Example 11 - Horizontal Layers

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 7364 m wide (W-E extent) and 9176 m high (N-S extent). The vertical model extent varies between 0 m and 1000 m. The model represents horizontally deposited layers (blue to purple) above a basement (red). 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

import matplotlib.pyplot as plt
import matplotlib.image as mpimg
img = mpimg.imread('../images/cover_example11.png')
plt.figure(figsize=(10, 10))
imgplot = plt.imshow(img)
plt.axis('off')
plt.tight_layout()

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.

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.

import geopandas as gpd
import rasterio

file_path = 'data/example11/'
gg.download_gemgis_data.download_tutorial_data(filename="example11_horizontal_layers.zip", dirpath=file_path)

Downloading file 'example11_horizontal_layers.zip' from 'https://rwth-aachen.sciebo.de/s/AfXRsZywYDbUF34/download?path=%2Fexample11_horizontal_layers.zip' to 'C:\Users\ale93371\Documents\gemgis\docs\getting_started\example\data\example11'.

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().

import matplotlib.pyplot as plt
import matplotlib.image as mpimg
img = mpimg.imread('../images/dem_example11.png')
plt.figure(figsize=(10, 10))
imgplot = plt.imshow(img)
plt.axis('off')
plt.tight_layout()

topo = gpd.read_file(file_path + 'topo11.shp')
topo.head()

	id	Z	geometry
0	None	300	LINESTRING (5554.550 9169.979, 5469.438 8954.3...
1	None	400	LINESTRING (4595.623 9164.305, 4696.339 8940.1...
2	None	700	LINESTRING (6411.342 6429.379, 6524.824 6460.5...
3	None	600	LINESTRING (7360.338 6813.800, 7170.255 6803.8...
4	None	500	LINESTRING (6784.415 7146.446, 6510.639 7045.7...

Interpolating the contour lines

topo_raster = gg.vector.interpolate_raster(gdf=topo, value='Z', method='rbf', res=15)

Plotting the raster

import matplotlib.pyplot as plt

fix, ax = plt.subplots(1, figsize=(10, 10))
topo.plot(ax=ax, aspect='equal', column='Z', cmap='gist_earth')
im = plt.imshow(topo_raster, origin='lower', extent=[0, 7364, 0, 9176], cmap='gist_earth')
cbar = plt.colorbar(im)
cbar.set_label('Altitude [m]')
ax.set_xlabel('X [m]')
ax.set_ylabel('Y [m]')
ax.set_xlim(0, 7364)
ax.set_ylim(0, 9176)

(0.0, 9176.0)

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 + 'raster11.tif', extent=[0, 7364, 0, 9176], crs='EPSG:4326', overwrite_file=True)

Opening Raster

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

topo_raster = rasterio.open(file_path + 'raster11.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.

import matplotlib.pyplot as plt
import matplotlib.image as mpimg
img = mpimg.imread('../images/interfaces_example11.png')
plt.figure(figsize=(10, 10))
imgplot = plt.imshow(img)
plt.axis('off')
plt.tight_layout()

interfaces = gpd.read_file(file_path + 'interfaces11.shp')
interfaces.head()

	id	formation	geometry
0	None	Limestone	LINESTRING (993.266 8745.838, 1122.353 8748.67...
1	None	Sandstone	LINESTRING (1496.845 9169.979, 1676.998 9043.7...
2	None	Claystone	LINESTRING (4006.933 7206.733, 4012.608 7058.4...
3	None	Schist	LINESTRING (3809.049 9166.432, 3899.834 8898.3...
4	None	Siltstone	LINESTRING (8.807 3915.744, 258.468 3921.418, ...

Extracting Z coordinate from Digital Elevation Model

interfaces_coords = gg.vector.extract_xyz(gdf=interfaces, dem=topo_raster)
interfaces_coords = interfaces_coords[interfaces_coords['formation'].isin(['Limestone', 'Sandstone', 'Claystone', 'Schist', 'Siltstone'])]
interfaces_coords

	formation	geometry	X	Y	Z
0	Limestone	POINT (993.266 8745.838)	993.27	8745.84	800.72
1	Limestone	POINT (1122.353 8748.675)	1122.35	8748.68	801.43
2	Limestone	POINT (1267.043 8743.001)	1267.04	8743.00	800.10
3	Limestone	POINT (1398.966 8710.375)	1398.97	8710.37	802.97
4	Limestone	POINT (1539.401 8647.960)	1539.40	8647.96	802.86
...	...	...	...	...	...
215	Siltstone	POINT (2861.471 3478.836)	2861.47	3478.84	364.48
216	Siltstone	POINT (2776.359 3405.073)	2776.36	3405.07	363.79
217	Siltstone	POINT (2677.062 3300.101)	2677.06	3300.10	357.72
218	Siltstone	POINT (2596.205 3209.316)	2596.21	3209.32	351.91
219	Siltstone	POINT (2472.793 3054.696)	2472.79	3054.70	338.67

220 rows × 5 columns

Plotting the Interface Points

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

interfaces.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, 7364)
ax.set_ylim(0, 9176)

(0.0, 9176.0)

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.

