.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "content/tutorials/05-dcr/plot_gen_3_3d_to_2d.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_content_tutorials_05-dcr_plot_gen_3_3d_to_2d.py: Convert 3D DC/IP Data to 2D Lines ================================= 3D DC/IP surveys are frequently comprised of a set of 2D survey lines. In this case, the 3D survey can be parsed into a list of 2D surveys; which can be imaged or inverted independently. In this tutorial, we focus on the following: - Loading and plotting the distribution of 3D DC/IP data using a 3D pseudo-section - Parsing the 3D survey geometry and associated data to a set a 2D surveys - Plotting data for each 2D survey on a 2D pseudo-section - Including survey topography when plotting pseudo-sections In this case, the survey consists of dipole-dipole data for three East-West lines and two North-South lines. .. GENERATED FROM PYTHON SOURCE LINES 22-25 Import modules -------------- .. GENERATED FROM PYTHON SOURCE LINES 25-54 .. code-block:: Python from simpeg import utils from simpeg.utils.io_utils.io_utils_electromagnetics import read_dcip_xyz from simpeg.electromagnetics.static.utils.static_utils import ( apparent_resistivity_from_voltage, convert_survey_3d_to_2d_lines, plot_pseudosection, ) import os import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt import tarfile mpl.rcParams.update({"font.size": 16}) try: import plotly from simpeg.electromagnetics.static.utils.static_utils import plot_3d_pseudosection has_plotly = True except ImportError: has_plotly = False pass # sphinx_gallery_thumbnail_number = 3 .. GENERATED FROM PYTHON SOURCE LINES 55-65 Download Assets --------------- Here we provide the file paths to assets we need to run the inversion. The path to the true model conductivity and chargeability models are also provided for comparison with the inversion results. These files are stored as a tar-file on our google cloud bucket: "https://storage.googleapis.com/simpeg/doc-assets/dcr3d.tar.gz" .. GENERATED FROM PYTHON SOURCE LINES 65-84 .. code-block:: Python # storage bucket where we have the data data_source = "https://storage.googleapis.com/simpeg/doc-assets/dcr3d.tar.gz" # download the data downloaded_data = utils.download(data_source, overwrite=True) # unzip the tarfile tar = tarfile.open(downloaded_data, "r") tar.extractall() tar.close() # path to the directory containing our data dir_path = downloaded_data.split(".")[0] + os.path.sep # files to work with topo_filename = dir_path + "topo_xyz.txt" data_filename = dir_path + "dc_data.xyz" .. rst-class:: sphx-glr-script-out .. code-block:: none Downloading https://storage.googleapis.com/simpeg/doc-assets/dcr3d.tar.gz saved to: /home/vsts/work/1/s/tutorials/05-dcr/dcr3d.tar.gz Download completed! .. GENERATED FROM PYTHON SOURCE LINES 85-92 Load the Data ------------- Here we load the file needed to run the tutorial. In this case, we load the surface topography and an XYZ formatted data file containing 3D DC resistivity data. .. GENERATED FROM PYTHON SOURCE LINES 92-112 .. code-block:: Python # Load 3D topography topo_xyz = np.loadtxt(str(topo_filename)) # Load 3D data. Here, the data are loaded from an XYZ formatted data file. # The user must supply the proper headers for the function to identify the # correct column. Using the 'dict_headers' keyword argument, we can load and # organize additional columns in the data file as a dictionary. data_3d, out_dict = read_dcip_xyz( data_filename, "volt", a_headers=["XA", "YA", "ZA"], b_headers=["XB", "YB", "ZB"], m_headers=["XM", "YM", "ZM"], n_headers=["XN", "YN", "ZN"], data_header="V/A", uncertainties_header="UNCERT", dict_headers=["LINEID"], ) .. GENERATED FROM PYTHON SOURCE LINES 113-120 Plot 3D Pseudosection --------------------- Here we demonstrate how 3D DC resistivity data can be represented on a 3D pseudosection plot. To use this utility, you must have Python's *plotly* package. Here, we represent the data