notebooks/frequency-domain-em/inv_fdem_1d.ipynb

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   "source": [
    "---\n",
    "title: \"1D Inversion of Frequency Domain EM Data for a Single Sounding\"\n",
    "authors:\n",
    "  - id: devincowan\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} Intermediate notebook\n",
    ":class: caution\n",
    "This tutorial focusses on intermediate level functionality within SimPEG. Basic functionality within SimPEG is not discussed in detail, as we assume the user is already familiar. \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} Light-weight notebook\n",
    ":class: hint\n",
    "This tutorial requires minimal computational resources and can be executed quickly in the background while other computer processes are running.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Keywords:** frequency-domain EM, 1D sounding, inversion, parametric, sparse norm, wires mapping\n",
    "\n",
    "</br>\n",
    "\n",
    "**Summary:** Here, we invert secondary magnetic field data for a single 1D FDEM sounding. We demonstrate 3 approaches for inverting the data:\n",
    "\n",
    "1. A weighted least-squares inversion where the number of layers and their thicknesses are fixed\n",
    "2. An iteratively re-weighted least-squares (IRLS) inversion to recover sparse and/or blocky structures\n",
    "3. A parametric inversion where we invert for the layer thicknesses and electrical properties assuming a 3-layered Earth\n",
    "\n",
    "The *weighted least-squares* approach is a great introduction to geophysical inversion with SimPEG. One drawback however, is that it recovers smooth structures which may not be representative of the true model. To recover sparse and/or blocky 1D structures, we demonstrate an *iteratively re-weighted least-squares* approach. If the number of layers is known, but their depths, thicknesses and conductivities are not, we can use a *parametric inversion* approach.\n",
    "\n",
    "**Learning Objectives:** Because this tutorial focusses primarily on inversion-related functionality, we urge the reader to become familiar with functionality explained in the [1D Forward Simulation of Frequency-Domain EM Data for a Single Sounding](fwd_fdem_1d.ipynb) tutorial before working through this one. For this tutorial, we focus on:\n",
    "\n",
    "- How to carry out 1D geophysical inversion with SimPEG.\n",
    "- How to assign appropriate uncertainties to FDEM data.\n",
    "- Choosing suitable parameters for the inversion.\n",
    "- Specifying directives that are applied throughout the inversion.\n",
    "- Weighted least-squares, sparse-norm and parametric inversion.\n",
    "- Analyzing inversion outputs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Modules\n",
    "\n",
    "Here, we import all of the functionality required to run the notebook for the tutorial exercise.\n",
    "All of the functionality specific to the forward simulation of 1D frequency domain EM data are imported from the [simpeg.electromagnetics.frequency_domain](xref:simpeg#simpeg.electromagnetics.frequency_domain) module. Classes required to define the data misfit, regularization, optimization, etc... are imported from elsewhere within SimPEG. We also import some useful utility functions from [simpeg.utils](xref:simpeg#simpeg.utils). To generate the mesh used for the inversion, we use the [discretize](https://discretize.simpeg.xyz/en/main) package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:43.325159Z",
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   "outputs": [],
   "source": [
    "# SimPEG functionality\n",
    "import simpeg.electromagnetics.frequency_domain as fdem\n",
    "from simpeg.utils import plot_1d_layer_model, download, mkvc\n",
    "from simpeg import (\n",
    "    maps,\n",
    "    data,\n",
    "    data_misfit,\n",
    "    regularization,\n",
    "    optimization,\n",
    "    inverse_problem,\n",
    "    inversion,\n",
    "    directives,\n",
    ")\n",
    "\n",
    "# discretize functionality\n",
    "from discretize import TensorMesh\n",
    "\n",
    "# Basic Python functionality\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import tarfile\n",
    "\n",
    "mpl.rcParams.update({\"font.size\": 14})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download and Extract the Tutorial Data\n",
    "\n",
    "For this tutorial, the frequencies and observed data for 1D sounding are stored within a tar-file. Here, we download and extract the data file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:45.023098Z",
     "iopub.status.busy": "2024-11-20T02:13:45.022676Z",
     "iopub.status.idle": "2024-11-20T02:13:45.587546Z",
     "shell.execute_reply": "2024-11-20T02:13:45.586367Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "overwriting /home/ssoler/git/user-tutorials/notebooks/frequency-domain-em/inv_fdem_1d_files.tar.gz\n",
      "Downloading https://github.com/simpeg/user-tutorials/raw/main/assets/frequency-domain-em/inv_fdem_1d_files.tar.gz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   saved to: /home/ssoler/git/user-tutorials/notebooks/frequency-domain-em/inv_fdem_1d_files.tar.gz\n",
      "Download completed!\n"
     ]
    }
   ],
   "source": [
    "# URL to assets\n",
