Workflow Overview

This section gives a brief overview of the typical workflow for MARE2DEM. The suite of MARE2DEM codes consists of three parts:

  1. A MATLAB user interface and other routines for building forward and inverse model and preparing data files

  2. The core MARE2DEM code (written in MPI-Fortran and C) for the finite element modeling and inversion of EM responses

  3. MATLAB routines for interactive plotting of inversion models and viewing EM responses and data fits

The typical workflow is outline in the image below:

_images/workflow.png

  1. Input data consists of the transmitters and receivers and response frequencies and any topography and other geologic surfaces that will be incorporated into the model.

  2. In MATLAB, the data parameters (and any EM responses to be inverted) can be written to a MARE2DEM format data file using m2d_writeEMData2DFile.m. A resistivity model (and possibly an inversion mesh) is built using the interactive Mamba2D.m graphical user interface.

  3. The MARE2DEM executable code is run for forward or inverse modeling.

  4. Results are viewed in MATLAB using plotMARE2DEM.m.

  5. For inversion, the resulting model and data fits are inspected and if deemed insufficient, the model parameterization is revised and outlier or noisy data are trimmed and the workflow is repeated.

Input Files

There are four input files required to run MARE2DEM for forward modeling; a fifth file is needed for inversion. They are all created by Mamba2D.m except for the data file. For a model named demo, the following files are needed:

  • demo.0.resistivity specifies the resistivity of each model parameter region and whether or not the parameter is fixed or an inversion free parameter, the resistivity anisotropy setting, inversion parameter resistivity bounds and prejudices (if any), and lists the names of the other required files. The file extension 0 specifies the inversion iteration number; 0 means this is is a starting model. When running inversions, a new resistivity file is output for each iteration of the inversion and this number will indicate the inversion iteration.

  • demo.poly specifies the geometry of the model parameter grid (nodes and segments connecting them)

  • demo.penalty contains the inversion’s roughness penalty matrix (only needed for inversion).

  • demo.emdata specifies the sources, receivers and frequencies to be modeled as well the type of data and any real data to be inverted.

  • mare2dem.settings specifies various runtime settings for MARE2DEM, including the accuracy tolerance for EM responses generated by the automatic adaptive mesh refinement, and how the data should be decomposed into subsets for parallel computations.

See the section File Formats section for further details.

Creating Input Models

_images/demo_fwd.png

Fig. 1 Example forward modeling mesh for the demo example created with the interactive Mamba2D.m graphical user interface. The model consists of nodes that are connected by line segments. These nodes and segments define the model geometry and are stored in the .poly file. Each segment bound polygonal region is assigned a unique resistivity value. You can draw model regions interactively by pointing and clicking, or by importing surface profiles (position,depth) and connecting them. Hitting the Write MARE2DEM Files button then creates the .resistivity, .poly and .settings files. Then entire model extends 100 km in each direction and here only the central region of interest is shown.

Note

No meshing or gridding is needed for forward modeling. Just draw the desired model structure and assign resistivity values to each segment bound region, save the files and let MARE2DEM do the rest. When you run MARE2DEM, it will load in the model that you created with Mamba2D.m and then MARE2DEM automatically generates adaptive finite element meshes that conform to the model geometry and that are iteratively refined to give accurate EM responses for the particular transmitters and receivers being modeled. All finite element meshing and refinement in MARE2DEM is done internally behind the scenes while it runs and these dynamically generated meshes are not saved to files.

_images/demo_inv.png

Fig. 2 Example inversion modeling grid for the demo example. For inversion, the model starts with three layers (air,sea, seafloor) and the seafloor region is assigned to be a free parameter. A region of interest box is created beneath the seafloor and is filed with a triangular mesh of model parameters with a target size. The outer padding region is also meshed with arbitrarily larger triangles. The inversion will then solve for the resistivity of these triangular parameters. Note that the any polygonal region can be a free parameter for inversion; here we used triangles since Mamba2D has a versatile unstructured triangular meshing engine that can mesh complicated regions, but we could have also used quadrilateral or other polygon shapes for the parameter grid. Only the central region of interest is shown in this image.

Running MARE2DEM

After the input files have been created, MARE2DEM can be run locally on your laptop or desktop if the modeling problem is small (a MT data set or a small CSEM problem), or the files can be transferred to a HPC cluster where MARE2DEM is run remotely (e.g., for large CSEM data sets).

_images/demo_start.png

Fig. 3 Example startup of MARE2DEM for the demo example on a MacBook Pro with 8 processing cores. Here I opened a terminal, cd’d to the demo folder and listed in contents using ls. You can see the five required input files are there. Then I launch MARE2DEM using mpirun command with the argument -n 8, which tells MPI to use 8 processors. The MARE2DEM executable is followed by the name of the resistivity file to start the inversion with, here Demo.0.resistivity.

_images/demo_running.png

Fig. 4 Example showing some runtime output of MARE2DEM for the demo example. The text on the lower half shows the adaptive mesh refinement details for various data subsets as the parallel calculations proceed. For inversion runs of MARE2DEM, the convergence history of each iteration is saved to a separate .logfile file so you can easily monitor its progress.

Evaluating the Results

_images/demo_joint_model.png

Fig. 5 Example showing the inversion result for the demo example of that has synthetic MT and CSEM data generated from the forward model shown above. The inversion model was plotted in MATLAB using MARE2DEM’s code plotMARE2DEM.m, which allows for interactive exploration of the model results.

_images/demo_csem_fit.png

Fig. 6 Example showing the inversion fit to the CSEM data for the demo example plotted in MATLAB using MARE2DEM’s code plotMARE2DEM_CSEM.m.

_images/demo_mt_fit.png

Fig. 7 Example showing the inversion fit to the MT data for the demo example plotted in MATLAB using MARE2DEM’s code plotMARE2DEM_MT.m.

_images/demo_mt_pseudosection.png

Fig. 8 Example pseudosection plot showing the inversion fit to the MT data for the demo example, made with plotMARE2DEM_MT.m. You can see a nice fit to the data, and in this ideal synthetic example, the normalized residuals have a nice random distribution.

_images/demo_mt_misfit_breakdown.png

Fig. 9 Example MT misfit-breakdown plot for the inversion fit to the MT data for the demo example, made using plotMARE2DEM_MT.m. In this ideal synthetic example, you can see that no matter how we slice-and-dice the MT data, the various subsets have RMS misfits close to the target 1.0 value, suggesting the model is a good overall fit to the MT stations.