.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "gallery/educational/dipoles_and_loops.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:here  to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_gallery_educational_dipoles_and_loops.py: Difference between magnetic dipole and loop sources =================================================== In this example we look at the differences between an electric loop loop, which results in a magnetic source, and a magnetic dipole source. The derivation of the electromagnetic field in Hunziker et al. (2015) is for electric and magnetic point-dipole sources and receivers. The magnetic field due to a magnetic source (:math:mm) is obtain from the electric field due to an electric source (:math:ee) using the duality principle, given in their Equation (11), .. math:: \hat{G}^{mm}_{pq}(\mathbf{x}, \mathbf{x'}, s, \eta_{kr}, \zeta_{ij}) = -\hat{G}^{ee}_{pq}(\mathbf{x}, \mathbf{x'}, s, -\zeta_{kr}, -\eta_{ij}) \, . \qquad (1) Without going into the details of the different parameters, we can focus on the difference between the :math:mm and :math:ee fields for a homogeneous, isotropic fullspace by simplifying this further to .. math:: \mathbf{G}^{mm}_\text{dip-dip} = \frac{\eta}{\zeta}\mathbf{G}^{ee} \quad \xrightarrow{\text{diff. approx}} \quad \frac{\sigma}{\mathrm{i}\omega \mu}\mathbf{G}^{ee}_\text{dip-dip} \, . \qquad (2) Here, :math:\sigma is conductivity (S/m), :math:\omega=2\pi f is angular frequency (Hz), and :math:\mu is the magnetic permeability (H/m). So from Equation (2) we see that the :math:mm field differs from the :math:ee field by a factor :math:\sigma/(\mathrm{i}\omega\mu). A magnetic dipole source has a moment of :math:I^mds; however, a magnetic dipole source is basically never used in geophysics. Instead a loop of an electric wire is used, which generates a magnetic field. The moment generated by this loop is given by :math:I^m = \mathrm{i}\omega\mu N A I^e, where :math:A is the area of the loop (m:math:^2), and :math:N the number of turns of the loop. So the difference between a unit magnetic dipole and a unit loop (:math:A=1, N=1) is the factor :math:\mathrm{i}\omega\mu, hence Equation (2) becomes .. math:: \mathbf{G}^{mm}_\text{loop-dip} = \mathrm{i}\omega\mu\mathbf{G}^{mm}_\text{dip-dip} = \sigma\,\mathbf{G}^{ee}_\text{dip-dip} \, . \qquad (3) This notebook shows this relation in the frequency domain, as well as for impulse, step-on, and step-off responses in the time domain. We can actually model an **electric loop** instead of adjusting the magnetic dipole solution to correspond to a loop source. This is shown in the second part of the notebook. **References** - 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(1), F1–F18; DOI: 10.1190/geo2013-0411.1 _. .. GENERATED FROM PYTHON SOURCE LINES 64-69 .. code-block:: default import empymod import numpy as np import matplotlib.pyplot as plt plt.style.use('ggplot') .. GENERATED FROM PYTHON SOURCE LINES 71-80 1. Using the magnetic dipole solution ------------------------------------- Survey parameters ~~~~~~~~~~~~~~~~~ - Homogenous fullspace of :math:\sigma = 0.01 S/m. - Source