    "data_source = \"https://github.com/simpeg/user-tutorials/raw/main/assets/frequency-domain-em/inv_fdem_1d_files.tar.gz\"\n",
    "\n",
    "# download the data\n",
    "downloaded_data = download(data_source, overwrite=True)\n",
    "\n",
    "# unzip the tarfile\n",
    "tar = tarfile.open(downloaded_data, \"r\")\n",
    "tar.extractall()\n",
    "tar.close()\n",
    "\n",
    "# path to the directory containing our data\n",
    "dir_path = downloaded_data.split(\".\")[0] + os.path.sep\n",
    "\n",
    "# files to work with\n",
    "data_filename = dir_path + \"em1dfm_data.txt\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and Plot the Data\n",
    "\n",
    "Here we load and plot the 1D sounding data. In this case, we have the\n",
    "secondary field response in ppm for a set of frequencies. The columns of the data file are: frequency (Hz), real component (ppm) and imaginary component (ppm)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:45.591806Z",
     "iopub.status.busy": "2024-11-20T02:13:45.591476Z",
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     "shell.execute_reply": "2024-11-20T02:13:45.595791Z"
    }
   },
   "outputs": [],
   "source": [
    "# Load data\n",
    "dobs = np.loadtxt(str(data_filename), skiprows=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:45.600817Z",
     "iopub.status.busy": "2024-11-20T02:13:45.600445Z",
     "iopub.status.idle": "2024-11-20T02:13:45.605759Z",
     "shell.execute_reply": "2024-11-20T02:13:45.604715Z"
    }
   },
   "outputs": [],
   "source": [
    "# Extract frequency and observed data columns\n",
    "frequencies = dobs[:, 0]\n",
    "dobs = mkvc(dobs[:, 1:].T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:45.609176Z",
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     "shell.execute_reply": "2024-11-20T02:13:46.153964Z"
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   "outputs": [
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UrFmTXfikJJRwZfTmzRuRic8JIYRotuTkZNjZ2Uksp4QrI+HSZ8nJyTA0NMS5c+fg4eFRoblg+Xx+udspzz7S1K1oHUllspwbFxQdp7zaV4W+Ik096ivcH4P6iuJlZWXB3t6+zPWcKeHKSHgb2czMDIaGhjAyMoKZmVmFf4mWt53y7CNN3YrWkVQmy7lxQdFxyqt9Vegr0tSjvsL9MaivKE9Zjxdp0BQhhBCiBJRwCSGEECWghEsIIYQoASVcQgghRAlo0FQF8fl86OjosF9XtK3ytlOefaSpW9E6kspkOTcuKDpOebWvCn1FmnrUV7g/BvUVxZP2+DS1YzkFBQUhKCgIAoEA8fHx2LdvH4yMjLgOixBCCEdyc3MREBCAzMxMmJmZSaxHCVdGWVlZMDc3R0ZGBgwNDREZGQl3d/dSh6YzDAOBQACBQICSPvaioiJcuXIFnTp1Yq+ay1KefaSpW9E6kspkOTcuKDpOebVf0b5iYGAAbW1tia8x8Pl8qfp0WfVKK5dUJu2xuaaMOOVxDFnaKM8+1Fe+5gMrK6syE67q/uarJHR1ddl/6G+//hbDMPj8+TPS09MhEAgktsUwDGrUqIG0tDSpp4sszz7S1K1oHUllspwbFxQdp7zal0df0dbWRvXq1WFubi6xDUl9urz1SiuXVCbtsbmmjDjlcQxZ2ijPPprcV6Q9NiVcJXj79i0+f/4MMzMzmJmZQUdHp8RfcMXFxcjJyYGJiUmp83HKuo80dStaR1KZLOfGBUXHKa/2K9JXjI2NUVxcjKysLKSlpSEvLw82NjYyx0IIkQ4lXAUTCATIzMxEtWrVYGVlVWrd4uJiFBYWwsDAoFy/RKXdR5q6Fa0jqUyWc+OCouOUV/sV6SuGhobQ0tKCqakp9PX1kZGRgerVq0NbW1vmeAipbAQCAeLi4pCWlgYbGxs4Ozsr/GdAdX/zqQk+nw+GYWhRbaKSjI2NwTAM56M8CVGmsLAwODg4oFu3bggICEC3bt3g4OCAsLAwhR6XEq6SqPJzS6K5qF8STRMWFgY/Pz+kpKSIbE9NTYWfn59Cky4lXEIIIRpBIBBgypQpJb4lItw2derUUge3VgQlXEIIIRohLi5O7Mr2WwzDIDk5GXFxcQo5PiVcQgghGiE5OVmqemlpaQo5PiVconECAwPB4/GQlJTEdSiEECXg8/nYvn07fvvtN6nqK+o1OUq4ROGSkpKgra0NS0tLdnYjXV1d2NraYtCgQfjf//7HdYiEEDVUVFSE4OBgNGjQAGPGjMG7d+9Krc/j8WBvbw9nZ2eFxEPv4RKlqVOnDoYNGwYej4cvX77g1q1bOHToEI4dO4bz58+ja9euXIdICFEDRUVF2Lt3L/744w+8ePFCpKxFixa4e/cueDyeyOAp4Yj9tWvXKux9XLrCVRMCgQAxMTEIDQ1FTEyMwkbZVYSjoyMWLlyIRYsWYcWKFYiKisLy5cvB5/Mxf/58rsMjhFRyAoEAu3fvRqNGjRAYGCiSbN3d3XHlyhXcuXMHR44cga2trci+dnZ2OHz4MHx9fRUWHyVcNRAeHg5HR0elv8QtD6NHjwYA3Lp1S2R7YWEhVq9ejVatWsHY2BimpqZwdnbGiRMnxNqIj4/HzJkz0apVK1StWhUGBgaoV68eZs+ejZycHKWcByGEOwKBAPv27UOjRo0wfPhwJCQksGVubm64dOkSzp07h44dOwIAfH19kZSUhOjoaOzbtw/R0dFITExUaLIF6JZypRcWFoYRI0aIvVcmfIlb0X+xycu3K94UFBTAy8sLMTExaNmyJUaPHg0+n49Tp07Bx8cHGzZswMSJE9n6YWFh2L59O7p16wZXV1cUFxfj2rVr+PvvvxEbG4uLFy9WiknwCSHlU1xcjLi4OMyePRtPnz4VKevWrRsWLVok8VGVtrY2XF1dlRDl/6GEW4kJBAL8+uuvEl/i5vF4mDp1Knx8fFR2ntwtW7YAALp06cJuW7JkCWJiYrBo0SIsWLCAfbaSnZ2N7t27Y/r06fD19UXNmjUBAMOGDcO0adOgp6cn0vaSJUuwcOFCHDx4EEOGDFHSGRFCFK24uBiHDx/GokWL8OTJE5Gyrl27YvHixUpPptKghMuxNm3a4O3bt+z3wkQpjYKCAmRkZEgsF77EXaNGDejr60vd/vd1atSoIZeRxC9fvsTixYvZQVM3b95EbGwsqlevjhUrVgD4+oO0efNm1K1bVyTZAoCpqSkWLFiAvn37IiwsjL3K/f5ZjNDEiROxcOFCnD9/nhIuIWqguLgYR44cwaJFi/Dw4UORsi5dumDx4sXo1q2byk5ZSgmXY2/fvkVqaqpCj1FaUlamxMRELFmyRGRb9erVERcXh3r16gEAnj17hk+fPqFmzZpYvHixWBvp6ekAIHL7iGEYBAcHIyQkBA8fPkRmZiaKi4vZ8jdv3ijidAghSsIwDK5du4b58+fjwYMHImUNGjTAmjVr4OnpqbKJVogSLsdq1Kgh8r08r3CFrKysKnyFKw9ubm44d+4ctLS0kJ6ejp07d2LWrFno168fbty4ARMTE3z8+BEA8OjRIzx69EhiW1++fGG/njx5MjZu3Ah7e3v07dsXNjY27PkuXrwYBQUFcomfEKJcDMPgxIkTWLhwIe7duydS1r59e8yfPx98Ph9ubm4qn2wBSric+/ZWrXBRcDMzM6nWOBUIBHBwcEBqamqJz3F5PB7s7OyQmJgIbW1tqdovbwyyqlatGmbMmIHMzEwsXboUv//+O9auXQszMzMAwIABA3D48OEy23n//j2CgoLQrFkzXL16FUZGRmzZ27dvS7xKJoSoNoZhcPLkSSxatAi3b98WKWvbti0WL14MLy8vFBUV4fTp0xxFWX70WlAlpq2tjTVr1gAQX2ZNGS9xy8PcuXNRs2ZNbNq0CUlJSWjYsCHMzMzwv//9T6o1Wl++fAmGYdCjRw+RZAtAYROQE0IUg2EYREREoF27dujbt69Isv3hhx9w7NgxXL9+HT179qwUV7Tfo4Rbyfn6+mLnzp2cvMQtD4aGhpg1axb4fD7++OMP6Ojo4Oeff8arV68wY8aMEpPuw4cP8f79ewBA7dq1AQBXrlwReW6bkpKC2bNnK+ckCCEVwjAMzp49i1mzZsHHx0fkzl/Lli0RFhaGlStXwtvbu1ImWiFKuGqgT58+ePnypdJf4paXcePGoWbNmti1axdevHiBxYsXw93dHevXr0fDhg0xevRozJ49G8OGDUOLFi3QtGlTvHz5EsDXScYHDBiAa9euoU2bNvjtt98wfPhwNGvWDC1btuT4zAghpWEYBufOnUOnTp3Qp08fxMfHs2XNmzfH0aNHcevWLfTu3btSJ1oheoZbQXw+n520oaSrMT6fD4ZhUFxcLHIFVhLhc1hhfWkI99HS0hJ7wfv7NqRpv6J1Sir7tk5J++jp6WH27NmYPHkyFi1ahJ07d+LUqVPYvn079uzZg8OHD6OgoADW1tZo2LAhxo0bh8aNG7Pt7NixA7Vr10ZYWBg2bNiAWrVq4ddff8XMmTNx5MiREmMQbpPlM6oIebVfkb7y/b8NwzDg8/kijx6Efbms2/pl1SutXFKZtMfmmjLilMcxZGmjPPvI0lcYhkF0dDSWLFmCK1euiNRr3LgxFixYAB8fH2hpaaGoqEjl+4q0x+cxJY22IRIFBQUhKCgIAoEA8fHx2Ldvn9izw2/p6OigRo0asLe3F5uYgRCuFRYWIjk5GW/fvkVRURHX4RAN8ODBA4SGhuLx48ci22vVqgV/f3906NBBoQM2FSE3NxcBAQHIzMxkB36WhBKujLKysmBubo6MjAwYGhoiMjIS7u7uYlMI5ufnIzk5GQ4ODjAwMCi1TYZhkJ2dDVNTU6lvn5RnH2nqVrSOpDJZzo0Lio5TXu3Lq6/k5+cjKSkJ9vb2Iv2Tz+dL7NPfKqteaeWSyqQ9NteUEac8jiFLG+XZR9q6UVFRmD59utjrfg0aNMDvv/8OHx8fXLhwoVL2laysLFhZWZWZcOmWcgXp6uqy/9Dffi0kEAjA4/GgpaVV5l9twtt8wvrSKM8+0tStaB1JZbKcGxcUHae82pdXX9HS0mLXJy7pF5ak7eWtV1p5RY/NNWXEKY9jyNJGefaRVPfSpUtYuHAhoqKiRLbXr18fCxcuxKBBg6Ctrc3elq2MfUXaY1PCJYQQIndXrlxhp1b9lpOTExYuXAh/f3+VfmVRESjhEkIIkZtr165h4cKFOHfunMj2unXrolevXli+fDkMDQ05io5bqntvjxBCSKVx8+ZN9OzZEx07dhRJto6OjggODsb9+/fRrVs3kaU4NY3mnjkhhJAKu337NpYuXSq2opiDgwPmz5+PYcOGQVdXl/NXd1QBXeESQggpt9u3b6Nv377o0KGDSLKtVasWtm7divj4eIwaNapSDHxTFrrCJYQQIrW7d+9i0aJFOH78uMh2e3t7zJs3DyNHjqQ5BySghEsIIaRM9+/fx+LFixEWFiay3c7ODr169cLKlSthYmLCUXSVA91SJoQQItHDhw8xcOBANG/eXCTZ1qxZExs3bsSTJ0/Qs2dPdg1qIhld4RJCCBGTnJyMgIAAHDlyRGS97Ro1amDu3LkYO3YsDAwMaDBUOVDCJYQQwnr69CkWLVqEgwcPiiRaa2trzJ49Gz/99JPGvkdbUZRwCSGEID4+HkuWLEFoaKjIClTVq1fHrFmzMH78+FIXaiFlo2e4hJQhJCQEPB4PISEhXIdCiNwlJCRg+PDhaNiwIfbu3csmW3Nzc/z11194+fIlpk2bRslWDugKlyhcUlIS6tSpAzc3N7Hp3ggh3Hjx4gWWLl2K3bt3QyAQsNurVq2KadOmwdHREQMGDKD3aOWIEq6aEAgEuHjxItLS0mBjYwNnZ2eNmxhcUfr3748OHTrAxsaG61AIqbDExEQsXboUO3fuFEm0VapUwYwZMzBx4kQYGBjg9OnTHEapnijhqoHw8HDMnTsXKSkp7DY7OzusW7cOvr6+HEamHszNzWFubs51GIRUSFJSEv755x+EhISgqKiI3W5paYnp06dj0qRJ7FquNPJYMegZbiUXFhaGESNGiCRbAEhNTYWfn5/YS+qqIjAwEDweDy9fvsTKlStRr149GBoaolGjRti/fz+Arz/0CxYsQJ06dWBgYIBmzZrh7NmzYm3dunULEydORJMmTWBubg5DQ0M0bdoUf/31l8RfHLGxsejatSuMjY1RtWpVDB48GMnJyejevTssLS1F6kp6hsvj8eDq6or09HSMGjUK1atXh6GhITp06ICYmJgS45w0aRI6duwIS0vLMuN0cHCAg4MDPn/+jMmTJ8Pe3h46OjoICQlBt27dUK1aNaSlpZV4foMGDQKPx8OdO3dKLCea4/Xr19i0aRMaN26Mbdu2scnW3NwcixcvRmJiIubNm1fqwulEPugKtxITCAT49ddfRYbuCzEMAx6Ph6lTp8LHx0dlby9PmzYN169fR58+faCtrY39+/cjICAAlpaWCAoKwsOHD+Ht7Y38/Hzs27cPffv2xdOnT1GnTh22ja1btyI8PBxdu3aFt7c3cnNzERMTgzlz5uDmzZs4cuSIyDHPnTuHXr16QUdHB4MHD4aNjQ1iYmLQpUsXsWRbls+fP6Nz584wMzPDkCFD8P79exw4cACenp64desWmjRpIhZnhw4d0Lt3b+Tl5ZUaJwAUFBSge/fuyM7ORp8+faCnpwdra2uMHTsWFy9eREhICObNmyeyT0ZGBo4fP47WrVujZcuW5TofUvkIBALExcWJPU5KSUnBsmXLsG3bNpE/6MzMzPDrr79i6tSpsLCw4C5wTcQQmWRmZjIAmMzMTKawsJA5duwYU1hYKFYvLy+Pefz4MZOXlyexrVWrVjG2traMra0tEx4ezggEArbs5cuXbNnEiRNF9ouOjmYAlPmflZUVc+TIEUYgEDCfPn1iBAIBk5WVxbYbEBDAtimsM2LECMbW1pZZtWqVWLzftiNNWWJiIgOAcXNzY7ePGDGCAcA4OTkx79+/Z+teu3aNAcBYWFgwXbp0YXJyctiyAwcOMACYyZMnixwzKSmJKSoqEtlWXFzMjBo1igHAXLp0id1eVFTE1K5dm9HS0mKuXbsmsk9gYCD7mX0bf3BwMAOACQ4OFqkvrPvLL7+I1N+2bRsDgPnpp5/E4iwsLBT5fCTFyTAMU7t2bQYA4+HhweTm5oqU5ebmMlWqVGF++OEHpri4WKRs9erVDABm8+bNIttL+reR1D9L69PlqVdauaQyaY/NNWXEWdYxjhw5wtjZ2Yn8vNvY2DBeXl6Mnp6eyHZTU1Nm/vz5zMePHyt8XFnqqnNf+TYflIZuKauArKwspKamIjU1FQUFBSJlAoGALfv06ZNImaTbid/LyMhAbm6uyDaGYdh2MzIyxPb58OEDUlNTkZWVVc6zKZ958+ahWrVq7Pft27eHo6MjPn/+jD///BPGxsZsmXDE5L1790TaqF27ttgVPI/Hw4QJEwAA58+fZ7dfunQJr169go+PD9q3by+yzx9//FHuOwHGxsb4+++/oaX1fz9KI0aMgI6ODm7evClznN9asWKF2EQD+vr6+PHHH/HixQtER0eLlG3fvh1GRkYICAgo17mQyiUsLAx+fn5ij5PS0tJw5swZFBYWAgBMTEzg5+eHhIQELFmypNx3cYj8UMJVAWZmZrC1tYWtra3YfKTa2tps2fc/KNKOmrWyshJ7h47H47HtWllZie1TtWpV2NraKvy5Tkm3PIXn1aJFC5Ht2traqF69OlJTU0W2FxYWYvXq1WjXrh3MzMygpaUFHo+H1q1bAwDevHnD1hUm606dOokd187ODrVq1SpX/E5OTmITtuvo6MDa2hqfP38Wi3PNmjVwc3ODhYVFqXEKGRgYoGnTpiUee8SIEQCAbdu2sduuXbuGR48eYdCgQfRMTo0JBAJMmTKlxMdJQjweDzNnzkR8fDyGDh2KKlWqKDFCUhJ6hqsCpk2bhmnTpqG4uFjsirJOnTpif8EKOTs7w87ODqmpqSX+4PF4PNjZ2SExMRHa2tois8eYmppKbBcAduzYIXLVpiglJQUdHZ1Sy74fYOTn54fw8HDUq1cPgwcPRvXq1aGrq4vPnz9j3bp1IncNhJ/vt1fV37K2tkZiYqLU8UsavayjoyPyysW3cdatWxeDBg2CtbW1xDiFqlevDh6PV+IxnJyc4OLigrCwMHz8+BFVqlRhk+/YsWOlPgdS+cTFxZX68wt8vYvVs2fPEv+gJtyghFuJaWtrY82aNeyI1G+TrvCX9Nq1a1V2wJQ83Lx5E+Hh4fD09MSpU6dEzvXatWtYt26dSH1hEk9PTy+xvXfv3ik0Tg8PD+zbtw+WlpbsHzQlxSkkKdkKjR07FrGxsdizZw9GjRqFAwcOoFGjRiVewRP1Ie3jJGnrEeWgW8qVnK+vL3bu3AlbW1uR7XZ2djh8+LDav4f74sULAECvXr3E/rCIi4sTq9+8eXMAwJUrV8TKUlJSkJycrIAoyx+ntAYMGAArKyts27YNBw4cQE5ODsaMGVOhWInqk/ZxEk3Woloo4aqBPn364OXLl4iOjsa+ffsQHR2NxMREtU+2wNeBSMDXwVDfevToEZYvXy5Wv0uXLqhVqxaOHz+OGzduiJTNnz9fZEIALuOUlp6eHkaMGIEHDx5gwYIF0NPTw/DhwysUK1F9Zf2RxuPxYG9vD2dnZyVFRKRBt5QriM/ns88cS5q8gM/ng2EYFBcXizxDLYnwlrCwvjSE+2hpaaFr164iZd+3IU37Fa1TUtm3dYTbhfVK+1xK+wyEZW3atEG7du1w8OBBpKWloX379nj9+jXCw8Ph7e3NruUprM/j8bBp0yb069cPLi4uGDx4MGrUqIGLFy8iNTUVzZs3x/3790uMX1Ks5Ynz0KFDSElJQadOnZCcnCwxzrLa//ZzHjNmDFatWoU3b95g0KBBsLS0LHOfb8+JYRjw+XyRK29hXy5rxqGy6pVWLqlM2mNzTRlxlnSMFStWYMGCBRL3ET6GWLlyJYqLi2WKszz7UF+R/viUcMspKCgIQUFB7ICYc+fOsSOAIyMjxerr6OigRo0ayMnJYYfplyU7O7vccZVnH2nqVrTOt2U5OTli24UdNCcnR2ygmPAqs6RXkoSJ4tuyvXv3YvHixbhw4QJu3rwJR0dHLFmyBO7u7jhy5Aj4fL5I/c6dOyMsLAzLli3DwYMHYWBgABcXF/z3338YNGgQTE1NReLPz89n/19SrLLEeefOnTLjLKmN72VnZ6NGjRpo27Ytbt68iYCAgDJf5fr23AoLC5GXl4eLFy+WeHVfUp8uSVn1SiuXVCbtsbmmjDiFxzh27JjIjGeurq548OABPnz4wG6rWrUqRo8eDX19fZH5kGWJszz7aHJf+f61S0l4TGnjyolEWVlZMDc3R0ZGBgwNDREZGQl3d3exlTXy8/ORnJwMBwcHGBgYlNomwzDIzs6GqalpmYNlZNlHmroVrSOpTJZzU7bs7GzY2NigUaNGuH79ukLilNfn8G07BQUFsLe3h4WFBeLj48v175afn4+kpCTY29uL9E8+ny+xT3+rrHqllUsqk/bYXFNGnN8eY9OmTfjtt9/YsqVLl2LmzJkQCAS4dOkSO9NUly5dxO5WlDfO8uxDfeVrPrCyskJmZmapr+PRFW4F6erqsv/Q334tJBAIwOPxoKWlVeZrNt/e9pT2lZzy7CNN3YrWkVQmy7kpypcvX1BcXAxTU1N