as apparent conductivities. .. GENERATED FROM PYTHON SOURCE LINES 120-155 .. code-block:: Python # Extract 3D survey and observed data survey_3d = data_3d.survey dobs_3d = data_3d.dobs # Convert predicted data to apparent conductivities apparent_conductivity_3d = 1 / apparent_resistivity_from_voltage( survey_3d, dobs_3d, space_type="half space" ) if has_plotly: fig = plot_3d_pseudosection( survey_3d, apparent_conductivity_3d, scale="log", units="S/m", ) fig.update_layout( title_text="Apparent Conductivity", title_x=0.5, title_font_size=24, width=650, height=500, scene_camera=dict( center=dict(x=0, y=0, z=-0.4), eye=dict(x=1.6, y=-1.6, z=1.8) ), ) plotly.io.show(fig) else: print("INSTALL 'PLOTLY' TO VISUALIZE 3D PSEUDOSECTIONS") .. raw:: html :file: images/sphx_glr_plot_gen_3_3d_to_2d_001.html .. GENERATED FROM PYTHON SOURCE LINES 156-164 Convert From 3D to 2D --------------------- Here, we convert the 3D survey into a list of 2D surveys. A vector containing a line ID for each datum is required. By setting 'output_indexing' to True, we output a list containing the indices to extract the data for each 2D survey from vectors associated with the 3D survey. .. GENERATED FROM PYTHON SOURCE LINES 164-183 .. code-block:: Python # Extract line ID from dictionary lineID = out_dict["LINEID"] # Convert 3D survey to a list of 3D surveys survey_2d_list, index_list = convert_survey_3d_to_2d_lines( survey_3d, lineID, data_type="volt", output_indexing=True ) # Create list of 2D apparent conductivities. Note that if you converted observed # data then computed apparent conductivities, you would be doing so assuming 2D # survey geometry and the values would not match those on the 3D pseudosection plot. dobs_2d_list = [] apparent_conductivities_2d = [] for ind in index_list: dobs_2d_list.append(dobs_3d[ind]) apparent_conductivities_2d.append(apparent_conductivity_3d[ind]) .. GENERATED FROM PYTHON SOURCE LINES 184-187 Plot 2D Pseudosections ---------------------- .. GENERATED FROM PYTHON SOURCE LINES 187-213 .. code-block:: Python title_str = [ "East-West Line at Northing = 0 m", "North-South Line at Easting = -350 m", "North-South Line at Easting = -350 m", ] # Plot apparent conductivity pseudo-section for ii in range(len(survey_2d_list)): vlim = [apparent_conductivity_3d.min(), apparent_conductivity_3d.max()] fig = plt.figure(figsize=(12, 5)) ax1 = fig.add_axes([0.1, 0.15, 0.75, 0.78]) plot_pseudosection( survey_2d_list[ii], dobs=apparent_conductivities_2d[ii], plot_type="contourf", ax=ax1, vlim=vlim, scale="log", cbar_label="Apparent Conducitivty [S/m]", mask_topography=True, contourf_opts={"levels": 30, "cmap": mpl.cm.viridis}, ) ax1.set_title(title_str[ii]) plt.show() .. rst-class:: sphx-glr-horizontal * .. image-sg:: /content/tutorials/05-dcr/images/sphx_glr_plot_gen_3_3d_to_2d_002.png :alt: East-West Line at Northing = 0 m :srcset: /content/tutorials/05-dcr/images/sphx_glr_plot_gen_3_3d_to_2d_002.png :class: sphx-glr-multi-img * .. image-sg:: /content/tutorials/05-dcr/images/sphx_glr_plot_gen_3_3d_to_2d_003.png :alt: North-South Line at Easting = -350 m :srcset: /content/tutorials/05-dcr/images/sphx_glr_plot_gen_3_3d_to_2d_003.png :class: sphx-glr-multi-img * .. image-sg:: /content/tutorials/05-dcr/images/sphx_glr_plot_gen_3_3d_to_2d_004.png :alt: North-South Line at Easting = -350 m :srcset: /content/tutorials/05-dcr/images/sphx_glr_plot_gen_3_3d_to_2d_004.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out .. code-block:: none /home/vsts/work/1/s/tutorials/05-dcr/plot_gen_3_3d_to_2d.py:200: UserWarning: plot_pseudosection unused kwargs: ['vlim'] .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 4.718 seconds) **Estimated memory usage:** 9 MB .. _sphx_glr_download_content_tutorials_05-dcr_plot_gen_3_3d_to_2d.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_gen_3_3d_to_2d.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_gen_3_3d_to_2d.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_