at the origin, x-directed. - Inline receiver with offset of 100 m, x-directed. .. GENERATED FROM PYTHON SOURCE LINES 80-87 .. code-block:: default freq = np.logspace(-1, 5, 301) # Frequencies (Hz) time = np.logspace(-6, 0, 301) # Times (s) src = [0, 0, 0, 0, 0] # x-dir. source at the origin [x, y, z, azimuth, dip] rec = [100, 0, 0, 0, 0] # x-dir. receiver 100m away from source, inline cond = 0.01 # Conductivity (S/m) .. GENERATED FROM PYTHON SOURCE LINES 88-90 Computation using empymod ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. GENERATED FROM PYTHON SOURCE LINES 90-118 .. code-block:: default # Collect common parameters inp = {'src': src, 'rec': rec, 'depth': [], 'res': 1/cond, 'verb': 1} # Frequency domain inp['freqtime'] = freq fee_dip_dip = empymod.bipole(**inp) fmm_dip_dip = empymod.bipole(msrc=True, mrec=True, **inp) f_loo_dip = empymod.loop(**inp) # Time domain inp['freqtime'] = time # ee ee_dip_dip_of = empymod.bipole(signal=-1, **inp) ee_dip_dip_im = empymod.bipole(signal=0, **inp) ee_dip_dip_on = empymod.bipole(signal=1, **inp) # mm dip-dip dip_dip_of = empymod.bipole(signal=-1, msrc=True, mrec=True, **inp) dip_dip_im = empymod.bipole(signal=0, msrc=True, mrec=True, **inp) dip_dip_on = empymod.bipole(signal=1, msrc=True, mrec=True, **inp) # mm loop-dip loo_dip_of = empymod.loop(signal=-1, **inp) loo_dip_im = empymod.loop(signal=0, **inp) loo_dip_on = empymod.loop(signal=1, **inp) .. GENERATED FROM PYTHON SOURCE LINES 119-121 Plot the result ~~~~~~~~~~~~~~~ .. GENERATED FROM PYTHON SOURCE LINES 121-206 .. code-block:: default fs = 16 # Fontsize # Figure fig = plt.figure(figsize=(12, 8)) # Frequency Domain plt.subplot(231) plt.title(r'$G^{ee}_{\rm{dip-dip}}$', fontsize=fs) plt.plot(freq, fee_dip_dip.real, 'C0-', label='Real') plt.plot(freq, -fee_dip_dip.real, 'C0--') plt.plot(freq, fee_dip_dip.imag, 'C1-', label='Imag') plt.plot(freq, -fee_dip_dip.imag, 'C1--') plt.xscale('log') plt.yscale('log') plt.ylim([5e-8, 2e-5]) ax1 = plt.subplot(232) plt.title(r'$G^{mm}_{\rm{dip-dip}}$', fontsize=fs) plt.plot(freq, fmm_dip_dip.real, 'C0-', label='Real') plt.plot(freq, -fmm_dip_dip.real, 'C0--') plt.plot(freq, fmm_dip_dip.imag, 'C1-', label='Imag') plt.plot(freq, -fmm_dip_dip.imag, 'C1--') plt.xscale('log') plt.yscale('log') plt.xlabel('Frequency (Hz)', fontsize=fs-2) plt.legend() plt.subplot(233) plt.title(r'$G^{mm}_{\rm{loop-dip}}$', fontsize=fs) plt.plot(freq, f_loo_dip.real, 'C0-', label='Real') plt.plot(freq, -f_loo_dip.real, 'C0--') plt.plot(freq, f_loo_dip.imag, 'C1-', label='Imag') plt.plot(freq, -f_loo_dip.imag, 'C1--') plt.xscale('log') plt.yscale('log') plt.ylim([5e-10, 2e-7]) plt.text(1.05, 0.5, "Frequency Domain", {'fontsize': fs}, horizontalalignment='left', verticalalignment='center', rotation=-90, clip_on=False, transform=plt.gca().transAxes) # Time Domain plt.subplot(234) plt.plot(time, ee_dip_dip_of, 'C0-', label='Step-Off') plt.plot(time, -ee_dip_dip_of, 'C0--') plt.plot(time, ee_dip_dip_im, 'C1-', label='Impulse') plt.plot(time, -ee_dip_dip_im, 'C1--') plt.plot(time, ee_dip_dip_on, 'C2-', label='Step-On') plt.plot(time, -ee_dip_dip_on, 