Example 21 - Coal Seam Mining

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 3727 m wide (W-E extent) and 2596 m high (N-S extent). The vertical model extent varies between 2400 m and 2475 m. The model represents a coal seam that is outcropping at the surface and was encountered in boreholes at depth.

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_example21.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 and 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/example21/'
gg.download_gemgis_data.download_tutorial_data(filename="example21_coal_seam_mining.zip", dirpath=file_path)

Downloading file 'example21_coal_seam_mining.zip' from 'https://rwth-aachen.sciebo.de/s/AfXRsZywYDbUF34/download?path=%2Fexample21_coal_seam_mining.zip' to 'C:\Users\ale93371\Documents\gemgis\docs\getting_started\example\data\example21'.

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_example21.png')
plt.figure(figsize=(10, 10))
imgplot = plt.imshow(img)
plt.axis('off')
plt.tight_layout()

topo = gpd.read_file(file_path + 'topo21.shp')
topo['Z'] = topo['Z']*0.3048
topo.head()

	id	Z	geometry
0	None	2423.16	LINESTRING (8.426 1648.306, 35.717 1675.998, 6...
1	None	2426.21	LINESTRING (5.215 1950.921, 79.464 1983.831, 1...
2	None	2429.26	LINESTRING (16.854 2215.007, 64.213 2217.014, ...
3	None	2432.30	LINESTRING (24.179 2563.075, 70.835 2554.346, ...
4	None	2432.30	LINESTRING (957.912 2594.380, 1031.960 2594.98...

Interpolating the contour lines

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

Plotting the raster

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, 3727, 0, 2596], cmap='gist_earth', clim=[2400, 2500])
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, 3727)
ax.set_ylim(0, 2596)

(0.0, 2596.0)

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 + 'raster21.tif', extent=[0, 3727, 0, 2596], 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 + 'raster21.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_example21.png')
plt.figure(figsize=(10, 10))
imgplot = plt.imshow(img)
plt.axis('off')
plt.tight_layout()

interfaces = gpd.read_file(file_path + 'interfaces21.shp')
interfaces.head()

	id	formation	geometry
0	None	Coal	LINESTRING (53.076 2181.695, 122.910 2160.023,...
1	None	Coal	LINESTRING (1107.213 927.687, 1168.017 852.435...
2	None	Fault	LINESTRING (523.855 319.647, 578.639 399.114, ...

Extracting Z coordinate from Digital Elevation Model

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

	formation	geometry	X	Y	Z
87	Fault	POINT (2604.437 2592.875)	2604.44	2592.88	2458.99
73	Fault	POINT (1608.696 1614.592)	1608.70	1614.59	2442.26
59	Fault	POINT (523.855 319.647)	523.86	319.65	2422.09
60	Fault	POINT (578.639 399.114)	578.64	399.11	2424.08
61	Fault	POINT (657.504 507.477)	657.50	507.48	2425.55

boreholes = gpd.read_file(file_path + 'points21.shp')
boreholes = gg.vector.extract_xy(gdf=boreholes)
boreholes['Z'] = boreholes['Z']*0.3048
boreholes.head()

	formation	Z	geometry	X	Y
0	CoalTop	2421.64	POINT (627.403 2088.984)	627.40	2088.98
1	CoalTop	2426.21	POINT (53.076 2493.542)	53.08	2493.54
2	CoalTop	2416.45	POINT (1033.164 2386.382)	1033.16	2386.38
3	CoalTop	2414.02	POINT (1252.300 2547.724)	1252.30	2547.72
4	CoalTop	2425.29	POINT (916.975 1506.229)	916.97	1506.23

import pandas as pd
interfaces_coords = pd.concat([interfaces_coords, boreholes])
interfaces_coords = interfaces_coords[interfaces_coords['formation'].isin(['Fault', 'CoalTop', 'Coal'])].reset_index()
interfaces_coords

	index	formation	geometry	X	Y	Z
0	87	Fault	POINT (2604.437 2592.875)	2604.44	2592.88	2458.99
1	73	Fault	POINT (1608.696 1614.592)	1608.70	1614.59	2442.26
2	59	Fault	POINT (523.855 319.647)	523.86	319.65	2422.09
3	60	Fault	POINT (578.639 399.114)	578.64	399.11	2424.08
4	61	Fault	POINT (657.504 507.477)	657.50	507.48	2425.55
...	...	...	...	...	...	...
127	39	Coal	POINT (3204.651 1400.273)	3204.65	1400.27	2416.45
128	40	Coal	POINT (3350.340 1792.790)	3350.34	1792.79	2411.88
129	41	Coal	POINT (3423.787 1996.273)	3423.79	1996.27	2409.75
130	42	Coal	POINT (3520.110 293.760)	3520.11	293.76	2429.87
131	43	Coal	POINT (3673.023 733.235)	3673.02	733.23	2424.68

132 rows × 6 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, 3727)
ax.set_ylim(0, 2596)

(0.0, 2596.0)

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.

