2c_Note_regarding_land_CSEM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OUTDATED, the examples moved to the manual\n",
    "## See https://empymod.readthedocs.io/en/stable/examples\n",
    "\n",
    "----\n",
    "\n",
    "# Improve land CSEM calculation\n",
    "\n",
    "## The problem\n",
    "\n",
    "There exists a numerical singularity in the wavenumber-frequency domain. This singularity is always present, but it is usually neglectible except when the resistivity is very high (like air; hence conductivity goes to zero and therefore the real part of $\\eta_H$, $\\eta_V$ goes to zero) and source and receiver are close to this boundary. So unfortunately exactly in the case of a land CSEM survey.\n",
    "\n",
    "This singularity leads to noise at very high frequencies and therefore at very early times because the Hankel transform cannot handle the singularity correctly (or, if you would choose a sufficiently precise quadrature, it would take literally forever to calculate it).\n",
    "\n",
    "## The \"solution\"\n",
    "\n",
    "Electric permittivity ($\\varepsilon_H$, $\\varepsilon_V$) are set to 1 by default. They are not important for the frequency range of CSEM. By setting the electric permittivity of the air-layer to 0, the singularity disapears, which subsquently improves a lot the time-domain result for land CSEM. It therefore uses the diffusive approximation for the air layer, but again, that doesn't matter for the frequencies required for CSEM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import empymod as epm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "plt.rcParams['figure.dpi'] = 100\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Times (s)\n",
    "t = np.logspace(-2, 1, 301)\n",
    "\n",
    "# Model\n",
    "model = {\n",
    "    'src': [0, 0, 0.001],           # Source at origin, slightly below interface\n",
    "    'rec': [6000, 0, 0.0001],       # 6 km offset, in-line, slightly below interface\n",
    "    'depth': [0, 2000, 2100],       # Target of 100 m at 2 km depth\n",
    "    'res': [2e14, 10, 100, 10],     # Resistivity: [air, overburden, target, half-space]\n",
    "    'epermH': [1, 1, 1, 1],         # epermH: corresponds to the default values if not provided\n",
    "    'epermV': [1, 1, 1, 1],         # epermV: corresponds to the default values if not provided\n",
    "    'freqtime': t,                  # Times\n",
    "    'signal': 0,                    # 0: Impulse response\n",
    "    'ftarg': 'key_81_CosSin_2009',  # We choose a shorter filter then the default one\n",
    "    'verb': 1,                      # Verbosity; set to 3 to see all parameters in the calculation\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate with default eperm_air = 1\n",
    "res_1 = epm.dipole(**model)\n",
    "\n",
    "# Set horizontal and vertical electric permittivity of air to 0\n",
    "model['epermH'][0] = 0\n",
    "model['epermV'][0] = 0\n",
    "\n",
    "# Calculate with eperm_air = 0\n",
    "res_0 = epm.dipole(**model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot result\n",
    "\n",
    "As it can be seen, setting $\\varepsilon_{air} =$ 0 improves the land CSEM result significantly for earlier times, where the signal should be zero. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "\n",
    "plt.title('Time domain impulse response')\n",
    "plt.semilogx(t, res_1, label=r'$\\varepsilon_{air}=1$')\n",
    "plt.semilogx(t, res_0, label=r'$\\varepsilon_{air}=0$')\n",
    "plt.xlabel(r'Time $[s]$')\n",
    "plt.ylabel(r'Amplitude $[V/(m\\,s)]$')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Version 1.7.0 and older\n",
    "\n",
    "If you use a version of `empymod` that is smaller than `1.7.1` and set $\\varepsilon_H$, $\\varepsilon_V$ to zero you will see the following warning,\n",
    "\n",
    "    * WARNING :: Parameter epermH < 1e-20  are set to 1e-20 !\n",
    "    * WARNING :: Parameter epermV < 1e-20  are set to 1e-20 !\n",
    "\n",
    "and the values will be re-set to the defined minimum value, which is by default 1e-20. Using a value of 1e-20 for `epermH`/`epermV`  is also working just fine for land CSEM.\n",
    "\n",
    "It is possible to change the minimum to zero for these old versions of `empymod`. However, there is a caveat: In `empymod v1.7.0` and older, the `min_param`-parameter also checks frequency and anisotropy. If you set `min_param=0`, and provide `empymod` with resistivities or anisotropies equals to zero, `empymod` will crash due to division by zero errors (avoiding division by zero is the purpose behind the `min_param`-parameter).\n",
    "\n",
    "To change the `min_param`-parameter do:\n",
    "\n",
    "    import empymod\n",
    "    empymod.set_minimum(min_param=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style='border: 3px solid #ddd;'>\n",
       "  <tr>\n",
       "     <td style='text-align: center; font-weight: bold; font-size: 1.2em; border: 2px solid #fff;' colspan='8'>Wed Jul 04 16:05:42 2018 CDT</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>Linux</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>OS</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>4</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>CPU(s)</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>1.13.3</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>numpy</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>1.1.0</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>scipy</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>1.7.1</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>empymod</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>6.4.0</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>IPython</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>2.6.5</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>numexpr</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>2.2.2</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>matplotlib</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "     <td style='text-align: center; border: 2px solid #fff;' colspan='8'>3.6.5 |Anaconda custom (64-bit)| (default, Apr 29 2018, 16:14:56) \n",
       "[GCC 7.2.0]</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "     <td style='text-align: center; background-color: #ddd;border: 2px solid #fff;' colspan='8'>Intel(R) Math Kernel Library Version 2018.0.3 Product Build 20180406 for Intel(R) 64 architecture applications</td>\n",
       "  </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epm.versions('HTML')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}