Getting started with controlled-source electromagnetic 1D modeling

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Forward modeling is an important part of understanding controlled-source electromagnetic (CSEM) responses. The diffusive term in the electromagnetic wave equation is dominant over the displacement term at these frequencies. It is the diffusive behavior that makes it difficult to imagine the actual propagation of the signal. An important tool in gaining experience therefore is forward modeling, and lots of it. The advantage of one-dimensional (1D) forward modeling, besides its speed, is to study isolated effects (see for instance Key, 2009)[1]: What is the influence of resistivity anisotropy, or of fine-scale resistivity variations? What is the influence of the airwave? With 1D modeling you can quickly study these effects in isolation before you go on to more complex models in higher dimensions. For an introduction to CSEM for hydrocarbon exploration see, for instance, Constable (2010).[2]

Luckily the EM community can count on some freely available, stable, high-quality, and open-source CSEM modeling codes. Well-known ones are the codes from the Marine EM Lab at Scripps: DIPOLE1D (Key, 2009) and MARE2DEM (Key and Ovall, 2011) for 1D and 2D modeling.[1][3] DIPOLE1D is an isotropic EM modeler for a stratified model using the diffusive approximation valid for low frequencies such as used in CSEM (neglecting displacement currents). MARE2DEM is a 2.5D CSEM and magnetotelluric (MT) modeling and inversion code, including resistivity anisotropy. More recently, Cockett et al. (2015) published SimPEG, and Hunziker et al. (2015) EMmod.[4][5] SimPEG is a framework for 3D simulation and modeling of all sorts of geophysical methods, among them CSEM. EMmod is a 1D electromagnetic modeler including anisotropy in the form of vertical transverse isotropic (VTI) resistivity as well as VTI electric permittivity and VTI magnetic permeability, therefore modeling the whole wavefield for electric and magnetic sources and receivers.

In this tutorial, I use the 1D electromagnetic forward modeling code empymod. The code is published under a permissive open-source license and available at github.com/prisae/empymod. It is based on Hunziker et al. (2015) for the wavenumber-domain calculation, and on Key (2012) for the Hankel and Fourier transforms.[5][6] The electromagnetic Python modeler empymod can model electric or magnetic responses due to a three-dimensional electric or magnetic source in a layered earth model (Figure 1) with n layers, each with electric vertical isotropy (, ), horizontal and vertical electric permittivity (, ), and horizontal and vertical magnetic permeability (, ), from very low frequencies (f ∼ 0 Hz) to very high frequencies (f ∼ GHz). The layer parameters all default to 1 if not provided ( is mandatory).

Figure 1. A layered earth model. Each horizontal layer is parameterized by horizontal resistivity and anisotropy, horizontal and vertical magnetic permeability, and horizontal and vertical electric permittivity.

A first example

We start with a simple model of a half-space below air:

  • air/half-space interface at 0 m;
  • electric, x-directed source at x = 0 m, y = 0 m, z = 1 m;
  • electric receiver at x = 1000 m, y = 0 m, z = 1 m, with azimuth = 45° and dip = 10°; and
  • frequency of 2 Hz.

The function bipole takes up to 24 arguments, but 19 of those have default values. We only have to provide the five mandatory parameters for this simple example: sources, receivers, depths of the interfaces, resistivities, and frequencies:

>>> from empymod import bipole 

>>> inp1 = {‘src’: [0, 0, 1, 0, 0], 

>>>         ‘rec’: [1000, 0, 1, 45, 10], 

>>>         ‘depth’: 0, 

>>>         ‘res’: [2e14, 100], 

>>>         ‘freqtime’: 2} # Frequency 

>>> bipole(**inp1) empymod END; runtime=0:00:00.003869; 3 kernel calls array((2.20420888976397e-08-7.1538671654131e-10j))

From this example, you can infer that an infinitesimally small dipole is defined as [x, y, z, azimuth, dip], using a left-handed coordinate system with positive z pointing downward. The azimuth is the anti-clockwise deviation from the x-axis, and the dip is the deviation downward from the xy-plane. A finite length bipole, on the other hand, would be defined as [], where [] is the first pole location, and [] is the second pole location. We will see in the next example why the frequency parameter is called freqtime.

The default verbosity reveals that empymod took some milliseconds to run on my machine, and three kernel calls were required. This is because our receiver has an arbitrary rotation, so it had to calculate the x-, y-, and z-fields due to an x-directed source. And it returns a complex number, the result.

Frequency and time domain

Let's look at a four-layer isotropic model (air, overburden, target, underburden), with and without hydrocarbons. The resistivities in the two models are:

>>> bri = [2e14, 20, 20, 20] 

>>> oil = [2e14, 20, 500, 20]

We will keep it simple in terms of source and receiver, with an x-directed source and an x-directed inline receiver at the same offset as before. Note that I shift the source and receiver to z = 0.01 m, because empymod places electrodes on a layer interface in the upper layer.

>>> inp2 = {‘src’: [0, 0, 0.01, 0, 0], 

>>>        ‘rec’: [1000, 0, 0.01, 0, 0], 

>>>        ‘depth’: [0, 500, 525]} 


>>> f = np.logspace(−1, 3, 100) 

>>> fbres = bipole(res=bri, freqtime=f, **inp2) 

>>> fores = bipole(res=oil, freqtime=f, **inp2)

For the time domain, we have to provide a signal in order to calculate time-domain results instead of frequency-domain results: 0 denotes an impulse response; 1 denotes switch-on; −1 denotes switch-off.

