Alesmodel: Plotting Sections and Maps

# %% # .. admonition:: Explanation # # This model is generally quite unstable and requires float64 precision to find a solution. # The lack of data in one corner for the TRIAS and LIAS series causes the model to bend unrealistically, # eroding the CARBO layer, which disappears in that section. The simple way to solve this is by adding more data in that area. # However, I left it as is because I did not construct the model. #

import gempy as gp
import gempy_viewer as gpv
import os

Setting up paths

cwd = os.getcwd()
data_path = os.path.join(cwd, 'examples') if 'examples' not in cwd else os.path.join(cwd, '../..')

path_interf = os.path.join(data_path, "data/input_data/AlesModel/2018_interf.csv")
path_orient = os.path.join(data_path, "data/input_data/AlesModel/2018_orient_clust_n_init5_0.csv")
path_dem = os.path.join(data_path, "data/input_data/AlesModel/_cropped_DEM_coarse.tif")

Creating the geological model

geo_model: gp.data.GeoModel = gp.create_geomodel(
    project_name='Claudius',
    extent=[729550.0, 751500.0, 1913500.0, 1923650.0, -1800.0, 800.0],
    resolution=None,
    refinement=6,
    importer_helper=gp.data.ImporterHelper(
        path_to_orientations=path_orient,
        path_to_surface_points=path_interf,
    )
)

Setting up the section grid

gp.set_section_grid(
    grid=geo_model.grid,
    section_dict={
            'section1': ([732000, 1916000], [745000, 1916000], [200, 150])
    }
)

Active grids: GridTypes.NONE|SECTIONS|OCTREE

	start	stop	resolution	dist
section1	[732000, 1916000]	[745000, 1916000]	[200, 150]	13000.0

Sorting lithologies

gp.map_stack_to_surfaces(
    gempy_model=geo_model,
    mapping_object={
            'fault_left': 'fault_left',
            'fault_right': 'fault_right',
            'fault_lr': 'fault_lr',
            'Trias_Series': ('TRIAS', 'LIAS'),
            'Carbon_Series': 'CARBO',
            'Basement_Series': 'basement'
    },
    remove_unused_series=True
)

Could not find element 'basement' in any group.

Structural Groups:	StructuralGroup: Name: fault_left Structural Relation: StackRelationType.ERODE Elements: StructuralElement: Name: fault_left StructuralGroup: Name: fault_right Structural Relation: StackRelationType.ERODE Elements: StructuralElement: Name: fault_right StructuralGroup: Name: fault_lr Structural Relation: StackRelationType.ERODE Elements: StructuralElement: Name: fault_lr StructuralGroup: Name: Trias_Series Structural Relation: StackRelationType.ERODE Elements: StructuralElement: Name: TRIAS StructuralElement: Name: LIAS StructuralGroup: Name: Carbon_Series Structural Relation: StackRelationType.ERODE Elements: StructuralElement: Name: CARBO
Fault Relations:	fault_left fault_righ... fault_lr Trias_Seri... Carbon_Ser... fault_left fault_right fault_lr Trias_Series Carbon_Series
	True False

Changing colors

geo_model.structural_frame.get_element_by_name("LIAS").color = "#015482"
geo_model.structural_frame.get_element_by_name("TRIAS").color = "#9f0052"
geo_model.structural_frame.get_element_by_name("CARBO").color = "#ffbe00"

Plotting the 2D model

gpv.plot_2d(geo_model, direction='y')

<gempy_viewer.modules.plot_2d.visualization_2d.Plot2D object at 0x7f8281b872b0>

Plotting section traces

gpv.plot_section_traces(geo_model)

<function plot_section_traces at 0x7f82811abf40>

Setting faults

gp.set_is_fault(
    frame=geo_model.structural_frame,
    fault_groups=[
            geo_model.structural_frame.get_group_by_name('fault_left'),
            geo_model.structural_frame.get_group_by_name('fault_right'),
            geo_model.structural_frame.get_group_by_name('fault_lr')
    ],
    change_color=True
)

Structural Groups:	StructuralGroup: Name: fault_left Structural Relation: StackRelationType.FAULT Elements: StructuralElement: Name: fault_left StructuralGroup: Name: fault_right Structural Relation: StackRelationType.FAULT Elements: StructuralElement: Name: fault_right StructuralGroup: Name: fault_lr Structural Relation: StackRelationType.FAULT Elements: StructuralElement: Name: fault_lr StructuralGroup: Name: Trias_Series Structural Relation: StackRelationType.ERODE Elements: StructuralElement: Name: TRIAS StructuralElement: Name: LIAS StructuralGroup: Name: Carbon_Series Structural Relation: StackRelationType.ERODE Elements: StructuralElement: Name: CARBO
Fault Relations:	fault_left fault_righ... fault_lr Trias_Seri... Carbon_Ser... fault_left fault_right fault_lr Trias_Series Carbon_Series
	True False

