Perth basin.

import os

# Importing GemPy
import gempy as gp

# Importing auxiliary libraries
import matplotlib
matplotlib.rcParams['figure.figsize'] = (20.0, 10.0)
os.environ["THEANO_FLAGS"] = "mode=FAST_RUN,device=cuda"

data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'
geo_model = gp.create_model('Perth_Basin')

gp.init_data(geo_model,
             extent=[337000, 400000, 6640000, 6710000, -18000, 1000],
             resolution=[100, 100, 100],
             path_i=data_path + "/data/input_data/Perth_basin/Paper_GU2F_sc_faults_topo_Points.csv",
             path_o=data_path + "/data/input_data/Perth_basin/Paper_GU2F_sc_faults_topo_Foliations.csv")

Out:

Active grids: ['regular']

Perth_Basin  2020-12-07 16:51

geo_model.surfaces

	surface	series	order_surfaces	color	id
0	Lesueur	Default series	1	#015482	1
1	Permian	Default series	2	#9f0052	2
2	Woodada_Kockatea	Default series	3	#ffbe00	3
3	Yarragadee	Default series	4	#728f02	4
4	Eneabba	Default series	5	#443988	5
5	Cattamarra	Default series	6	#ff3f20	6
6	Cadda	Default series	7	#5DA629	7
7	Cretaceous	Default series	8	#4878d0	8
8	Darling	Default series	9	#ee854a	9
9	Urella_North	Default series	10	#6acc64	10
10	Eneabba_South	Default series	11	#d65f5f	11
11	Coomallo	Default series	12	#956cb4	12
12	Hypo_fault_E	Default series	13	#8c613c	13
13	Hypo_fault_W	Default series	14	#dc7ec0	14
14	Urella_South	Default series	15	#797979	15
15	Abrolhos_Transfer	Default series	16	#d5bb67	16
16	basement	Basement	1	#82c6e2	17

del_surfaces = ['Cadda', 'Woodada_Kockatea', 'Cattamarra']

geo_model.delete_surfaces(del_surfaces, remove_data=True)

	surface	series	order_surfaces	color	id
0	Lesueur	Default series	1	#015482	1
1	Permian	Default series	2	#9f0052	2
3	Yarragadee	Default series	3	#728f02	3
4	Eneabba	Default series	4	#443988	4
7	Cretaceous	Default series	5	#4878d0	5
8	Darling	Default series	6	#ee854a	6
9	Urella_North	Default series	7	#6acc64	7
10	Eneabba_South	Default series	8	#d65f5f	8
11	Coomallo	Default series	9	#956cb4	9
12	Hypo_fault_E	Default series	10	#8c613c	10
13	Hypo_fault_W	Default series	11	#dc7ec0	11
14	Urella_South	Default series	12	#797979	12
15	Abrolhos_Transfer	Default series	13	#d5bb67	13
16	basement	Basement	1	#82c6e2	14

%debug

geo_model.stack

	order_series	BottomRelation	isActive	isFault	isFinite
Default series	1	Erosion	True	False	False
Basement	2	Erosion	False	False	False

gp.map_stack_to_surfaces(geo_model,
                          {"fault_Abrolhos_Transfer": ["Abrolhos_Transfer"],
                           "fault_Coomallo": ["Coomallo"],
                           "fault_Eneabba_South": ["Eneabba_South"],
                           "fault_Hypo_fault_W": ["Hypo_fault_W"],
                           "fault_Hypo_fault_E": ["Hypo_fault_E"],
                           "fault_Urella_North": ["Urella_North"],
                           "fault_Urella_South": ["Urella_South"],
                           "fault_Darling": ["Darling"],
                           "Sedimentary_Series": ['Cretaceous',
                                                  'Yarragadee',
                                                  'Eneabba',
                                                  'Lesueur',
                                                  'Permian']
                           })

	surface	series	order_surfaces	color	id
15	Abrolhos_Transfer	fault_Abrolhos_Transfer	1	#d5bb67	1
11	Coomallo	fault_Coomallo	1	#956cb4	2
10	Eneabba_South	fault_Eneabba_South	1	#d65f5f	3
13	Hypo_fault_W	fault_Hypo_fault_W	1	#dc7ec0	4
12	Hypo_fault_E	fault_Hypo_fault_E	1	#8c613c	5
9	Urella_North	fault_Urella_North	1	#6acc64	6
14	Urella_South	fault_Urella_South	1	#797979	7
8	Darling	fault_Darling	1	#ee854a	8
0	Lesueur	Sedimentary_Series	1	#015482	9
1	Permian	Sedimentary_Series	2	#9f0052	10
3	Yarragadee	Sedimentary_Series	3	#728f02	11
4	Eneabba	Sedimentary_Series	4	#443988	12
7	Cretaceous	Sedimentary_Series	5	#4878d0	13
16	basement	Basement	1	#82c6e2	14