'C2--') plt.xscale('log') plt.yscale('log') plt.subplot(235) plt.plot(time, dip_dip_of, 'C0-', label='Step-Off') plt.plot(time, -dip_dip_of, 'C0--') plt.plot(time, dip_dip_im, 'C1-', label='Impulse') plt.plot(time, -dip_dip_im, 'C1--') plt.plot(time, dip_dip_on, 'C2-', label='Step-On') plt.plot(time, -dip_dip_on, 'C2--') plt.xscale('log') plt.yscale('log') plt.xlabel('Time (s)', fontsize=fs-2) plt.legend() plt.subplot(236) plt.plot(time, loo_dip_of, 'C0-', label='Step-Off') plt.plot(time, -loo_dip_of, 'C0--') plt.plot(time, loo_dip_im, 'C1-', label='Impulse') plt.plot(time, -loo_dip_im, 'C1--') plt.plot(time, loo_dip_on, 'C2-', label='Step-On') plt.plot(time, -loo_dip_on, 'C2--') plt.xscale('log') plt.yscale('log') plt.text(1.05, 0.5, "Time Domain", {'fontsize': fs}, horizontalalignment='left', verticalalignment='center', rotation=-90, clip_on=False, transform=plt.gca().transAxes) fig.text(-0.01, 0.5, 'Amplitude; e-rec (V/m); m-rec (A/m)', va='center', rotation='vertical', fontsize=fs, color='.4') plt.tight_layout() plt.show() .. image-sg:: /gallery/educational/images/sphx_glr_dipoles_and_loops_001.png :alt: $G^{ee}_{\rm{dip-dip}}$, $G^{mm}_{\rm{dip-dip}}$, $G^{mm}_{\rm{loop-dip}}$ :srcset: /gallery/educational/images/sphx_glr_dipoles_and_loops_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 207-235 The figure shows the main points of Equations (2) and (3): - The magnetic dipole-dipole response differs by a factor :math:\sigma/(\mathrm{i}\omega\mu) from the electric dipole-dipole response. That means for the time-domain that the magnetic response looks more like the time derivative of the electric response (e.g., the magnetic impulse responses resembles the electric step-on response). - The magnetic loop-dipole response differs only by :math:\sigma from the electric dipole-dipole response, hence a factor of 0.01. The units of the response only depend on the receiver, what the receiver actually measures. So if we change the source from a dipole to a loop it does not change the units of the received responses. 2. Using an electric loop ------------------------- We can use empymod to model arbitrary shaped sources by simply adding point dipole sources together. This is what empymod does internally to model a finite length dipole (empymod.bipole), where it uses a Gaussian quadrature with a few points. Here, we are going to compare the result from loop, as presented above, with two different simulations of an electric loop source, assuming a square loop which sides are 1 m long, so the area correspond to one square meter. Plotting routines ~~~~~~~~~~~~~~~~~ .. GENERATED FROM PYTHON SOURCE LINES 235-306 .. code-block:: default def plot_result(data1, data2, x, title, vmin=-15., vmax=-7., rx=0): """Plot result.""" fig = plt.figure(figsize=(18, 10)) def setplot(name): """Plot settings""" plt.title(name) plt.xlim(rx.min(), rx.max()) plt.ylim(rx.min(), rx.max()) plt.axis("equal") # Plot Re(data) ax1 = plt.subplot(231) setplot(r"(a) |Re(magn.dip*iwu)|") cf0 = plt.pcolormesh(rx, rx, np.log10(np.abs(data1.real)), linewidth=0, rasterized=True, cmap="viridis", vmin=vmin, vmax=vmax, shading='nearest') ax2 = plt.subplot(232) setplot(r"(b) |Re(el. square)|") plt.pcolormesh(rx, rx, np.log10(np.abs(data2.real)), linewidth=0, rasterized=True, cmap="viridis", vmin=vmin, vmax=vmax, shading='nearest') ax3 = plt.subplot(233) setplot(r"(c) Error real part") error_r = np.abs((data1.real-data2.real)/data1.real)*100 cf2 = plt.pcolormesh(rx, rx, np.log10(error_r), vmin=-2, vmax=2, linewidth=0, rasterized=True, cmap=plt.cm.get_cmap("RdBu_r", 8), shading='nearest') # Plot Im(data) ax4 = plt.subplot(234) setplot(r"(d) |Im(magn.dip*iwu)|") plt.pcolormesh(rx, rx, np.log10(np.abs(data1.imag)), linewidth=0, rasterized=True, cmap="viridis", vmin=vmin, vmax=vmax, shading='nearest') ax5 = plt.subplot(235) setplot(r"(e) |Im(el. square)|") plt.pcolormesh(rx, rx, np.log10(np.abs(data2.imag)), linewidth=0, rasterized=True, cmap="viridis", vmin=vmin, vmax=vmax, shading='nearest') ax6 = plt.subplot(236) setplot(r"(f) Error imag part") error_i = np.abs((data1.imag-data2.imag)/data1.imag)*100 plt.pcolormesh(rx, rx, np.log10(error_i), vmin=-2, vmax=2, linewidth=0, rasterized=True, cmap=plt.cm.get_cmap("RdBu_r", 8), shading='nearest') # Colorbars fig.colorbar(cf0, ax=[ax1, ax2, ax3], label=r"$\log_{10}$ Amplitude (A/m)") cbar = fig.colorbar(cf2, ax=[ax4, ax5, ax6], label=r"Relative Error") cbar.set_ticks([-2, -1, 0, 1, 2]) cbar.ax.set_yticklabels([r"$0.01\,\%$", r"$0.1\,\%$", r"$1\,\%$", r"$10\,\%$", r"$100\,\%$"]) # Axis label fig.text(0.4, 0.05, "Inline Offset (m)", fontsize=14) fig.text(0.08, 0.5, 'Crossline Offset (m)', rotation=90, fontsize=14) # Title fig.suptitle(title, y=.95, fontsize=20) plt.show() .. GENERATED FROM PYTHON SOURCE LINES 307-319 Model parameters ~~~~~~~~~~~~~~~~ - Resistivity: :math:1 \Omega m fullspace Survey ~~~~~~ - Source at [0, 0, 0] - Receivers at [x, y, 10] - frequencies: 100 Hz. - Offsets: -250 m - 250 m .. GENERATED FROM PYTHON SOURCE LINES 319-336 .. code-block:: default # Survey parameters x = ((np.arange(502))-250.5) rx = np.repeat([x, ], np.size(x), axis=0) ry = rx.transpose() rxx = rx.ravel() ryy = ry.ravel() # Model model = { 'depth': [], # Fullspace 'res': 1., # 1 Ohm.m 'freqtime': 100, # 100 Hz 'htarg': {'pts_per_dec': -1}, 'verb': 1, } .. GENERATED FROM PYTHON SOURCE LINES 337-339 Compute empymod.loop result ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. GENERATED FROM PYTHON SOURCE LINES 339-343 .. code-block:: default epm_loop = empymod.loop(src=[0, 0, 0, 0, 90], rec=[rxx, ryy, 10, 0, 0], **model).reshape(np.shape(rx)) .. GENERATED FROM PYTHON SOURCE LINES 344-352 2.1 Point dipoles at (x, y) using empymod.dipole ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - (0.5, 0), ab=42 - (0, 0.5), ab=41 - (-0.5, 0), ab=-42 - (0, -0.5), ab=-41 .. GENERATED FROM PYTHON SOURCE LINES 352-367 .. code-block:: default rec_dip = [rxx, ryy, 10] square_pts = +empymod.dipole(src=[+0.5, +0.0, 0], rec=rec_dip, ab=42, **model).reshape(np.shape(rx)) square_pts += empymod.dipole(src=[+0.0, +0.5, 0], rec=rec_dip, ab=41, **model).reshape(np.shape(rx)) square_pts -= empymod.dipole(src=[-0.5, +0.0, 0], rec=rec_dip, ab=42, **model).reshape(np.shape(rx)) square_pts -= empymod.dipole(src=[+0.0, -0.5, 