For this example, the orientations were defined by points in QGIS as the layers were deposited horizontally and have no dip.

import matplotlib.pyplot as plt
import matplotlib.image as mpimg
img = mpimg.imread('../images/orientations_example11.png')
plt.figure(figsize=(10, 10))
imgplot = plt.imshow(img)
plt.axis('off')
plt.tight_layout()

orientations = gpd.read_file(file_path + 'orientations11.shp')
orientations = gg.vector.extract_xyz(gdf=orientations, dem=topo_raster)
orientations.head()

	dip	azimuth	polarity	formation	geometry	X	Y	Z
0	0.00	0.00	1.00	Limestone	POINT (1111.004 8516.036)	1111.00	8516.04	867.24
1	0.00	0.00	1.00	Limestone	POINT (2041.560 6791.104)	2041.56	6791.10	842.33
2	0.00	0.00	1.00	Limestone	POINT (747.861 7245.033)	747.86	7245.03	837.07
3	0.00	0.00	1.00	Limestone	POINT (1791.899 7971.321)	1791.90	7971.32	848.08
4	0.00	0.00	1.00	Sandstone	POINT (2540.883 6223.692)	2540.88	6223.69	755.14

Plotting the Orientations

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

interfaces.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()
plt.xlim(0,7364)
plt.ylim(0,9176)
plt.xlabel('X [m]')
plt.ylabel('Y [m]')

Text(123.55887265135695, 0.5, 'Y [m]')

GemPy Model Construction

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

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

geo_model = gp.create_model('Model11')
geo_model

Model11  2022-04-05 11:02

Initiate Data

gp.init_data(geo_model, [0, 7364, 0, 9176, 0, 1000], [100, 100, 100],
             surface_points_df=interfaces_coords[interfaces_coords['Z'] != 0],
             orientations_df=orientations,
             default_values=True)

Active grids: ['regular']

Model11  2022-04-05 11:02

Model Surfaces

geo_model.surfaces

	surface	series	order_surfaces	color	id
0	Limestone	Default series	1	#015482	1
1	Sandstone	Default series	2	#9f0052	2
2	Claystone	Default series	3	#ffbe00	3
3	Schist	Default series	4	#728f02	4
4	Siltstone	Default series	5	#443988	5

Mapping the Stack to Surfaces

gp.map_stack_to_surfaces(geo_model,
                         {
                          'Strata1': ('Limestone', 'Sandstone', 'Claystone', 'Schist', 'Siltstone'),
                         },
                         remove_unused_series=True)
geo_model.add_surfaces('Basement')

	surface	series	order_surfaces	color	id
0	Limestone	Strata1	1	#015482	1
1	Sandstone	Strata1	2	#9f0052	2
2	Claystone	Strata1	3	#ffbe00	3
3	Schist	Strata1	4	#728f02	4
4	Siltstone	Strata1	5	#443988	5
5	Basement	Strata1	6	#ff3f20	6

Showing the Number of Data Points

gg.utils.show_number_of_data_points(geo_model=geo_model)

	surface	series	order_surfaces	color	id	No. of Interfaces	No. of Orientations
0	Limestone	Strata1	1	#015482	1	54	4
1	Sandstone	Strata1	2	#9f0052	2	44	4
2	Claystone	Strata1	3	#ffbe00	3	50	3
3	Schist	Strata1	4	#728f02	4	43	5
4	Siltstone	Strata1	5	#443988	5	29	5
5	Basement	Strata1	6	#ff3f20	6	0	0

Loading Digital Elevation Model

geo_model.set_topography(
    source='gdal', filepath=file_path + 'raster11.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']

Grid Object. Values:
array([[3.68200000e+01, 4.58800000e+01, 5.00000000e+00],
       [3.68200000e+01, 4.58800000e+01, 1.50000000e+01],
       [3.68200000e+01, 4.58800000e+01, 2.50000000e+01],
       ...,
       [7.35650102e+03, 9.13851634e+03, 3.66282013e+02],
       [7.35650102e+03, 9.15350980e+03, 3.66405060e+02],
       [7.35650102e+03, 9.16850327e+03, 3.66539032e+02]])

Defining Custom Section

custom_section = gpd.read_file(file_path + 'customsection11.shp')
custom_section_dict = gg.utils.to_section_dict(custom_section, section_column='name')
geo_model.set_section_grid(custom_section_dict)

Active grids: ['regular' 'topography' 'sections']

	start	stop	resolution	dist
Section1	[2033.048983708095, 9169.978630835749]	[2008.9339736694492, 4.856286148714844]	[100, 80]	9165.15

gp.plot.plot_section_traces(geo_model)

<gempy.plot.visualization_2d.Plot2D at 0x1b5ae7f52e0>

Plotting Input Data

gp.plot_2d(geo_model, direction='z', show_lith=False, show_boundaries=False)
plt.grid()

gp.plot_3d(geo_model, image=False, plotter_type='basic', notebook=True)

<gempy.plot.vista.GemPyToVista at 0x1b5b036d3d0>

Setting the Interpolator

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             11807.94
$C_o$           3319701.71
drift equations        [3]

<gempy.core.interpolator.InterpolatorModel at 0x1b5ae7f5d90>

Computing Model

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

Plotting Cross Sections

gp.plot_2d(geo_model, section_names=['Section1'], show_topography=True, show_data=False)

<gempy.plot.visualization_2d.Plot2D at 0x1b5b16055b0>

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

<gempy.plot.visualization_2d.Plot2D at 0x1b5b1a325b0>

Plotting 3D Model

gpv = gp.plot_3d(geo_model, image=False, show_topography=True,
                 plotter_type='basic', notebook=True, show_lith=True)