Setting topography from file

gp.set_topography_from_file(
    grid=geo_model.grid,
    filepath=path_dem,
    crop_to_extent=[729550.0, 751500.0, 1913500.0, 1923650.0]
)

# Plotting 3D model with topography
gpv.plot_3d(geo_model, show_topography=True, ve=1, image=True)

Active grids: GridTypes.NONE|SECTIONS|TOPOGRAPHY|OCTREE

<gempy_viewer.modules.plot_3d.vista.GemPyToVista object at 0x7f8281566680>

Getting the Carbon Series

carbo = geo_model.structural_frame.get_group_by_name("Carbon_Series")

Modifying interpolation options for better model fitting

geo_model.interpolation_options.number_octree_levels_surface = 4
geo_model.interpolation_options.kernel_options.range = 0.8
gp.modify_surface_points(
    geo_model=geo_model,
    elements_names=["CARBO", "LIAS", "TRIAS"],
    nugget=0.005
)

Structural Groups:	StructuralGroup: Name: fault_left Structural Relation: StackRelationType.FAULT Elements: StructuralElement: Name: fault_left StructuralGroup: Name: fault_right Structural Relation: StackRelationType.FAULT Elements: StructuralElement: Name: fault_right StructuralGroup: Name: fault_lr Structural Relation: StackRelationType.FAULT Elements: StructuralElement: Name: fault_lr StructuralGroup: Name: Trias_Series Structural Relation: StackRelationType.ERODE Elements: StructuralElement: Name: TRIAS StructuralElement: Name: LIAS StructuralGroup: Name: Carbon_Series Structural Relation: StackRelationType.ERODE Elements: StructuralElement: Name: CARBO
Fault Relations:	fault_left fault_righ... fault_lr Trias_Seri... Carbon_Ser... fault_left fault_right fault_lr Trias_Series Carbon_Series
	True False

Displaying the structural frame

print(geo_model.structural_frame)
geo_model.structural_frame

StructuralFrame(
        structural_groups=[
StructuralGroup(
        name=fault_left,
        structural_relation=StackRelationType.FAULT,
        elements=[
Element(
        name=fault_left,
        color=#527682,
        is_active=True
)
]
),
StructuralGroup(
        name=fault_right,
        structural_relation=StackRelationType.FAULT,
        elements=[
Element(
        name=fault_right,
        color=#527682,
        is_active=True
)
]
),
StructuralGroup(
        name=fault_lr,
        structural_relation=StackRelationType.FAULT,
        elements=[
Element(
        name=fault_lr,
        color=#527682,
        is_active=True
)
]
),
StructuralGroup(
        name=Trias_Series,
        structural_relation=StackRelationType.ERODE,
        elements=[
Element(
        name=TRIAS,
        color=#9f0052,
        is_active=True
),
Element(
        name=LIAS,
        color=#015482,
        is_active=True
)
]
),
StructuralGroup(
        name=Carbon_Series,
        structural_relation=StackRelationType.ERODE,
        elements=[
Element(
        name=CARBO,
        color=#ffbe00,
        is_active=True
)
]
)
],
        fault_relations=
[[False, False, False,  True,  True],
 [False, False, False,  True,  True],
 [False, False, False,  True,  True],
 [False, False, False, False, False],
 [False, False, False, False, False]],

Structural Groups:	StructuralGroup: Name: fault_left Structural Relation: StackRelationType.FAULT Elements: StructuralElement: Name: fault_left StructuralGroup: Name: fault_right Structural Relation: StackRelationType.FAULT Elements: StructuralElement: Name: fault_right StructuralGroup: Name: fault_lr Structural Relation: StackRelationType.FAULT Elements: StructuralElement: Name: fault_lr StructuralGroup: Name: Trias_Series Structural Relation: StackRelationType.ERODE Elements: StructuralElement: Name: TRIAS StructuralElement: Name: LIAS StructuralGroup: Name: Carbon_Series Structural Relation: StackRelationType.ERODE Elements: StructuralElement: Name: CARBO
Fault Relations:	fault_left fault_righ... fault_lr Trias_Seri... Carbon_Ser... fault_left fault_right fault_lr Trias_Series Carbon_Series
	True False

Explanation of model characteristics and adjustments This model has characteristics that make it difficult to get the right default values: - It is large, and we want high resolution - Some series have a large conditional number (i.e., the model input is not very stable) To address these issues: - Reduce the chunk size during evaluation to trade speed for memory - Reduce the std of the error parameter in octree refinement, which evaluates fewer voxels but may leave some without refinement Enable debugging options to help tune these parameters.

