.00 0.00 1.00 0.01 LowerCoal
27 6308.87 2057.71 858.20 -0.05 0.05 1.00 0.01 LowerCoal
28 5600.99 1343.69 844.53 -0.04 0.06 1.00 0.01 LowerCoal
29 6331.95 934.35 931.46 -0.04 0.04 1.00 0.01 LowerCoal
30 5867.22 477.31 915.96 -0.04 0.05 1.00 0.01 LowerCoal
31 2760.28 9053.33 1003.53 0.00 0.00 1.00 0.01 LowerCoal

Showing the Number of Data Points#

[30]:

gg.utils.show_number_of_data_points(geo_model=geo_model)

[30]:
surface series order_surfaces color id No. of Interfaces No. of Orientations
0 MiddleCoal Strata1 1 #015482 1 327 5
1 LowerCoal Strata1 2 #9f0052 2 313 29
2 Basement Strata1 3 #ffbe00 3 0 0

Loading Digital Elevation Model#

[31]:

geo_model.set_topography(
    source='gdal', filepath=file_path + 'raster22.tif')

Cropped raster to geo_model.grid.extent.
depending on the size of the raster, this can take a while...
storing converted file...
Active grids: ['regular' 'topography']

[31]:

Grid Object. Values:
array([[  34.375     ,   49.77      ,  503.75      ],
       [  34.375     ,   49.77      ,  511.25      ],
       [  34.375     ,   49.77      ,  518.75      ],
       ...,
       [6869.98906706, 9928.98994975, 1228.36560059],
       [6869.98906706, 9938.99396985, 1228.96081543],
       [6869.98906706, 9948.99798995, 1229.55761719]])

Plotting Input Data#

[32]:

gp.plot_2d(geo_model, direction='z', show_lith=False, show_boundaries=False)
plt.grid()
../../_images/getting_started_example_example22_58_0.png 
[33]:

gp.plot_3d(geo_model, image=False, plotter_type='basic', notebook=True)
../../_images/getting_started_example_example22_59_0.png 
[33]:

<gempy.plot.vista.GemPyToVista at 0x151a458fca0>

Setting the Interpolator#

[34]:

gp.set_interpolator(geo_model,
                    compile_theano=True,
                    theano_optimizer='fast_compile',
                    verbose=[],
                    update_kriging=False
                    )

Compiling theano function...
Level of Optimization:  fast_compile
Device:  cpu
Precision:  float64
Number of faults:  0
Compilation Done!
Kriging values:
                     values
range             12120.65
$C_o$           3497862.88
drift equations        [3]

[34]:

<gempy.core.interpolator.InterpolatorModel at 0x1519d051df0>

Computing Model#

[35]:

sol = gp.compute_model(geo_model, compute_mesh=True)

Plotting Cross Sections#

[36]:

gp.plot_2d(geo_model, direction=['x', 'x', 'y', 'y'], cell_number=[25, 75, 25, 75], show_topography=True, show_data=False)

[36]:

<gempy.plot.visualization_2d.Plot2D at 0x151a5bcdbb0>
../../_images/getting_started_example_example22_65_1.png

Plotting 3D Model#

[37]:

gpv = gp.plot_3d(geo_model, image=False, show_topography=True,
                 plotter_type='basic', notebook=True, show_lith=True, ve=5)
../../_images/getting_started_example_example22_67_0.png 
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