2m0AgwKxZs5CXlwdvb2+FxSmvz+HbdkJCQvDx40fMmjWr1BHpJR1b+C5wSX0XKLlPl6SseqWVV/TYXFNGnFu2bBFLtsIpPXV1ddGjR48y25AlzvLso8l9RdpjU8IlGichIQFdunSBp6cnHB0dkZ2djbi4ODx+/BiNGzfGTz/9xHWIUlmzZg2ys7Px33//oXr16hg/fjzXIREFOH36NP777z/2+0WLFonNn00qB0q4ROPY2tpi4MCBiI2NxZkzZ1BUVIRatWphxowZmDNnDudX4NJasmQJ9PT00Lx5c6xfv55mllJDW7duFUm28+fPx8KFCzmMiFQEJVyicapVq4bg4OASy0qa7UtVffr0iZ3KkqifrVu3svNsA8CcOXOwePFiDiMiFUU/qYQQomJ27NiBcePGsd9Pnz4df/75p8oOOCTSoYRLCCEqZOfOnSKzhfn4+GDZsmWUbNUA3VJWEnr7iqgi6peqZc+ePRg5ciT77zJp0iR0796dkq2aoCtcBRO+psH1TCiElETYL9V5gYvKIjQ0FCNGjBBJtitXrqRkq0Yo4SqYrq4u9PX1kZmZSVcTRKUwDIPMzEzo6+tXivdd1dnBgwcxdOhQ9l3pn3/+GevWraNkq2bolrISWFlZITU1FSkpKTA3N4eurm6JP0jFxcUoLCxEfn5+uSa+kHYfaepWtI6kMlnOjQuKjlNe7Vekr+Tl5UEgECAzMxM5OTliK00R5Tpy5AgCAgLYZDtu3Dhs3LiRkq0aooSrBML3IzMyMpCamiqxHsMwyMvLg6GhYbmmdpR2H2nqVrSOpDJZzo0Lio5TXu3Lo6/o6+vD1taW3t/l0NGjR+Hv78/OzT569Ghs3rxZpf8oJbKjhKskZmZmMDMzA5/PZ3+4vsfn83Hx4kV07dq1XHOeSruPNHUrWkdSmSznxgVFxymv9ivSV1xcXOg2sgo4ceIEBg0axC4aERgYiP/++4+SrRqjhKtkpc35qa2tjaKiIhgYGEj9y7A8+0hTt6J1JJXJcm5cUHSc8mq/In2Fki33Tp48CT8/PzbZDhs2DNu2baNkq+boX5cQQpQoIiICAwYMYEeIBwQEIDg4mEaKawBKuIQQoiTnzp1D//792bWx/f39sXPnTkq2GoISLiGEKMH58+fh4+ODgoICAMDAgQOxe/du6OjQkz1NQQmXEEIULCoqCn379kV+fj4AwNfXF3v37qVkq2Eo4RJCiALFxsaiT58+yMvLA/B1buTQ0FAauKaBKOESQoiCxMXFoVevXsjNzQUA9OnTBwcPHoSenh7HkREuUMIlhBAFuHLlCry9vfHlyxcAgLe3Nw4dOkTJVoNRwiWEEDm7du0avLy8kJOTAwDw8vLCkSNHoK+vz3FkhEuUcAkhRI5u3LgBT09PZGdnAwDc3d0RFhYGAwMDjiMjXKOESwghcnLr1i14eHggKysLANC9e3ccO3YMhoaGHEdGVAElXEIIkYM7d+7A3d0dmZmZAABXV1eEh4fDyMiI48iIqqCESwghFXTv3j306NEDnz59AgA4Ozvj5MmTlGyJCEq4hBBSAQ8ePICbmxs+fvwIAOjcuTNOnz4NY2NjjiMjqoYSLiGEyOj169fw8vLChw8fAAAdO3ZEREQETExMOI6MqCKaV4wQQmTw5MkTzJ8/n31m265dO0RERMDU1JTjyIiqoitcQggpp2fPnsHT05NNtm3atMHZs2dhbm7OcWRElVHCJYSQckhISEC3bt3w9u1bAEDLli1x7tw5WFhYcBsYUXmUcAkhRErPnz9Ht27dkJaWBgCoU6cOIiIiYGlpyXFkpDKgZ7iEECKFly9folu3bkhNTQUANG3aFL/99huqVKnCcWSksqArXEIIKUNSUhK6deuGlJQUAECTJk1w5swZmJmZcRwZqUzoCreC+Hw+u4g0n8+vcFvlbac8+0hTt6J1JJXJcm5cUHSc8mpfFfqKNPXUoa+8evUK7u7ueP36NQCgYcOGOHPmDPvMVpFxyuOzoL6ieNIen8cwDKPgWNRKUFAQgoKCIBAIEB8fj3379tFsMoSoqfT0dPz+++949+4dAMDOzg5Lly6lAVJERG5uLgICApCZmVnqXQ9KuDLKysqCubk5MjIyYGhoiMjISLi7u0NXV1fmNvl8frnbKc8+0tStaB1JZbKcGxcUHae82leFviJNvcrcV1JTU9GjRw+8ePECAODk5ITz58/DxsZGaXHK4xjUVxQvKysLVlZWZSZcuqVcQbq6uuw/9Ldfy6tNRewjTd2K1pFUJq/PSNEUHac69RVp6lW2vvLmzRt4eHiwybZu3bqIjo6Gra2tWF1lxCmPY1BfURxpj02Dpggh5Btv375F9+7dkZCQAABwdHSUmGwJKQ9KuIQQ8v+9e/cO3bt3x7NnzwB8fc82OjoadnZ2HEdG1AElXEIIwdcBUm5ubnjy5AkAoHbt2oiOjkatWrU4joyoC0q4hBCNl5GRATc3Nzx69AgAYG9vj+joaNSuXZvjyIg6oYRLCNFoHz58QI8ePfDgwQMAX1/9iY6ORp06dTiOjKgbSriEEI316dMnuLu74969ewCAmjVrIioqCj/88APHkRF1RAmXEKKRPn/+DHd3d9y5cwcAUKNGDURFRcHJyYnjyIi6ooRLCNE4mZmZ8PT0xK1btwAA1tbWiI6ORv369TmOjKgzSriEEI2SlZUFLy8v3LhxAwBQrVo1REVFoUGDBhxHRtQdJVxCiMbIzs5Gz549ce3aNQCAlZUVoqKi0KhRI44jI5qAEi4hRCPk5OTA29sbV65cAQBUqVIFFy5cQJMmTTiOjGgKSriEELX35csX9O7dG5cuXQIAWFpa4sKFC2jWrBnHkRFNQgmXEKLWcnNz0adPH8TGxgIALCwscP78ebRo0YLbwIjGoYRLCFFbeXl58PHxQXR0NADA3NwckZGRaNWqFceREU1ECZcQopby8/PRr18/nD9/HgBgZmaGs2fPok2bNhxHRjQVJVxCiNopKCiAr68vzp07BwAwMTHBmTNn0L59e44jI5qMEi4hRK0UFBTAz88PERERAABjY2NERESgY8eOHEdGNB0lXEKI2igsLMSgQYNw8uRJAICRkRFOnz6NLl26cBwZIZRwCSFqgs/nw9/fHydOnAAAGBoa4tSpU+jatSvHkRHyFSVcQkilx+fzERAQgKNHjwIADAwMEB4eDldXV24DI+QblHAJIZVaUVERhg0bhsOHDwMA9PX1ceLECbi5uXEcGSGiKOESQiotgUCAESNG4MCBAwAAPT09HD9+HO7u7hxHRog4SriEkEpJIBAgMDAQ+/btA/A12R49ehSenp4cR0ZIySjhEkIqneLiYowePRp79uwBAOjq6uLw4cPw9vbmODJCJKOESwipVIqLizF27Fjs3LkTAKCjo4NDhw6hT58+HEdGSOko4RJCKo3i4mKMHz8eO3bsAABoa2vjwIED8PHx4TgyQspGCZcQUikwDIOJEydi69atAL4m29DQUPj6+nIcGSHS0ZF1x7y8PNy4cQMpKSnIyMiAkZERqlWrhqZNm+KHH36QZ4yEEA3HMAwmTZqEzZs3AwC0tLSwd+9eDBw4kOPICJFeuRJuXl4eQkNDERwcjBs3bqCoqAjA1x8GHo/H1rOxsUH//v0xbtw4NG3aVL4RE0I0CsMw+PXXXxEUFATga7LdvXs3Bg8ezHFkhJSPVAm3sLAQa9aswd9//43Pnz/D2NgYHTt2ROvWrWFtbY0qVaogLy8PHz9+xLNnz3D9+nUEBQVh06ZN6N69O1asWEGLPRNCyo1hGMyYMQPr1q0DAPB4PISEhCAgIIDjyAgpP6kSbr169ZCamop+/fph6NCh8Pb2hq6ubqn7vHz5Ert378auXbvQpk0bbN26FSNHjpRL0IQQ9ccwDGbNmoXVq1cD+Jpsd+zYgWHDhnEcGSGykWrQlKurK54+fYpDhw7Bx8enzGQLAI6Ojli4cCHi4+OxdetWaGnR+CxCiHQYhsH8+fOxYsUKdtvWrVsRGBjIXVCEVJBUV7ghISEyH0BbW5uubAkhUmMYBvv27cOhQ4fYbVu2bMHo0aM5jIqQiqPLTkKISlm6dKlIst20aRPGjRvHYUSEyAclXEKIyli6dCn++OMP9vsNGzbg559/5jAiQuRH5vdwX716hbVr1+LevXtITU0Fn88Xq8Pj8fDixYsKBUgI0QzLly/H/Pnz2e9XrVqFiRMnchgRIfIlU8I9d+4cfHx8UFBQAF1dXVSvXh06OuJNMQxT4QAJIepvxYoVmDt3Lvv9yJEjMWnSJA4jIkT+ZEq4v/32G7S0tHDgwAEMGDCARiATQmS2du1azJw5k/1++fLlaNiwIYcREaIYMmXK+Ph4BAQEYODAgZRsCSEyCw8PF0m2y5Ytw/Tp0zmMiBDFkSlb2tjYwMDAQN6xEEI0yKZNm7B9+3b2+z/++ANz5szhMCJCFEumhDt06FBEREQgPz9f3vEQQjTA5s2bMXXqVPb7hQsX4vfff+cuIEKUQKZnuAsWLMDdu3fh6emJZcuWoXnz5jAxMZF3bJUCn89nB4yVNFK7vG2Vt53y7CNN3YrWkVQmy7lxQdFxyqt9Vegr0tQrqXzbtm345Zdf2O9nzZqFuXPnitXV9L4ir2NU5r5SVpmq9BVpj89jZBxKfO7cOfj7+yMzM1Ny4zweu6KQuggKCkJQUBAEAgHi4+Oxb98+GBkZcR0WIZVCZGQku+oPAAwYMABDhw4VWW2MkMomNzcXAQEByMzMhJmZmcR6MiXcAwcOYMiQISguLoajoyNsbGxKfC0IAKKjo8vbfKWQlZUFc3NzZGRkwNDQEJGRkXB3d5dqnmlJ+Hx+udspzz7S1K1oHUllspwbFxQdp7zaV4W+Ik29b8tDQ0MxduxY9nXBqVOnwsXFBR4eHtRXFHiMythXKtvvlaysLFhZWZWZcGW6pbxkyRKYm5sjIiIC7dq1kzlIdaCrq8v+Q3/7tbzaVMQ+0tStaB1JZfL6jBRN0XGqU18pqZ5AIEBcXBySk5Px6tUrZGRkiCTbadOmYfny5YiIiKC+oqRjqGpfKU+5qvYVaY8tU8JNTEzEyJEjNT7ZEkLEhYWFYcqUKUhJSSmxfMqUKVi5cqXaPW4ipCwyJVx7e3sIBAJ5x0IIqeTCwsLg5+cncZa5nj17Ys2aNfTMlmgkmV4LGjt2LMLDw/Hx40d5x0MIqaQEAgGmTJlS6pSuDx8+RHFxsRKjIkR1yHSF6+fnh8uXL6NTp074/fff0aJFC4kPimvVqlWhAAkhlUNcXJzE28hCycnJiIuLg6urq3KCIkSFyJRwHR0dwePxwDAMRowYIbGeOr4WRAgpWVpamlzrEaJuZEq4w4cPp2cwhBARNjY2cq1HiLqRKeGGhITIOQxCSGXGMAwiIiJKrcPj8WBnZwdnZ2clRUWIapF5AXpCCAGA4uJiTJkyBf/++6/EOsI7YmvXroW2trayQiNEpVQ44V65cgV3795FZmYmzM3N0aJFC3Tq1EkesRFCVFxRURHWr1+PmJgYAF8T65gxYxARESEygMrW1hbr1q2Dr68vR5ESwj2ZE+7FixcxduxYPH/+HMDXW0rCv2KdnJywdetWunVEiBorKChAQEAAm2y1tbUREhKCoUOHis00NWPGDFrSk2g8mRLu1atX4eHhAT6fD29vbzg7O8Pa2hrv3r3DxYsXERERAQ8PD0RHR6NDhw7yjpkQwrHc3Fz4+vri7NmzAAA9PT0cOHAA/fr1A/A1+bq6uoLP5+P06dN0G5kQyJhw586dCx6Ph5iYGLGr2JkzZyI2Nhaenp6YO3cuoqKi5BIoIUQ1ZGZmonfv3rh06RIAQF9fH0ePHkXPnj05jowQ1SbTTFM3b97E4MGDJd4ydnFxweDBg3Hjxo0KBUcIUS0ZGRno3r07m2zNzMywaNEi9OjRg+PICFF9Ml3hGhgYwNbWttQ6tra29MyGEDXy5s0buLu74/HjxwAAKysrnDp1iiayIERKMl3hurm5lXmrOCoqiv7qJURNJCYmwtnZmU22NWvWxMWLF9GyZUuOIyOk8pAp4a5atQpv3rzByJEjkZqaKlKWmpqKwMBAvH37FitXrpRLkIQQ7jx58gRdunTBy5cvAQB16tTBpUuX0LBhQ44jI6RykXlqxypVqmDXrl3Yu3cvateujerVq+P9+/d49eoVBAIBmjVrhuHDh4vsx+PxcOHCBbkETghRvDt37qBXr17IyMgAADRs2BCRkZFlPlIihIiTKeEK37sDvr74/uLFC7x48UKkzr1798T2o/mXCak8njx5guHDhyMrKwsA0KpVK5w9exZWVlYcR0ZI5SRTwqX1LAlRb+fPn8eiRYtQUFAAAOjSpQtOnjwJc3NzjiMjpPKS6RkuIUR9HTt2DP369WOTrYeHB86ePUvJlpAKkkvC/fLlC9LS0vDlyxd5NEcI4ciePXvg5+eHwsJCAICPjw9OnDgBIyMjjiMjpPKTOeEWFBTgzz//RL169WBmZgY7OzuYmZmhXr16WLZsGfvXMSGkcvj3338xfPhwCAQCAICrqytCQ0Ohr6/PcWSEqAeZnuF+/vwZ3bt3x71796Cjo4P69euzcym/ePEC8+fPx5EjR3DhwgVYWFjIOWRCiLz9888/mDVrFvv9+PHj4eHhAR0dWsGTEHmR6Qp37ty5uHv3LgIDA5GUlITHjx8jOjoajx8/RlJSEkaMGIE7d+5g3rx58o6XECJHDMPg999/F0m2s2fPxrp166ClRUM8CJEnmX6ijh49ik6dOmH79u2wsbERKbOxscGOHTvQoUMHhIWFySVIQoj8CReO//PPP9lty5Ytw/Lly+kVPkIUQKaEm5WVBRcXl1LruLq6su/vEUJUi0AgwOjRo7FhwwZ228aNGzFnzhwOoyJEvcn0gKZx48ZITk4utU5ycjIaN24sU1CEEMUpLCzEyJEjcejQIQCAlpYWduzYgREjRnAcGSHqTaYr3Dlz5uDQoUMiM059KyoqCocOHaJnuISomIKCAvj5+bHJVldXFwcPHqRkS4gSyHSFm52djR49esDNzQ0eHh7o0qULO5dyXFwcIiMj0atXL2RmZmLXrl0i+34/vzIhRDmysrKwZMkSPHr0CABgaGiIsLAweHl5cRwZIZpBpoQbGBgIHo8HhmFw9uxZnD17VqzOyZMncerUKfZ7hmHA4/Eo4RLCgQ8fPsDT05NNtqampjh16hScnZ05jowQzSFTwg0ODpZ3HIQQBUlLS4O7uzubbKtWrYozZ86gTZs2HEdGiGaRKeHS8x5CKoekpCT06NGDXc3L0tIS58+fR4sWLbgNjBANRNPIEKKmnj17hh49eiAlJQUA4ODggFmzZtHbA4RwRKpRyqmpqRU+UFpaWoXbIIRI5969e3B2dmaTbYMGDRAVFSU2UQ0hRHmkSrg//PADJk+ejKSkpHI1LhAIcOjQITRr1gxbt26VJT5CSDldvXoVrq6uSE9PBwC0aNECsbGxsLOz4zgyQjSbVAl36dKl2LNnD+rWrQtXV1ds3LgR//vf/1BUVCRW982bNzh27BgmTpwIGxsb+Pv7w8bGBj/++KPcgyeEiLpw4QLc3d3x+fNnAEDHjh0RHR2N6tWrcxsYIUS6Z7gzZszA6NGjsXLlSgQHB2Py5Mng8XjQ0tKChYUFLC0tkZeXh48fPyI/P5/dz8PDA9OmTYO7u7vCToAQ8lV4eDgGDhzILo3p5uaGY8eOwcTEhOPICCFAOQZNWVpa4s8//8TixYsRERGBCxcu4OrVq0hJScHr169haGgIW1tbNG3aFC4uLvDx8UHt2rUVGTsh5P8LDQ3FsGHD2LVsfXx8sH//fhgYGHAcGSFEqNyjlHV0dNCnTx/06dNHEfEQQsrpv//+w/jx48EwDAAgICAAISEh0NXV5TgyQsi3aMFLQiqxNWvW4KeffmKT7U8//YTdu3dTsiVEBdF7uIRUQgzDIDQ0FAcOHGC3/fbbb/j7779pLVtCVBRd4RJSyTAMg99++00k2S5dupSSLSEqjq5wCalEBAIBxo8fj23btrHb1q5diylTpnAYFSFEGpRwCakkCgsLMXz4cPbKVktLC//++y/Gjh3LcWSEEGlQwiWkEsjLy8PAgQPZJS91dHQwdepUBAYGchsYIURqlHAJUXHZ2dno27cvYmJiAAAGBgY4cOAAOzKZEFI50KApQlTYx48f0aNHDzbZmpiY4MyZM+jZsye3gRFCyo2ucAlRUW/fvoWHhwcePHgA4Otsb2fOnEG7du3A5/M5jo4QUl5SJdzu3bvL7YCBgYEYPny43NojRB29fv0aPXr0QEJCAgDA2toakZGRaNq0KceREUJkJVXCffnypdze7xOuYkIIKVlCQgLc3NyQnJwMAKhVqxbOnz8PJycnjiMjhFSEVAm3vOvgEkJkc//+fXh4eODdu3cAACcnJ5w/fx61atXiODJCSEXRoClCVMT169fh6urKJttmzZohLi6Oki0haoISLiEqICYmBj169MCnT58AAO3bt0dMTAysra05jowQIi8VHqV85coV3L17F5mZmTA3N0eLFi3QqVMnecRGiEY4deoU/Pz8kJ+fDwDo1q0bjh8/DlNTU44jI4TIk8wJ9+LFixg7diyeP38O4OuE6sKBVU5OTti6dSucnZ3lEyUhaurAgQMYOnQoioqKAAC9e/fGoUOHaOF4IjWBQIC4uDikpaXBxsYGzs7O0NbW5josUgKZEu7Vq1fh4eEBPp8Pb29vODs7w9raGu/evcPFixcREREBDw8PREdHo0OHDvKOmRC1sH37dowdO5adMcrf3x+7du2itWyJ1MLCwjBlyhSkpKSw2+zs7LBu3Tr4+vpyGBkpiUwJd+7cueDxeIiJiRG7ip05cyZiY2Ph6emJuXPnIioqSi6BEqJO1q9fjxkzZrDfjxkzBv/++y9dmRCphYWFwc/PT2yKz9TUVPj5+eHw4cOUdFWMTIOmbt68icGDB0u8Zezi4oLBgwfjxo0bFQqOEHXDMAwOHDggkmx//fVX/Pfff5RsNVhhYSE+ffokljwTExMRGxuLbdu24fHjx+x2gUCAyZMnlziftnDb1KlTIRAIFBs4KReZEq6BgQFsbW1LrWNra0vPoQj5BsMwmDNnDkJDQ9ltixYtwqpVq9Ru4XiBQICYmBjs378fDx48UJtf/AzDIC8vT2x7YmIijh49il27diExMVGk7PPnzwgICEDfvn2xcOFCsX29vLygr6+PKlWq4MuXLyJlly5dwpo1a/DLL7+w82kDQFxcHFJTU0uNMzk5GXFxceU8Q6JIMt1SdnNzK/NWcVRUFHr06CFTUISoG4FAgF9++QX//fcfu23VqlWYNm0ah1EpRknPFf/991+sX79eqbc4i4uLoaUlek2RmJiIp0+fIicnB87OzqhRowZb9vbtW8ybNw85OTno2LEjpk6dKrKvq6sr4uLiwOPxxOayPnnyJCZPngwA2LNnD+rUqcOW8Xg89o+sgoICsTi/vTD58uULTExM2O+//TonJ4f9Oi0trczzL089ohwyJdxVq1ahc+fOGDlyJJYuXSpytZuamop58+bh7du3OHz4sNwCVVV8Ph86Ojrs1xVtq7ztlGcfaepWtI6kMlnOjQuKiJPP52PUqFHswvE8Hg8bNmzAuHHjZD6OKvSVkuodPXoU/v7+Yrc637x5Az8/P+zfvx/9+/cv9Rh8Ph/v3r3DhQsXkJ+fj06dOsHS0pItf/XqFVavXo2cnBy4uLiIzc3epUsXPHr0CAYGBmIJZ+fOnVi8eDEA4NixY/D29mbLvnz5gh07dgD4+gfShAkTRPZlGAbFxcUAviY/4SMAPp8PQ0NDtl5mZqbIOenp6bFfZ2dni51vw4YN8eHDB5iYmIDP54uUN27cGGPGjEGrVq3Qvn17tqxatWqQxrd/HKhaXylPuar/XpH2+DxGhkU1u3fvjk+fPuH+/fvQ1tZG7dq1Ub16dbx//x6vXr2CQCBAs2bNRH5IgK//+BcuXCjv4VRKUFAQgoKCIBAIEB8fj3379sHIyIjrsIiKKiwsxIoVK3Dz5k0AgLa2NqZOnaqWr8wJBAKMGzcOHz58kFjHysoKW7Zsgba2NqZNm4b09HRYWFhgw4YNIvWCg4Nx/PhxAMDy5cvRsGFDtiwpKYm9+nR3dxdLjFOnTkVSUhJ0dHTE/ug/ceIEm1RnzJiBLl26sGXZ2dkYNmwYAKBVq1ZYsGCByL7//vsvnj9/DgMDA8ydO1fk5/7Vq1e4desWDAwM0KRJE7HZwdLS0mBoaAhDQ0Po6+tL/HykJc1nDQAWFhZYtGgRHBwcKnxMIllubi4CAgKQmZkJMzMzifVkSrjf36aRFo/HU5tnOVlZWTA3N0dGRgYMDQ0RGRkJd3f3Cr3Swefzy91OefaRpm5F60gqk+XcuCDPOHNycjBgwABER0cDAPT19bF3717o6OioRV/5vt6VK1fg7u5eZhyRkZFwcXGBk5MTXr16hWrVqok8j/z+jsDJkyfh4eHBlr98+RINGjQAAAwePBi7d+8Wad/f3x9Pnz6FiYkJYmNjRQajXb9+HWfPnoWpqSm8vLxEErlAIGD3Mzc3h4WFhVw+o4oo7RjCuwkARO4o8Hg89vu6desiJiYG//vf/1Sqr5RUrzL/XsnKyoKVlVWZCVemW8rC2yoE0NXVZf+hv/1aXm0qYh