geo_model.series

	order_series	BottomRelation	isActive	isFault	isFinite
fault_Abrolhos_Transfer	1	Erosion	True	False	False
fault_Coomallo	2	Erosion	True	False	False
fault_Eneabba_South	3	Erosion	True	False	False
fault_Hypo_fault_W	4	Erosion	True	False	False
fault_Hypo_fault_E	5	Erosion	True	False	False
fault_Urella_North	6	Erosion	True	False	False
fault_Urella_South	7	Erosion	True	False	False
fault_Darling	8	Erosion	True	False	False
Sedimentary_Series	9	Erosion	True	False	False
Basement	10	Erosion	False	False	False

order_series = ["fault_Abrolhos_Transfer",
                "fault_Coomallo",
                "fault_Eneabba_South",
                "fault_Hypo_fault_W",
                "fault_Hypo_fault_E",
                "fault_Urella_North",
                "fault_Darling",
                "fault_Urella_South",
                "Sedimentary_Series", 'Basement']

geo_model.reorder_series(order_series)

	order_series	BottomRelation	isActive	isFault	isFinite
fault_Abrolhos_Transfer	1	Erosion	True	False	False
fault_Coomallo	2	Erosion	True	False	False
fault_Eneabba_South	3	Erosion	True	False	False
fault_Hypo_fault_W	4	Erosion	True	False	False
fault_Hypo_fault_E	5	Erosion	True	False	False
fault_Urella_North	6	Erosion	True	False	False
fault_Darling	7	Erosion	True	False	False
fault_Urella_South	8	Erosion	True	False	False
Sedimentary_Series	9	Erosion	True	False	False
Basement	10	Erosion	False	False	False

Drop input data from the deleted series:

geo_model.surface_points.df.dropna(inplace=True)
geo_model.orientations.df.dropna(inplace=True)

Select which series are faults

geo_model.faults

	order_series	BottomRelation	isActive	isFault	isFinite
fault_Abrolhos_Transfer	1	Erosion	True	False	False
fault_Coomallo	2	Erosion	True	False	False
fault_Eneabba_South	3	Erosion	True	False	False
fault_Hypo_fault_W	4	Erosion	True	False	False
fault_Hypo_fault_E	5	Erosion	True	False	False
fault_Urella_North	6	Erosion	True	False	False
fault_Darling	7	Erosion	True	False	False
fault_Urella_South	8	Erosion	True	False	False
Sedimentary_Series	9	Erosion	True	False	False
Basement	10	Erosion	False	False	False

geo_model.set_is_fault(["fault_Abrolhos_Transfer",
                        "fault_Coomallo",
                        "fault_Eneabba_South",
                        "fault_Hypo_fault_W",
                        "fault_Hypo_fault_E",
                        "fault_Urella_North",
                        "fault_Darling",
                        "fault_Urella_South"])

Out:

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

	order_series	BottomRelation	isActive	isFault	isFinite
fault_Abrolhos_Transfer	1	Fault	True	True	False
fault_Coomallo	2	Fault	True	True	False
fault_Eneabba_South	3	Fault	True	True	False
fault_Hypo_fault_W	4	Fault	True	True	False
fault_Hypo_fault_E	5	Fault	True	True	False
fault_Urella_North	6	Fault	True	True	False
fault_Darling	7	Fault	True	True	False
fault_Urella_South	8	Fault	True	True	False
Sedimentary_Series	9	Erosion	True	False	False
Basement	10	Erosion	False	False	False

Fault Network

geo_model.faults.faults_relations_df

	fault_Abrolhos_Transfer	fault_Coomallo	fault_Eneabba_South	fault_Hypo_fault_W	fault_Hypo_fault_E	fault_Urella_North	fault_Darling	fault_Urella_South	Sedimentary_Series	Basement
fault_Abrolhos_Transfer	False	False	False	False	False	False	False	False	True	True
fault_Coomallo	False	False	False	False	False	False	False	False	True	True
fault_Eneabba_South	False	False	False	False	False	False	False	False	True	True
fault_Hypo_fault_W	False	False	False	False	False	False	False	False	True	True
fault_Hypo_fault_E	False	False	False	False	False	False	False	False	True	True
fault_Urella_North	False	False	False	False	False	False	False	False	True	True
fault_Darling	False	False	False	False	False	False	False	False	True	True
fault_Urella_South	False	False	False	False	False	False	False	False	True	True
Sedimentary_Series	False	False	False	False	False	False	False	False	False	False
Basement	False	False	False	False	False	False	False	False	False	False

fr = geo_model.faults.faults_relations_df.values

fr[:, :-2] = False
fr

Out:

array([[False, False, False, False, False, False, False, False,  True,
         True],
       [False, False, False, False, False, False, False, False,  True,
         True],
       [False, False, False, False, False, False, False, False,  True,
         True],
       [False, False, False, False, False, False, False, False,  True,
         True],
       [False, False, False, False, False, False, False, False,  True,
         True],
       [False, False, False, False, False, False, False, False,  True,
         True],
       [False, False, False, False, False, False, False, False,  True,
         True],
       [False, False, False, False, False, False, False, False,  True,
         True],
       [False, False, False, False, False, False, False, False, False,
        False],
       [False, False, False, False, False, False, False, False, False,
        False]])

geo_model.set_fault_relation(fr)

	fault_Abrolhos_Transfer	fault_Coomallo	fault_Eneabba_South	fault_Hypo_fault_W	fault_Hypo_fault_E	fault_Urella_North	fault_Darling	fault_Urella_South	Sedimentary_Series	Basement
fault_Abrolhos_Transfer	False	False	False	False	False	False	False	False	True	True
fault_Coomallo	False	False	False	False	False	False	False	False	True	True
fault_Eneabba_South	False	False	False	False	False	False	False	False	True	True
fault_Hypo_fault_W	False	False	False	False	False	False	False	False	True	True
fault_Hypo_fault_E	False	False	False	False	False	False	False	False	True	True
fault_Urella_North	False	False	False	False	False	False	False	False	True	True
fault_Darling	False	False	False	False	False	False	False	False	True	True
fault_Urella_South	False	False	False	False	False	False	False	False	True	True
Sedimentary_Series	False	False	False	False	False	False	False	False	False	False
Basement	False	False	False	False	False	False	False	False	False	False

%matplotlib inline

gp.plot_2d(geo_model, direction=['z'])

Out:

<gempy.plot.visualization_2d.Plot2D object at 0x7f8f6d4b92e0>

geo_model.set_topography(source='random')

Out:

[-2800.  1000.]
Active grids: ['regular' 'topography']

Grid Object. Values:
array([[ 3.37315000e+05,  6.64035000e+06, -1.79050000e+04],
       [ 3.37315000e+05,  6.64035000e+06, -1.77150000e+04],
       [ 3.37315000e+05,  6.64035000e+06, -1.75250000e+04],
       ...,
       [ 4.00000000e+05,  6.70858586e+06, -9.71872329e+02],
       [ 4.00000000e+05,  6.70929293e+06, -9.90037309e+02],
       [ 4.00000000e+05,  6.71000000e+06, -9.20856518e+02]])

gp.plot_3d(geo_model)

Out:

<gempy.plot.vista.GemPyToVista object at 0x7f8f98be0cd0>

interp_data = gp.set_interpolator(geo_model,
                                  compile_theano=True,
                                  theano_optimizer='fast_run', gradient=False,
                                  dtype='float32')

Out:

Setting kriging parameters to their default values.
Compiling theano function...
Level of Optimization:  fast_run
Device:  cpu
Precision:  float32
Number of faults:  8
Compilation Done!
Kriging values:
                                          values
range                                   9.6e+04
$C_o$                                   2.2e+08
drift equations  [3, 3, 3, 3, 3, 3, 3, 3, 3, 3]

gp.compute_model(geo_model)

Out:

Lithology ids
  [14. 14. 14. ... 14. 13. 13.]

gp.plot_2d(geo_model, cell_number=[25])

Out:

<gempy.plot.visualization_2d.Plot2D object at 0x7f8f58b16580>

gp.plot_2d(geo_model, cell_number=[25], series_n=-1, show_scalar=True)

Out:

<gempy.plot.visualization_2d.Plot2D object at 0x7f8f984b4c40>

gp.plot_2d(geo_model, cell_number=[12], direction=["y"], show_data=True, show_topography=True)

Out:

<gempy.plot.visualization_2d.Plot2D object at 0x7f8f6e101b20>

sphinx_gallery_thumbnail_number = 6

gp.plot_3d(geo_model, show_topography=True)

Out:

<gempy.plot.vista.GemPyToVista object at 0x7f8f9870f100>

Times

Fast run

• 1M voxels:

  • CPU: intel® Core™ i7-7700HQ CPU @ 2.80GHz × 8 15 s ± 1.02 s per loop (mean ± std. dev. of 7 runs, 1 loop each)

  • GPU (4gb) not enough memmory

  • Ceres 1M voxels 2080 851 ms

• 250k voxels

  • GPU 1050Ti: 3.11 s ± 11.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

  • CPU: intel® Core™ i7-7700HQ CPU @ 2.80GHz × 8 2.27 s ± 47.3 ms

Fast Compile

• 250k voxels

  • GPU 1050Ti: 3.7 s ± 11.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

  • CPU: intel® Core™ i7-7700HQ CPU @ 2.80GHz × 8 14.2 s ± 51.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%%timeit gp.compute_model(geo_model)

ver = np.load(‘ver.npy’) sim = np.load(‘sim.npy’) lith_block = np.load(‘lith.npy’)

gp.save_model(geo_model)

Out:

True