This third notebook in this tutorial series builds upon the first notebook (notebook on Github) and second notebook (notebook on Github) where horizontal and folded layers were modeled, respectively. This notebook illustrates how to create a simple sample model of faulted layers in GemPy
. The model consists of three parallel layers and two faults and has an extent of 1000 m by 1000 m with a vertical extent of 600 m.
If you have not gone through the introduction notebook for the course, please check it out: Introduction Notebook (notebook on Github)
In reality, most geological settings are formed by a concatenation of depositional phases partitioned by unconformity boundaries and subjected to tectonic stresses that displace and deform the layers. While the interpolation is able to represent realistic folding – given enough data – the method fails to describe discontinuities. To overcome this limitation, it is possible to combine several scalar fields to recreate the desired result. So far, the implemented discontinuities in GemPy are unconformities and infinite faults. Both types are computed by specific combinations of independent scalar fields. To display the offset of faults, a drift function is calculated.
Faults require the same input data as layers, interface points and orientations. GemPy
has to be told that faults are present in the model in order to treat them as such. This is done with the function geo_model.set_is_fault()
where you pass the name of the faults.
If you have not installed GemPy
yet, please follow the installation instructions. If you encounter any issues, feel free to open a new discussion at GemPy Discussions. If you encounter an error in the installation process, feel free to also open an issue at GemPy Issues. There, the GemPy
development team will help you out.
For this notebook, we need the pandas
library for the data preparation and matplotlib
for plotting and of course the gempy
library. Any warnings that may appear can be ignored for now.
import pandas as pd
import gempy as gp
import matplotlib.pyplot as plt
For this model, the only thing that needs to be done is loading the already created interface points and orientations. In the next tutorials, you will create the data yourself and process it further to make it usable for GemPy.
We are using the pandas
library to load the interface points that were prepared beforehand and stored as CSV-file. The only information that is needed are the location of the interface point (X
, Y
, Z
) and the formation
it belongs to. You may have to adjust the delimiter
when loading the file.
interfaces = pd.read_csv('../data/model3/model3_interfaces.csv',
delimiter = ';')
interfaces.head()
The orientations will also be loaded using pandas
. In addition to the location and the formation the orientation belongs to, the dip value, azimuth value (dip direction) and a polarity value (mostly set to 1 by default) needs to be provided. As the model will feature faulted layers, the dip and the azimuth values may be variable. Orientations are provided for all three modeled layers.
orientations = pd.read_csv('../data/model3/model3_orientations.csv',
delimiter=';')
orientations.head()
The following part presents the main steps of creating a model in GemPy
.
The creation of a GemPy
Model follows particular steps which will be performed in the following:
gp.create_model()
gp.init_data()
gp.map_stack_to_surfaces()
geo_model.set_is_fault()
gp.set_interpolator()
gp.compute_model()
The first step is to create a new empty GemPy
model by providing a name for it.
geo_model = gp.create_model('Model3_Faulted_Layers')
geo_model
During this step, the extent
of the model (xmin
, xmax
, ymin
, ymax
, zmin
, zmax
) and the resolution
in X
, Y
and Z
direction (res_x
, res_y
, res_z
, equal to the number of cells in each direction) will be set using lists of values.
The interface points (surface_points_df
) and orientations (orientations_df
) will be passed as pandas
DataFrames
.
gp.init_data(geo_model=geo_model,
extent=[0, 1000, 0, 1000, -600, 0],
resolution=[100, 100, 100],
surface_points_df=interfaces,
orientations_df=orientations,
default_values=True)
The model consists of three different layers or surfaces now which all belong to the Default series
. During the next step, the proper Series
will be assigned to the surfaces. Using the surfaces
-attribute again, we can check which layers were loaded.
geo_model.surfaces
The loaded interface points and orientations can again be inspected using the surface_points
- and orientations
-attributes. Using the df
-attribute of this object will convert the displayed table in a pandas
DataFrame
.
geo_model.surface_points.df.head()
geo_model.orientations.head()
During this step, all three layers of the model are assigned to the Strata1
series. We know that the layers modeled here are parallel. If the layers were not parallel as shown in the next models, multiple series would be defined. Further, we define two separate series for Fault1
and Fault2
. We will also add a Basement
here (geo_model.add_surfaces('Basement')
). The order within one series also defines the age relations within this series and has to be according to the depositional events of the layers.
gp.map_stack_to_surfaces(geo_model,
{
'Fault1': ('Fault1'),
'Fault2': ('Fault2'),
'Strata1': ('Layer1', 'Layer2', 'Layer3'),
},
remove_unused_series=True)
geo_model.add_surfaces('Basement')
geo_model.surfaces
geo_model.stack
In addition to Series
for the layers, we defined two Series
containing the two faults. In order for GemPy
to recognize the faults as faults, we need to define them as faults (geo_model.set_is_fault()
).
geo_model.set_is_fault(['Fault1', 'Fault2'])
The input data can now be visualized in 2D using matplotlib
. This might for example be useful to check if all points and measurements are defined the way we want them to. Using the function plot_2d()
, we attain a 2D projection of our data points onto a plane of chosen direction (we can choose this attribute to be either 'x'
, 'y'
, or 'z'
).
gp.plot_2d(geo_model,
direction='z',
show_lith=False,
show_boundaries=False)
plt.grid()
The input data can also be viszualized using the pyvista
package. In this view, the interface points are visible as well as the orientations (marked as arrows) which indicate the normals of each orientation value.
The pyvista
package requires the Visualization Toolkit (VTK) to be installed.
gp.plot_3d(geo_model,
image=False,
plotter_type='basic',
notebook=True)
Once we have made sure that we have defined all our primary information, we can continue with the next step towards creating our geological model: preparing the input data for interpolation.
Setting the interpolator is necessary before computing the actual model. Here, the most important kriging parameters can be defined.
gp.set_interpolator(geo_model,
compile_theano=True,
theano_optimizer='fast_compile',
verbose=[],
update_kriging=False
)
At this point, we have all we need to compute our full model via gp.compute_model()
. By default, this will return two separate solutions in the form of arrays. The first provides information on the lithological formations, the second on the fault network in the model, which is not present in this example.
sol = gp.compute_model(geo_model,
compute_mesh=True)
sol
geo_model.solutions
gp.plot_2d(geo_model,
direction=['x', 'x', 'y', 'y'],
cell_number=[25, 50, 25, 75],
show_topography=False,
show_data=True)
Next to the lithology data, we can also plot the calculated scalar field.
gp.plot_2d(geo_model, direction='y', show_data=False, show_scalar=True, show_lith=False)
The computed model can be visualized in 3D using the pyvista
library. Setting notebook=False
will open an interactive windows and the model can be rotated and zooming is possible.
gpv = gp.plot_3d(geo_model,
image=False,
show_topography=True,
plotter_type='basic',
notebook=True,
show_lith=True)
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Institute for Computational Geoscience, Geothermics and Reservoir Geophysics, RWTH Aachen University & Fraunhofer IEG, Fraunhofer Research Institution for Energy Infrastructures and Geothermal Systems IEG, Authors: Alexander Juestel. For more information contact: alexander.juestel(at)ieg.fraunhofer.de
All notebooks are licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0, http://creativecommons.org/licenses/by/4.0/). References for each displayed map are provided. Most of the maps originate from the books of Powell (1992) and Bennison (1990). References for maps with unknown origin will gladly be added.