>>> t = np.linspace(0, 0.06, 100) 

>>> tbres = bipole(res=bri, freqtime=t, signal=−1, **inp2) 

>>> tores = bipole(res=oil, freqtime=t, signal=−1, **inp2)

Figure 2 shows the comparison of frequency-domain and time-domain responses for the same model. The jump in the time-domain response is the so-called airwave. Now you see why the frequency-parameter is called freqtime: this parameter takes frequencies if signal=None (default) or times if signal is −1, 0, or 1.

Figure 2. A simple land CSEM example. (a) Frequency- and (b) time-domain responses for the four-layer model, with brine (blue) and oil (red) in the third layer. The offset distance is 1 km.

Anisotropy

Anisotropic CSEM modeling, more specifically vertical transverse isotropic (VTI) modeling, is the normal case these days (unlike 10 years ago). Resistivity anisotropy and mean resistivity are commonly defined in terms of horizontal () and vertical () resistivity as:

and

The three resistivities and are therefore related through :

A common mistake is to only speak about increasing anisotropy, without further specification. As can be seen from the above relationship, increasing anisotropy is ambiguous. The relationship between the three resistivities is elaborated in detail in Werthmüller (2009).[7] An example of this ambiguity is given in Figure 3; the code to produce it is in the accompanying notebook.

Figure 3. The effect of increasing resistivity anisotropy λ on the time-domain response of the model. The offset distance is 2 km.

These results show that stating “increasing anisotropy” can mean quite different things, and it is crucial to specify it more, for instance “increasing anisotropy by keeping the mean resistivity constant.” The isotropic case, the black line, is the same in all three plots. We can draw the following conclusions:

  1. The initial step, the airwave, depends only on .
  2. The DC value, hence time , depends only on .

These insights can be used to calculate apparent anisotropy values of the subsurface from early and late time CSEM responses.

Interactive modeling

Jupyter and the ipywidgets make it incredibly easy to create interactive plots, which I use in the notebook to create an interactive model for empymod. All there is to do is to load the widgets, define the desired sliders, create a plot function as we have done for the examples before, and subsequently call interact. In the example, I include three sliders: one for the resistivity of the target layer, one for the depth of the target layer, and one for the thickness of the target layer. Five additional lines of code are all that is required to generate this interactive modeler: one line to import the required functions, three lines to define the three sliders, and a last line to activate the interaction. The result is shown in Figure 4.

Figure 4. (a) A simple earth model and (b) the real and (c) imaginary responses for an x-directed, electric source at 250 m depth and x-directed, electric receivers at the sea bottom. Water depth is 300 m, and the background resistivity is 1 Ωm.

The interactive example should give you a good idea how to write your own interactive modeler. With a few changes, you could instead have a slider for frequencies, anisotropies, source/receiver azimuth and dip, or any other parameter that goes into bipole.

More examples

The notebook contains, besides much more details, the following four additional examples:

  • comparison of bipole versus dipole;
  • calculate the amplitude and phase of the entire x, y-plane;
  • calculate offset versus frequency crossplot, a common tool for feasibility studies; and
  • common-source gather of time-domain responses.

The eight complete examples in the notebook should get you up and running with CSEM modeling. I am open to suggestions and pull requests, thankful for bug reports, and happy if I see that you fork it and create something awesome of your own. Happy EM modeling!

References

  1. 1.0 1.1 Key, K., 2009, 1D inversion of multicomponent, multifrequency marine CSEM data: Methodology and synthetic studies for resolving thin resistive layers: Geophysics, 74, no. 2, F9–F20, http://dx.doi.org/10.1190/1.3058434. Code at http://marineemlab.ucsd.edu/Projects/Occam/1DCSEM.
  2. Constable, S., 2010, Ten years of marine CSEM for hydrocarbon exploration: Geophysics, 75, no. 5, 75A67–75A81, http://dx.doi.org/10.1190/1.3483451.
  3. Key, K., and J. Ovall, 2011, A parallel goal-oriented adaptive finite element method for 2.5-D electromagnetic modelling: Geophysical Journal International, 186, no. 1, 137–154, http://dx.doi.org/10.1111/j.1365-246X.2011.05025.x. Code at http://mare2dem.ucsd.edu.
  4. Cockett, R., S. Kang, L. J. Heagy, A. Pidlisecky, and D. W. Oldenburg, 2015, SimPEG: An open source framework for simulation and gradient based parameter estimation in geophysical applications: Computers & Geosciences, 85, 142–154, http://dx.doi.org/10.1016/j.cageo.2015.09.015. Code at http://simpeg.xyz.
  5. 5.0 5.1 Hunziker, J., J. Thorbecke, and E. Slob, 2015, The electromagnetic response in a layered vertical transverse isotropic medium: A new look at an old problem: Geophysics, 80, no. 1, F1–F18, http://dx.doi.org/10.1190/geo2013-0411.1. Code at http://software.seg.org/2015/0001.
  6. Key, K., 2012, Is the fast Hankel transform faster than quadrature?: Geophysics, 77, no. 3, F21–F30, http://dx.doi.org/10.1190/GEO2011-0237.1. Code at http://software.seg.org/2012/0003.
  7. Werthmüller, D., 2009, Inversion of multi-transient EM data from anisotropic media: M.Sc. thesis, TU Delft, UUID: http://repository.tudelft.nl/view/ir/uuid:f4b071c1-8e55-4ec5-86c6-a2d54c3eda5a.

Acknowledgements

I would like to thank Matt Hall, Jürg Hunziker, Rowan Cockett, and Lindsey Heagy for comments that greatly improved this tutorial.

Corresponding author

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Getting started with controlled-source electromagnetic 1D modeling
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