import matplotlib.pyplot as plt
import matplotlib.image as mpimg
img = mpimg.imread('../images/orientations_example21.png')
plt.figure(figsize=(10, 10))
imgplot = plt.imshow(img)
plt.axis('off')
plt.tight_layout()

strikes = gpd.read_file(file_path + 'strikes21.shp')
strikes['Z'] = strikes['Z']*0.3048
strikes.head()

	id	formation	Z	geometry
0	1	Coal1	2426.21	LINESTRING (335.423 2099.821, 499.173 1840.952)
1	2	Coal1	2429.26	LINESTRING (564.191 1250.972, 3.108 2216.613)
2	2	Coal2	2432.30	LINESTRING (1619.532 600.790, 2945.181 207.069)
3	1	Coal2	2429.26	LINESTRING (2364.833 540.588, 2570.724 477.978)

orientations_fault = gpd.read_file(file_path + 'orientations21_fault.shp')
orientations_fault = gg.vector.extract_xyz(gdf=orientations_fault, dem=topo_raster)
orientations_fault

	formation	dip	azimuth	polarity	geometry	X	Y	Z
0	Fault	60.00	305.00	1.00	POINT (670.146 526.140)	670.15	526.14	2425.71
1	Fault	60.00	305.00	1.00	POINT (1410.631 1387.029)	1410.63	1387.03	2439.39
2	Fault	60.00	305.00	1.00	POINT (2034.324 2095.005)	2034.32	2095.00	2447.71

Calculate Orientations for each formation

orientations_coal1 = gg.vector.calculate_orientations_from_strike_lines(gdf=strikes[strikes['formation'] == 'Coal1'].sort_values(by='Z', ascending=True).reset_index())
orientations_coal1

	dip	azimuth	Z	geometry	polarity	formation	X	Y
0	0.76	59.69	2427.73	POINT (350.474 1852.089)	1.00	Coal1	350.47	1852.09

orientations_coal2 = gg.vector.calculate_orientations_from_strike_lines(gdf=strikes[strikes['formation'] == 'Coal2'].sort_values(by='Z', ascending=True).reset_index())
orientations_coal2

	dip	azimuth	Z	geometry	polarity	formation	X	Y
0	1.14	16.55	2430.78	POINT (2375.067 456.607)	1.00	Coal2	2375.07	456.61

Merging Orientations

import pandas as pd
orientations = pd.concat([orientations_coal1, orientations_coal2, orientations_fault]).reset_index()
orientations['formation'] = ['Coal', 'Coal', 'Fault', 'Fault', 'Fault']
orientations = orientations[orientations['formation'].isin(['Coal', 'Fault'])]
orientations

	index	dip	azimuth	Z	geometry	polarity	formation	X	Y
0	0	0.76	59.69	2427.73	POINT (350.474 1852.089)	1.00	Coal	350.47	1852.09
1	0	1.14	16.55	2430.78	POINT (2375.067 456.607)	1.00	Coal	2375.07	456.61
2	0	60.00	305.00	2425.71	POINT (670.146 526.140)	1.00	Fault	670.15	526.14
3	1	60.00	305.00	2439.39	POINT (1410.631 1387.029)	1.00	Fault	1410.63	1387.03
4	2	60.00	305.00	2447.71	POINT (2034.324 2095.005)	1.00	Fault	2034.32	2095.00

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()
ax.set_xlabel('X [m]')
ax.set_ylabel('Y [m]')
ax.set_xlim(0, 3727)
ax.set_ylim(0, 2596)

(0.0, 2596.0)

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('Model21')
geo_model

Model21  2022-04-05 11:19

Initiate Data

gp.init_data(geo_model, [0, 3727, 0, 2596, 2400, 2475], [100, 100, 100],
             surface_points_df=interfaces_coords[interfaces_coords['Z'] != 0],
             orientations_df=orientations,
             default_values=True)

Active grids: ['regular']

Model21  2022-04-05 11:19

Model Surfaces

geo_model.surfaces

	surface	series	order_surfaces	color	id
0	Fault	Default series	1	#015482	1
1	Coal	Default series	2	#9f0052	2
2	CoalTop	Default series	3	#ffbe00	3

Mapping the Stack to Surfaces

gp.map_stack_to_surfaces(geo_model,
                         {
                          'Fault1': ('Fault'),
                          'Strata1': ('CoalTop', 'Coal'),
                         },
                         remove_unused_series=True)
geo_model.add_surfaces('Basement')
geo_model.set_is_fault(['Fault1'])

Fault colors changed. If you do not like this behavior, set change_color to False.

	order_series	BottomRelation	isActive	isFault	isFinite
Fault1	1	Fault	True	True	False
Strata1	2	Erosion	True	False	False

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	Fault	Fault1	1	#527682	1	29	3
1	Coal	Strata1	1	#9f0052	2	81	2
2	CoalTop	Strata1	2	#ffbe00	3	22	0
3	Basement	Strata1	3	#728f02	4	0	0

Loading Digital Elevation Model

geo_model.set_topography(
    source='gdal', filepath=file_path + 'raster21.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([[  18.635     ,   12.98      , 2400.375     ],
       [  18.635     ,   12.98      , 2401.125     ],
       [  18.635     ,   12.98      , 2401.875     ],
       ...,
       [3722.04388298, 2571.13409962, 2462.30371094],
       [3722.04388298, 2581.08045977, 2463.51391602],
       [3722.04388298, 2591.02681992, 2464.8894043 ]])

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 0x1799e93afa0>

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:  1
Compilation Done!
Kriging values:
                    values
range             4542.62
$C_o$           491318.33
drift equations    [3, 3]

<gempy.core.interpolator.InterpolatorModel at 0x17997fa6520>

Computing Model

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

Plotting Cross Sections

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 0x1799fb532b0>

Plotting 3D Model

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