0], rec=rec_dip, ab=41, **model).reshape(np.shape(rx)) plot_result(epm_loop, square_pts, x, 'Loop made of four points', vmin=-13, vmax=-5, rx=x) .. image-sg:: /gallery/educational/images/sphx_glr_dipoles_and_loops_002.png :alt: Loop made of four points, (a) |Re(magn.dip*iwu)|, (b) |Re(el. square)|, (c) Error real part, (d) |Im(magn.dip*iwu)|, (e) |Im(el. square)|, (f) Error imag part :srcset: /gallery/educational/images/sphx_glr_dipoles_and_loops_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 368-377 2.2 Finite length dipoles using empymod.bipole ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Each simulated with a 5pt Gaussian quadrature. The dipoles are: - (-0.5, -0.5) to (+0.5, -0.5) - (+0.5, -0.5) to (+0.5, +0.5) - (+0.5, +0.5) to (-0.5, +0.5) - (-0.5, +0.5) to (-0.5, -0.5) .. GENERATED FROM PYTHON SOURCE LINES 377-397 .. code-block:: default inp_dip = { 'rec': [rxx, ryy, 10, 0, 0], 'mrec': True, 'srcpts': 5 # Gaussian quadr. with 5 pts to simulate a finite length dip. } square_dip = +empymod.bipole(src=[+0.5, +0.5, -0.5, +0.5, 0, 0], **inp_dip, **model) square_dip += empymod.bipole(src=[+0.5, -0.5, +0.5, +0.5, 0, 0], **inp_dip, **model) square_dip += empymod.bipole(src=[-0.5, -0.5, +0.5, -0.5, 0, 0], **inp_dip, **model) square_dip += empymod.bipole(src=[-0.5, +0.5, -0.5, -0.5, 0, 0], **inp_dip, **model) square_dip = square_dip.reshape(np.shape(rx)) plot_result(epm_loop, square_dip, x, 'Loop made of four dipoles', vmin=-13, vmax=-5, rx=x) .. image-sg:: /gallery/educational/images/sphx_glr_dipoles_and_loops_003.png :alt: Loop made of four dipoles, (a) |Re(magn.dip*iwu)|, (b) |Re(el. square)|, (c) Error real part, (d) |Im(magn.dip*iwu)|, (e) |Im(el. square)|, (f) Error imag part :srcset: /gallery/educational/images/sphx_glr_dipoles_and_loops_003.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 398-408 Close to the source the results between - (1) a magnetic dipole, - (2) an electric loop conisting of four point sources, and - (3) an electric loop consisting of four finite length dipoles, differ, as expected. However, for the vast majority they are identical. Skin depth for our example with :math:\rho=1\Omega m and :math:f=100 Hz is roughly 50 m, so the results are basically identical for 4-5 skin depths, after which the signal is very low. .. GENERATED FROM PYTHON SOURCE LINES 408-410 .. code-block:: default empymod.Report() .. raw:: html
 Sat Oct 15 19:19:22 2022 UTC OS Linux CPU(s) 2 Machine x86_64 Architecture 64bit RAM 7.6 GiB Environment Python File system ext4 Python 3.8.6 (default, Oct 19 2020, 15:10:29) [GCC 7.5.0] numpy 1.23.4 scipy 1.9.2 numba 0.56.3 empymod 2.2.1 IPython 8.5.0 matplotlib 3.6.1

.. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 7.865 seconds) **Estimated memory usage:** 159 MB .. _sphx_glr_download_gallery_educational_dipoles_and_loops.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:Download Python source code: dipoles_and_loops.py  .. container:: sphx-glr-download sphx-glr-download-jupyter :download:Download Jupyter notebook: dipoles_and_loops.ipynb  .. only:: html .. rst-class:: sphx-glr-signature Gallery generated by Sphinx-Gallery _