Setting verbose and condition number options for debugging

geo_model.interpolation_options.evaluation_options.verbose = True
geo_model.interpolation_options.kernel_options.compute_condition_number = True

Observations and parameter adjustments The octree refinement is making the octree grid almost dense, and smaller chunks are needed to avoid running out of memory. Adjusting parameters accordingly:

geo_model.interpolation_options.evaluation_options.octree_error_threshold = 0.5
geo_model.interpolation_options.evaluation_options.evaluation_chunk_size = 50_000

Computing the model with the adjusted settings

geo_model.interpolation_options.mesh_extraction = False
_ = gp.compute_model(
    geo_model,
    engine_config=gp.data.GemPyEngineConfig(
        backend=gp.data.AvailableBackends.PYTORCH,
        use_gpu=True,
        dtype="float64"
    )
)

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Setting Backend To: AvailableBackends.PYTORCH
Condition number: 2917573.740764309.
Chunking done: 19 chunks
Condition number: 1174481.1989650137.
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Condition number: 1642063.3117937273.
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Chunking done: 32 chunks
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Number of voxels marked by stats: 134 of torch.Size([512]).
 Number of voxels marked by corners : 317
Total voxels: 134
Dense Grid would be 512 voxels
Number of voxels marked by stats: 130 of torch.Size([512]).
 Number of voxels marked by corners : 317
Total voxels: 188
Dense Grid would be 512 voxels
Number of voxels marked by stats: 149 of torch.Size([512]).
 Number of voxels marked by corners : 317
Total voxels: 239
Dense Grid would be 512 voxels
Number of voxels marked by stats: 131 of torch.Size([512]).
 Number of voxels marked by corners : 317
Total voxels: 308
Dense Grid would be 512 voxels
Number of voxels marked by stats: 155 of torch.Size([512]).
 Number of voxels marked by corners : 317
Total voxels: 365
Dense Grid would be 512 voxels
Chunking done: 26 chunks
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Number of voxels marked by stats: 995 of torch.Size([3264]).
 Number of voxels marked by corners : 1575
Total voxels: 995
Dense Grid would be 4096 voxels
Number of voxels marked by stats: 964 of torch.Size([3264]).
 Number of voxels marked by corners : 1575
Total voxels: 1411
Dense Grid would be 4096 voxels
Number of voxels marked by stats: 1109 of torch.Size([3264]).
 Number of voxels marked by corners : 1575
Total voxels: 1829
Dense Grid would be 4096 voxels
Number of voxels marked by stats: 1023 of torch.Size([3264]).
 Number of voxels marked by corners : 1575
Total voxels: 2365
Dense Grid would be 4096 voxels
Number of voxels marked by stats: 1203 of torch.Size([3264]).
 Number of voxels marked by corners : 1575
Total voxels: 2761
Dense Grid would be 4096 voxels
Chunking done: 14 chunks
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Chunking done: 1444 chunks
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Chunking done: 1160 chunks
Number of voxels marked by stats: 7774 of torch.Size([23984]).
 Number of voxels marked by corners : 7469
Total voxels: 7774
Dense Grid would be 32768 voxels
Number of voxels marked by stats: 7506 of torch.Size([23984]).
 Number of voxels marked by corners : 7469
Total voxels: 11092
Dense Grid would be 32768 voxels
Number of voxels marked by stats: 8637 of torch.Size([23984]).
 Number of voxels marked by corners : 7469
Total voxels: 14458
Dense Grid would be 32768 voxels
Number of voxels marked by stats: 7964 of torch.Size([23984]).
 Number of voxels marked by corners : 7469
Total voxels: 18652
Dense Grid would be 32768 voxels
Number of voxels marked by stats: 9365 of torch.Size([23984]).
 Number of voxels marked by corners : 7469
Total voxels: 21748
Dense Grid would be 32768 voxels
Chunking done: 104 chunks
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Condition number: 1586673.9519253916.
Chunking done: 1386 chunks
Condition number: 1122611.6721707575.
Chunking done: 1113 chunks

Plotting the 2D model with and without topography

gpv.plot_2d(geo_model, show_topography=False, section_names=['topography'], show_lith=True)
gpv.plot_2d(geo_model, cell_number=[4], direction=['y'], show_topography=True, show_data=True)
gpv.plot_2d(geo_model, cell_number=[-4], direction=['y'], show_topography=True, show_data=True)

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/Users/flow/opt/anaconda3/envs/gempy3_pre/lib/python3.10/site-packages/gempy_viewer/API/_plot_2d_sections_api.py:105: UserWarning: Section contacts not implemented yet. We need to pass scalar field for the sections grid
  warnings.warn(

<gempy_viewer.modules.plot_2d.visualization_2d.Plot2D object at 0x7f826113e740>

Setting thumbnail number for Sphinx-Gallery sphinx_gallery_thumbnail_number = -1

gpv.plot_3d(geo_model, show_lith=True, show_topography=True, kwargs_plot_structured_grid={'opacity': 0.8})

<gempy_viewer.modules.plot_3d.vista.GemPyToVista object at 0x7f827b91f310>