9p6la0jqQyeX1GilbROD99+gRvb29cu3YNAGBsbIzw8HB06dIFp0+fVqu+IqyXnp4uVZvp6enQ1dVF7dq1kZ+fDwcHB7FjNGrUCDNmzIC5uTnq1asnUu7g4ICbN2/CxMQEVatWFdv3yJEjEo/dpUsXkava78+hRYsWUp3D9/spuk+XdIxBgwZBR0enxPdwFy9ejLCwMKxYsQLVq1eXOU5F9ZXS6lXG3yvSHpsWoCdEzt6/fw8PDw/cu3cPwNfbehEREejQoQPnz5oUycbGplz1zp8/j9OnT4s8RxVq3rw5vL29S/xFpq+vjzZt2lQsWDXh6+sLHx+fEmeaGjlyJADR54sFBQXg8/kig7GI8lDCJUSOkpOT0aNHD8THxwMAqlevjnPnzqF58+YcR6Z4WlpasLW1xZs3b0p8P5TH48HOzk4tn19zSVtbG66urmXWYxgGP//8M27duoUTJ06gdu3aig+OiJA64e7atUumA3w/gpAQdfX8+XP06NEDr169AvD11t758+dRv359jiNTvEOHDmH48OFo164d3rx5I/IcEQD7nvHatWtpgg+OBAUFITg4GMDXyYmePXsmlwFcRHpSJ9zAwMByvZwvXMyAEi7RBA8fPoS7uzvevn0L4OtglfPnz2vEVURmZiZ++eUXCAQCXL16FT/99BNOnTol8lzR1taW5vflmLu7O5ycnJCQkIC///6bki0HpE64y5YtE0u458+fR1RUFJYtWyb3wAipLG7evAkvLy98/PgRANCkSRNERkaKTKqgzszNzbF//34MGDAAw4YNY1+di4uLQ3JyMl69eoUZM2bQzHMcq1+/Pq5fv46TJ09i8ODBXIejkaROuLNnzxbblp+fj6ioKMyaNUuuQRFSWVy8eBG9e/dGdnY2AKBt27Y4c+YMqlSpwnFkyuXp6YkbN26gadOm7GuDrq6u4PP5OH36NN1GVhGWlpbsu8bfOnfuHNq3bw9zc3MOotIcsr1QSwhBREQEPD092WTr4uKCCxcuaESyffnypdi25s2by/yOPuHO5cuX0adPH3To0AEJCQlch6PW6KeDEBkcOnQIPj4+yM/PBwD07NkTERERMDU15TgyxVu5ciUaNGiA8PBwrkMhFVRcXIxx48ahsLAQT58+xdatW7kOSa1RwiWknIKDg+Hv78++3zhw4EAcO3ZMZD5ddRUeHo7ffvsNfD4ffn5+eP78OdchkQrQ0tJCeHg4GjVqBDc3N/z5559ch6TWKOESUg4bNmzAqFGj2NnWRo4cidDQUJEJ6tWZt7c3fvzxRwDA/PnzUbduXY4jIhXl6OiIq1ev4tChQ5ViFrjKjBIuIVJgGAbLli1jl2ADgMmTJ2Pbtm0aNSBIW1sbu3btwtGjR/H7779zHQ6REzMzM7HFZjIzMzFq1KgyF0gg0pN6lPI///wjtu3SpUsAgBUrVpQ4swwAzJw5U8bQCFENDMNg9uzZIj8D8+fPx+LFi9Vu4fjvMQyD7OxskWfTOjo66NevH3dBEYUrLCzE33//jcePH+Pq1as4deqURkzgomjlei3o+9ljhCS9FsTj8SjhkkqtuLgYEyZMwL///stu++eff/Dbb79xGJVyFBcXY+rUqbh27RouXLggdgVE1FdiYiLevHkDAMjLy9OIwYDKIHXC3bFjh9r/NU/It4qKijBy5Ejs2bMHwNc/IDdv3oyffvqJ48iUY+/evezKOz179sTly5c16va5Jqtfvz5WrFiBHTt2YN26dahZsybXIamFck3tSIimKCgowLBhw3Ds2DEA//fsMiAggNvAlKhbt26Ii4tDRkYGJk6cSMlWw1SrVg0xMTFiAwIFAgGKiopoakgZSD1oasSIETh69Ci+fPmiyHgI4Vx+fj769+/PJls9PT0cOXJEo5It8HXxhTNnzmD//v0YOnQo1+EQDpR0V3POnDno3r073r17x0FElZvUV7jHjh3Dnj17oK+vj+7du6Nfv37o3bu3xswXSzTD58+fsWjRIjx9+hQAYGRkhOPHj6NHjx4cR6Z4xcXFYjNFNWnSBC1btuQoIqJqdu3ahRUrVgAAOnXqhEePHtGdj3KQ+go3IyMDp0+fRmBgIO7fv49x48bBzs4OHTt2xN9//40nT54oMk5CFC49PR0eHh5ssjU3N0dkZKRGJNuCggL079+/xLcRCBFq3LgxbG1tAQDTp0+nBSnKSeqEq6urC09PT2zatAmvX7/GzZs3MWfOHOTm5mLOnDlo0qQJ6tWrh5kzZ+LSpUsSXxMiRBWlpqaia9euuHv3LgDAysoK0dHR6NSpE7eBKYFAIED//v1x4sQJzJo1C+vXr+c6JKKiWrdujZs3b2LVqlX45ZdfuA6n0pF54ovWrVvjjz/+wL179/Dy5UusWrUKdnZ2WLt2LVxcXGBtbY3Ro0fjxIkTyMvLk2fMhFSIQCBATEwMQkNDERMTg4SEBDg7O7NXtlWrVkVUVJTG3ErV1tZG165dAXy9hd6kSROOIyKqzMbGBtOmTRPbfvPmTeTm5nIQUeUh9TPc0jg4OGDq1KmYOnUqPn36hJMnT+L48eM4dOgQgoODYWRkhJycHHkcipAKCQsLw5QpU0QWR9fS0mKnanR0dMSsWbPQoEEDrkLkhHD5zc6dO8PZ2ZmdJ5oQaSQlJWHo0KGoX78+jh8/zt52JqLkPrWjcL3Fw4cPIyMjAydPnqQRjkQlhIWFwc/PTyTZAmCTrZ2dHaKiomBtbc1FeJybPXs2nJ2duQ6DVDLFxcVYs2YNcnJycOvWLSxbtozrkFSWXBNuVlYWIiMj2We4enp68Pb2FpmlhxAuCAQCTJkypdSxBQzDaESy/fTpE3x8fPDs2TOuQyFqQEtLC7/++iscHBzQpk0bdhQzESdTwt2+fTvc3Nzw6dMndtu9e/dQv359eHl5wcXFBS4uLvTslqiMuLg4sSvb76WmprLzg6urrKwseHl54cSJE+jevTstr0fkwsHBAZcvX8bx48dhZGTEdTgqS6aEu2fPHuTk5IjMrTpt2jSkp6dj5MiR8Pb2xuXLl7F582a5BUpIRaSlpcm1XmXF4/HYW+hFRUXIz8/nOCKiLqpVqyY2BWR2djamTZuG7OxsjqJSLTIl3Pj4eLRo0YL9Pj09HTExMRgzZgy2bduG8PBwtG3bFnv37pVXnIRUiLQTtNjY2Cg4Em6ZmpoiIiICHh4eiI2NpRHJRGEEAgECAgKwZs0adO7cGUlJSVyHxDmZEu6HDx9QrVo19vu4uDgAgK+vL7utS5cuSExMrGB4hFScQCDAoUOHSq3D4/Fgb2+PLl26KCkq7lhZWeHs2bMaNxKbKFdCQgKbG1JSUmjkO2RMuFWrVhW59RYVFQVtbW2RSQIYhqEPmHAuPz8f/v7+pT7eEM4Xu3btWrWbpu7Vq1eYNWsWBAIB16EQDdOgQQNcv34dDRs2xMGDB+Hk5MR1SJyTKeE2a9YMx48fx6NHj/DixQuEhoaiU6dOMDExYeskJSWp/e05otoyMzPh5eWFw4cPA/i6cPqkSZNgZ2cnUs/Ozg6HDx8WuUOjDl6+fImuXbvin3/+wdixY9lnt4QoS/369XH//n2x6VEZhtHIPwJlmvhi5syZ6NGjB5o1a8Zumzp1Kvt1QUEBYmJi4OHhUeEACZHFmzdv0LNnT9y/fx/A1xmUjhw5Ai8vL6xZswZxcXFIS0uDjY0NnJ2d1e7KFgCePn3KLiJ++fJlkbcKCFEWHR3xNLNixQpERUVh//79sLCwUH5QHJEp4Xbr1g0nTpxAcHAwAGDQoEHo168fW3758mXUqlVL7a4YSOXw7NkzeHp64tWrVwC+PrM8deoU2rVrB+DrVIaurq4cRqgc3t7e2L9/P/744w+cOXMGVatW5TokQnDq1CnMnj0bDMOgU6dOuHXrFgwNDbkOSylkntqxV69e6NWrV4ll3bt3x507d2QOihBZXb9+Hb169cKHDx8AfH0/8OzZs6hXrx7HkXFjwIAB8PHxgY6ODo2pICrB1NQUVapUwYcPH+Dv768xyRZQwNSOhHDl9OnT6N69O5tsmzdvjitXrmhMsr19+zbOnTsntr2kW3qEcKVr1664ceMGZs+ejfnz53MdjlJJ/ZN48eJFmQ4gXIWEEEUKCQnBmDFj2IEY3bp1w9GjR2Fubs5xZMpx48YNeHp6Ij8/H6dOnUL37t25DokQiRwdHbF8+XKx7cLXh3R1dTmISvGkTriurq7s6xPloYkj0YjyMAyDv//+G3PmzGG3DRo0CLt27YK+vj6HkSnX5s2b8fnzZwDAP//8g27dusn080oIVxITEzF37lwcOnQIR44cUcsxB1In3HHjxon9AP/vf//D7du3MW7cOLkHRkhZiouL8euvv4osmD5p0iSsXbsWWlqa9bRky5Yt+PDhA3JycnD48GFKtqRSYRgGAQEByMrKQmxsLGbNmoVt27ZxHZbcSZ1wS1rxZ/Hixbh9+zbNmUyUrqCgAMOHD8fBgwfZbcuWLcPs2bM1Mtno6enh0KFDEAgENHk8qXR4PB7Wrl2LPn36oFq1ali5ciXXISkEjaYglU5WVhb69++PqKgoAF9f89m2bRsCAwO5DUyJ4uLikJubK7JNk26hE/XTvn17rFy5Ep06dVLbd3M1674bqfTevn0LFxcXNtkaGhri+PHjGpVsT5w4gZ49e2LJkiXIycnhOhxC5MbKygp169YV2VZQUIDly5ejsLCQo6jkhxIuqTQSEhLQqVMn3L17FwBQpUoVREVFSXwfXB3l5uZi/PjxKCwsxNOnT0WeXxOibhiGwbhx47Bw4ULMnz8fb9++5TqkCqGESyqFmzdvolOnTuwKVLVq1cLly5fRoUMHjiNTLiMjI5w6dQoWFhbo2rUrZs6cyXVIhChMfHw8u9JXYmJipV+vmp7hEpV39uxZDBgwAF++fAEANGnSBGfOnIGtrS3HkXGjZcuWuHLlCp48eUKTWhC1Vr9+fVy6dAn9+vWDv78/WrZsyXVIFSL1T+u3o0GFHj58CAA4dOgQGIYpcb9BgwbJGBohwJ49ezBy5EgUFRUB+DqRyvHjx9V2UEVJHj16hMaNG4tsq1u3LuLj4zmKiBDladWqFR48eICYmBixMkl5R1VJnXD9/f3FXrcQnqy/v79YfYZhwOPxKOESma1cuRK//fYb+72vry/27t0LAwMDDqNSrqCgIEyaNAlBQUH4+eefuQ6HEE6U9Krbjh07EBERgYEDB3IQkWykTrgLFizQyPcbifIVFxfjt99+w+rVq9lt48ePx8aNG9VyGT1Jrl27hokTJwIAfvnlF7Rs2VLjnlkTUpJLly5h/Pjx4PP5uH37Njw8PCrFXS+pE+6iRYsUGAYhXxUWFmLUqFHYu3cvu23JkiX4/fffNe4Pvvbt22P27Nn466+/MGfOHLRv357rkAhRCdnZ2dDX1wefz0fDhg1hbGzMdUhSoREXRGVkZ2fDz8+PXfFGS0sL//77L8aOHctxZNzg8XhYtmwZunXrBnd3d437g4MQSXr27ImrV69i9erV6N27N9fhSI1eCyIq4f379+jWrRubbA0MDBAWFqZRyZZhGHZpQSEejwcPDw9KtoR8p0mTJtiyZYvYY6aUlBQUFxdzFFXppLrCff36tdwOaGFhATMzM7m1Ryq/Fy9ewNPTEy9evAAAWFpaIjw8HJ07d+Y4MuVhGAazZs3C/v37cfHiRTg4OHAdEiGVzvv379G5c2c0b94ce/fuhampKdchiZAq4To4OMjtL+yFCxdiwYIFcmmLVH63b99Gz5498f79ewCAnZ0dzpw5I/YajLpbuXIlVqxYAQBwc3PDgwcPaBECQsqBYRgMGjQIr1+/xuvXrzFp0iSEhIRwHZYIqRKuPEcou7i4yKUdUvmdP38e/fv3Z+cDbtSoEc6cOQN7e3uOI1O+4cOHY/v27YiPj8esWbMo2RJSTjweD/PmzcO9e/dgbGxc4gL3XJMq4dIIZSJv+/fvx/Dhw8Hn8wEAnTt3xokTJ1ClShWOI+OGtbU1oqKicOnSJXp3nRAZubu74/r16/jy5QtsbGy4DkcMDZoiSrd27Vr8+OOPbLLt27cvIiMjNSrZCgQCsYEdNWvWpGRLSAXVq1dPbApIgUCALVu2sDPWcYUSLlEahmEwZ84c/Prrr+y2sWPH4siRIzA0NOQwMuUqKirC8OHD8csvv1S6qekIqYxmzZqF8ePHw9vbG58+feIsDnoPlygFn8/H+vXrER0dzW5bsGABFi1apFGvvDAMg9WrV+PKlSsAAGNjY6xatYrjqAhRX8+fP2eXsYyOjsbDhw/h7OwMgUCAuLg4pKWlwcbGBs7OzgqfyY6ucInC5eTkwNfXl022PB4PmzdvxuLFizUq2QJfz719+/bg8XjQ19eHm5sb1yERotbq1q2L8+fPw8rKCuvXr4ezszPCwsLg4OCAbt26ISAgAN26dYODgwPCwsIUGgtd4RKFSk9PR69evXDz5k0AgL6+Pvbt2wdfX1+OI+OOi4sLmjRpAnt7e7i7u3MdDiFqr2vXrnjy5AmsrKwQFhYGPz8/scc5qamp8PPzw+HDhxX2+4kSLlGYxMREeHp6IiEhAcDXFT/Cw8PRvXt3jiPj3rBhw6Crq8t1GIRoDCsrKwgEAkyZMqXEsRPCFe6mTp0KHx8fhdxeplvKRCHu3buHTp06scm2Zs2aWLZsGZydnTmOTLmys7Ph7e2NuLg4rkMhROPFxcUhJSVFYjnDMEhOTlbYzyslXCJ3Dx48gJubG96+fQsAaNCgAWJjYzVuusLMzEx4eHggIiIC3t7euHbtGtchEaLR0tLS5FqvvCjhErk6fPgwFi9ejKysLABAhw4dcOnSJdSuXZvjyJTPwMCAXaNTT08Penp63AZEiIaTdjIMRU2aQQmXyM3GjRsxZMgQ9uXy3r1748KFC6hatSrHkXFDX18fYWFh8Pf3R1RUFFq1asV1SIRoNGdnZ9jZ2Ul8O4LH48He3l5hj74o4ZIKYxgG8+bNw6RJk9jBCIGBgTh69KjGzwlsaGiI0NBQNG/enOtQCNF42traWLduHQCIJV3h92vXrlXY+7iUcEmFFBUVYfTo0Vi2bBm7beDAgdiyZQt0dDRrEPybN28wduxY5Obmch0KIUQCX19fHD58GLa2tiLb7ezsFPpKEECvBZEKyM3NxZAhQ3Dq1CkAX/9CXLNmjVyXc6wskpOT0b17dzx//hyvXr3CiRMnYGBgwHVYhJAS+Pr6wsfHR+kzTVHCJTLJysqCp6cnrl+/DuDroKA9e/agX79+OH36NMfRKd+7d+/YNX2fP3+OjIwM2NnZcRwVIUQSbW1tuLq6KvWYdEuZlNurV68wZ84cNtmamZnhzJkzGDhwIMeRcadNmzaIiIhAq1atEBsbS8mWECKGrnBJuTx48ABeXl548+YNAKBGjRo4c+YMDQoC0KlTJ9y8eRNaWvR3LCFEHP1mIFK7ePEinJ2d2WTr5OSEq1evamSyffToEfbs2SO2nZItIUQSusIlUgkLC0NAQAAKCgoAfE22MTExqFmzJseRKd+9e/fQo0cPfPjwAQzDYNiwYVyHRAipBOjPcVKmzZs3w8/Pj022Xl5e+OOPP1CtWjWOI+PGkSNHkJGRAYZhsGnTJggEAq5DIoRUApRwiUQMw2DBggX45Zdf2AktRowYgSNHjmj0Ky+LFy/GhAkT0LFjR5w5c0bhrxIQQtQD3VImJSoqKsKECROwdetWdtusWbOwfPlydupGTcXj8bB+/Xrk5+dr/ExahBDpUcIlYgoKCjBo0CCcPHmS3bZ27VpMmTKFw6i4c/HiRTg6Ooq86qOlpUXJlhBSLpRwiYiPHz9i4cKFePr0KQBAV1cXu3btgr+/P8eRcePcuXPw8fGBvb09YmNjFbaKCCFE/dEzXMJKTk5Gt27d2GRramqKiIgIjU22AoEA06dPR35+PhISEvD3339zHRIhpBKjK1wC4Ot7pV5eXkhJSQEAWFtbIyIiAi1btuQ4Mu5oa2vj1KlTcHFxQatWrfDPP/9wHRIhpBKjhEtw+fJl9O7dG58/fwbwdfHl6Oho1K9fn9vAVECtWrVw+fJlVKtWDbq6ulyHQwipxCjharjjx4/D398f+fn5AIBWrVph8uTJcHR05Dgybvzvf/9jX4ES0sTJPQgh8kfPcDXY1q1b4evryyZbDw8PnD9/HhYWFtwGxpGQkBB07twZO3fuFEu6hBBSUZRwNRDDMFiyZAnGjRuH4uJiAMCQIUMQHh4OExMTjqPjRkJCAkaPHg2GYXDs2DF2jV9CCJEXSrgaRiAQYNKkSVi4cCG7bfr06di1axf09PQ4jEx5BAIBYmJiEBoaipiYGAgEAjg5OWHz5s0AgN69e6NXr14cR0kIUTf0DFeD5OfnY8WKFbh27Rq7beXKlZg+fTqHUSlXWFgYpkyZwo7GBgA7OzusW7cO48aNQ4MGDfDx40fweDwOoySEqCO6wtUQnz9/Rq9evdhkq6Ojgz179mhcsvXz8xNJtgCQmpoKPz8/hIWFoWPHjpRsCSEKQQlXA6SmpqJr166Ii4sDABgbG+PUqVMYMmQIx5Epj0AgwJQpU0ocDCXcNnXqVFr5hxCiMHRLWc09ffoUnp6eeP36NQDA3NwcZ86cQYcOHTiOTLni4uLErmy/xTAMkpOTcenSJSVGRQjRJHSFq8auXbuGzp07s8nW0dERf/31F1q3bs1xZMqXlpYm13qEEFJelHDV1KlTp9C9e3d8/PgRANCyZUvExMRo7OT70p63pn4+hBDFo4SrhoKDg+Hj44O8vDwAgJubG2JiYlCjRg2OI1O+3NxcTJ8+HfXr14ednZ3EAVE8Hg/29vbo0qWLkiMkhGgKSrhqhGEYLFu2DKNGjWIH//j7++PUqVMwMzPjODrlS0xMRMeOHbF69WoMGzYMa9asAQCxpCv8fu3atdDW1lZ6nIQQzUAJV00IBAJMnjwZ8+bNY7dNmTIFe/fuhb6+PoeRccfQ0BDv378H8PV5tpOTEw4fPgxbW1uRenZ2djh8+DB8fX25CJMQoiFolLIa4PP5GDp0KI4cOcJu++uvvzBz5kyNfqe0Ro0aOHDgACZOnIj9+/ejUaNGaN68OXx8fBAXF4e0tDTY2NjA2dmZrmwJIQpHCbeSy8zMxOLFi/Hw4UMAX9dw3b59O0aMGMFxZMr37t07VK1aFTo6/9etu3btijt37ogkVG1tbbi6unIQISFEk9Et5UosLS0Nbm5ubLI1MjJCeHi4RibbmJgYNGvWTOSWuhBdvRJCVAEl3EoqPj4enTp1wv379wEAVatWRVRUFHr27MlxZMr39u1b9OzZE+/fv8c///yDkydPch0SIYSIoYRbCd24cQOdO3dGUlISAKBatWqIiYlB+/btuQ2MIzVq1MDy5csBAO7u7hr7ORBCVBs9w61kIiIi4Ofnh9zcXABA06ZN8euvv6J+/focR8atKVOmwMbGBn5+fnQLmRCikugKtxLZtWsX+vbtyyZbV1dXREVFoUqVKhxHpjwMw2Dr1q0ICwsT2c7j8TB48GBKtoQQlaWRCXfdunVo3LgxTExMYGFhATc3N1y/fp3rsCRiGAb//PMPRowYgaKiIgCAn58fIiIiYG5uznF0ysPn8zFq1CiMGzcOgYGBiI+P5zokQgiRmkYm3Fq1amH16tW4d+8erly5gh9++AGenp748OED16GJKS4uxrRp0zBr1ix224QJE7B//34YGBhwGJny6ejooLCwEACQnZ2NY8eOcRsQIYSUg0Y+w+3fv7/I9ytXrsTWrVvx8OFDuLi4cBSVuIKCAgQGBmL//v3stj///BNz5szRyAkteDwe/vvvPzx//hxTpkxBQEAA1yERQojUVPYKd8+ePfjpp5/Qpk0b6Ovrg8fjISQkpNR9bt68CW9vb1haWsLY2Bjt2rXDvn37St2nsLAQ//33HywtLdG0aVM5nkHFZGVloVevXmyyFU5oMXfuXI1Jtnw+H6mpqSLbjI2NcfXqVUq2hJBKR2WvcH///Xe8evUKVlZWsLGxwatXr0qtHxMTA09PT+jp6cHf3x/m5uYICwvDkCFDkJSUhLlz54rUj4uLQ8+ePZGXl4caNWogMjJSZQYfvXv3Dn379sWdO3cAfJ0T+ODBg+jduzfHkSnPmzdv4O/vj4yMDNy4cQMmJiZsmZaWyv6dSAghEqnsb65t27YhKSkJ6enpGD9+fKl1i4qKMGbMGPB4PFy8eBFbt27FypUrce/ePTRu3BgLFy5EQkKCyD5t2rTB3bt3ceXKFfTs2RODBg1CRkaGIk9JKmlpaXBxcWGTbZUqVXDhwgWNSrYAEBgYiLi4ODx58gQ///wz1+EQQkiFqWzC7dGjB2rXri1V3aioKLx48QIBAQFo2bIlu93U1BTz589HUVERgoODRfYxNDRE3bp10b59e2zbtg1aWlpidRRNIBAgJiYGoaGhiImJwY0bNzB79my8fPkSAGBvb49Lly6hY8eOSo1LFWzcuBGmpqaws7PDhAkTuA6HEEIqTGVvKZdHTEwMAMDDw0OsTLgtNja21DYYhkFBQYHcY5MkLCwMU6ZMQUpKCruNx+OBYRgAQJMmTXDmzBmxpeQ0Rb169RAeHo5GjRqhWrVqXIdDCCEVphYJV3i72MnJSazM0tISVlZWIreUZ82ahb59+8LOzg4fP37Epk2bkJKSggEDBkg8RkFBgUhCzsrKAvB1YI9wdRo+ny9VvEePHoW/vz+bXIWE3zdo0AAXLlyApaVlmW0Ky6U5tjR1K1pHUllp+9y5cwd79+7FihUrRAaEderUqcxY5K08nyeX7cvSjrz7ijT15N1XVIky4pTHMaivKJ60x+cx3//WV0F//fUX5syZg+DgYAQGBoqVe3h4IDIyEgkJCahbt65Y+Q8//ICUlBQ2YQ4bNgwxMTF4//49qlSpgrZt22L+/Plo27atxBgWLVqExYsXi23ft28fjIyMpD4XgUCAcePGlfrOb9WqVfHff/9pxKxJ0dHR2LRpEzupRd++fbkOiRBCyiU3NxcBAQHIzMyEmZmZxHpqcYVbXrt37y73PnPmzMG0adPY77OysmBvbw8PDw8YGhoiMjIS7u7u0NXVLbWd2NjYMifY+PDhA8zMzKR6J5jP50t9bGnqVrSOpDJJ24uLi7Fu3ToAwJMnTxAUFMTpHxrl+Ty5bF+WduTdV6SpJ8++omqUEac8jkF9RfGEdzzLohYJVzi9YWZmZonlWVlZFZ4CUV9fH/r6+mLbdXV12X/ob7+WJD09Xarjpaenl6sDSXPs8tStaB1JZd9v79+/P2bPno3s7GysWrWqxM+YC+X5PLlsX5Z25N1XpKknj76iqpQRpzyOQX1FcaQ9tlokXOGz24SEBLRu3Vqk7NOnT8jIyGCfB3LNxsZGrvUqmxcvXqBBgwYi25YtW6Yxk3kQQjSXyr4WVB7CW6/nzp0TKxNuU5UpG52dnWFnZ1dqgrGzs4Ozs7MSo1I8Pp+PkJAQNGnShB1VLkTJlhCiCdQi4bq5ucHR0RH79u3D3bt32e3Z2dn4448/oKOjU+JgKy5oa2uzzyy/TzTC71etWqV2A6Z2796NY8eOQSAQwN/fXyUXiiCEEEVS2VvK27Ztw6VLlwAADx48YLcJr4769euHfv36Afi6isy2bdvg6ekJZ2dn/PjjjzAzM0NYWBgSExOxdOlS1KtXj4vTKJGvry8OHz4s9h6ura0thgwZIra4gjoYMWIENm/ejEePHmHevHkqM40mIYQoi8om3EuXLmHnzp0i2y5fvozLly8DABwcHNiECwDdunXDpUuXsHDhQhw8eBCFhYVo3Lgx/vjjDwwZMkSZoUvF19cXPj4+iIuLQ1paGmxsbNChQwecPXuW69AUQltbG9OmTYOjoyO6dOnCdTiEEKJ0KptwQ0JCylwd6Hvt2rVDRESEYgJSAG1tbbi6urLfc/3ytrzk5ORgzJgx+PPPP2FnZ8duNzc3R/v27TmMjBBCuKOyCZdUTk+fPsX06dPx7t07PH/+HDExMZXi1Q5CCFE0tRg0RVRHtWrVUFxcDOBr8n327BnHERFCiGqghEvkqmrVqpg5cyY6d+6M27dvo0mTJlyHRAghKoESLqmQxMREsVWWnJycEBUVJfXyioQQogko4RKZHT9+HC1atMD06dPFymgyC0IIEUUJl8jk48ePGDZsGLKyshAUFISjR49yHRIhhKg0GqVcQbKsh1taW+Vth6v1cKtUqYLVq1fjl19+gZ+fH1xcXMDn81V+3cqy0Hq45aurzmucloXWwy1fXXXuK2q1Hq4qCQoKQlBQEAQCAeLj48u9Hq66uXv3Lpo3b063kAkhGkva9XAp4cpIuORfRkZGudbDLY0qrFtZUh2GYbBq1SpYW1tj2LBhlXrdyrLQerjlq6vOa5yWhdbDLV9dde4rWVlZsLKyogXoFa286+GWt01F7FOetW75fD4GDRqE48ePw8DAAG3atEHDhg3LbEdV162UFq2HW7666rjGqbRoPdzy1VXHviLtsWnQFCmVrq4urK2tAQD5+fm4ePEixxERQkjlRFe4pEzr1q1DUlISpk6dip49e3I+QIEQQiojSrhERF5eHtLS0kS2GRgYqO0qRoQQoix0S5mwnj9/DmdnZyxatAifPn3iOhxCCFErlHAJa8KECbh//z7evXuHiRMnch0OIYSoFUq4hLV161ZUrVoVtra2mDt3LtfhEEKIWqFnuIRVq1YtnDp1ComJiWjcuDHX4RBCiFqhK1wNFRsbi8DAQHbtWqFWrVrB0NCQo6gIIUR90RWuBtqyZQumTp0KgUCAH374AfPnz+c6JEIIUXt0hauBnJycIJzR89KlSxAIBBxHRAgh6o+ucDVQ9+7dsXTpUuTm5mLRokXQ1tbmOiRCCFF7lHA1wN27d9GiRQuRbXPmzOEmGEII0VB0S1mN5eXlYcyYMWjZsiUiIiK4DocQQjQaJVw1tm/fPmzfvh0AMHToUHz8+JHjiAghRHNRwlVjo0aNgo+PD4yMjLB+/XpUqVKF65AIIURj0TPcCuLz+dDR0WG/rmhb5W2nrH22bt2K1NRUNG7cWKr2K1pHUpks58YFRccpr/YV0VdkqVtWPeor3B+D+oriSXt8HiN8P4RIJSgoCEFBQRAIBIiPj8e+fftgZGTEdVj49OkTNm/ejJEjR8LGxobrcAghRGPk5uYiICAAmZmZMDMzk1iPEq6MsrKyYG5ujoyMDBgaGiIyMhLu7u7Q1dWVuU0+n1/udvh8PrZu3Yrly5fj3bt3aN68OS5evFjibFHStF/ROpLKZDk3Lig6Tnm1L2tfkXYfaeuWVY/6CvfHoL6ieFlZWbCysioz4dIt5QrS1dVl/6G//VpebUqjevXqMDMzw7t375CRkYE3b96gQYMGFWq/onUklcnrM1I0RcfJVV8p7z7S1i2rHvUV7o9BfUVxpD02DZpSA0ZGRti/fz/69u2L27dvl5psCSGEcIMSbiX08OFD5Obmimxr2rQpjh8/jurVq3MUFSGEkNJQwq1kgoOD0bZtW/z888+gx++EEFJ5UMKtRNLS0jBp0iTk5+dj165dOHz4MNchEUIIkRIl3ErExsaGnTnq559/Rt++fTmOiBBCiLRolHIlM3jwYDg6OqJt27YAuH/hmxBCiHToCldFFRUVYfbs2di0aZNYmTDZEkIIqTzoClcF8fl89OrVCzExMdDV1UXr1q3Rvn17rsMihBBSAXSFyyGBQICYmBiEhoYiJiYGAoEAwNeXqFu1agUAYBgGjx494jJMQgghckBXuBwJCwvDlClTkJKSwm6ztbXF0KFD4e3tjb/++gspKSmYPHkyOnfuzGGkhBBC5IESLgfCwsLg5+cn9h7tmzdv8Pfff6NVq1YYNGgQDhw4wFGEhBBC5I1uKSuZQCDAlClTSpy0Qrht+vTp7O1lQggh6oESrpLFxcWJ3EYuSUpKCuLi4pQUESGEEGWghKtkaWlpcq1HCCGkcqCEq2TSLg5Pi8gTQoh6oYSrZM7OzrCzswOPx5NYx87ODs7OzkqMihBCiKJRwlUybW1trFu3DgDEkq7w+1WrVkFbW1vpsRFCCFEcei2ogvh8PnR0dNivpdGnTx/s378f06ZNQ2pqKrvd1tYWQ4YMQe/evaVuS1hPmvrS1K1oHUll5YmTS4qOU17ty9KOvPuKNPWor3B/DOoriift8XkMLapaLkFBQQgKCoJAIEB8fDz27dsHIyMjmdoSCAR4/PgxPn36BEtLSzRq1IiubAkhpJLJzc1FQEAAMjMzYWZmJrEeJVwZZWVlwdzcHBkZGTA0NERkZCTc3d2hq6src5t8Pr/c7ZRnH2nqVrSOpDJZzo0Lio5TXu2rQl+Rph71Fe6PQX1F8bKysmBlZVVmwqVbyhWkq6vL/kN/+7W82lTEPtLUrWgdSWXy+owUTdFxqlNfkaYe9RXuj0F9RXGkPTYNmiKEEEKUgBIuIYQQogSUcAkhhBAloIRLCCGEKAElXEIIIUQJKOESQgghSkAJlxBCCFECSriEEEKIElDCJYQQQpSAZpqSkXBGzKysLPD5fOTm5iIrK6vC0/WVt53y7CNN3YrWkVQmr89I0RQdpzr1FWnqUV/h/hjUVxQvKysLwP/lBUko4cooOzsbAGBvb89xJIQQQlRBdnY2zM3NJZbT4gUyKi4uxps3b2Bqagoej4e2bdvi5s2bFW5XlnbKs480dStap6SyrKws2NvbIzk5udTJvVWBvP4tFd2+KvQVaepRX+H+GNRXFIthGGRnZ6NmzZrQ0pL8pJaucGWkpaUFOzs79nttbW25/IPL0k559pGmbkXrlFZmZmam8r9E5fVvqej2VaGvSFOP+gr3x6C+onilXdkK0aApOZkwYQJn7ZRnH2nqVrSOvD4Lrig6fnXqK9LUo77C/TGor6gGuqVMlEK4fnBZ60USQn2FSKuy9RW6wiVKoa+vj4ULF0JfX5/rUIiKo75CpFXZ+gpd4RJCCCFKQFe4hBBCiBJQwiWEEEKUgBIuIYQQogSUcIlKWbduHRo3bgwTExNYWFjAzc0N169f5zosouJ+/vln8Hg8bNy4ketQiApatGgReDyeyH9t2rRRehw08QVRKbVq1cLq1atRt25dFBQUYO3atfD09MSLFy9QtWpVrsMjKujkyZO4evUqatasyXUoRIU1b94cZ86cYb/nYu5lSrhEpfTv31/k+5UrV2Lr1q14+PAhXFxcOIqKqKp3797h559/xunTp9GnTx+uwyEqTEdHBzVq1OA0BrqlTMptz549+Omnn9CmTRvo6+uDx+MhJCSk1H1u3rwJb29vWFpawtjYGO3atcO+fftK3aewsBD//fcfLC0t0bRpUzmeAVEWRfeVkSNHYvLkydQ/1ICi+8qTJ09gY2ODunXrYtSoUXj79q0CzqIMDCHlVLt2bQYAY2VlxX4dHBwssX50dDSjp6fHmJiYMGPGjGGmT5/O1KlThwHA/Pnnn2L1L168yBgbGzNaWlpMzZo1mf/9738KPBuiSIrsKxs2bGCcnZ0ZgUDAHmvDhg2KPB2iQIrsK6dPn2YOHjzI3L9/nzl9+jTTtm1bpmHDhkx+fr6Cz0oUJVxSbpGRkUxSUhLDMAyzfPnyUn8w+Hw+88MPPzD6+vrM7du32e1ZWVlM48aNGR0dHSY+Pl5kn9zcXCYhIYG5du0aM3r0aMbR0ZFJT09X2PkQxVFUX3ny5AlTvXp1JjExka1HCbdyU/TvlW+lp6czRkZGzOHDh+V6DmWhW8qk3Hr06IHatWtLVTcqKgovXrxAQEAAWrZsyW43NTXF/PnzUVRUhODgYJF9DA0NUbduXbRv3x7btm2DlpaWWB1SOSiqr1y7dg3p6emoW7cudHR0oKOjg1evXmHKlClo0aKFIk6FKJiif698y8rKCo6OjkhMTKxw3OVBg6aIQsXExAAAPDw8xMqE22JjY0ttg2EYFBQUyD02olrK01f69esn9lqHp6cnAgMDMXLkSMUGSjhX0d8rmZmZSEpKgoODgyLCk4gSLlGohIQEAICTk5NYmaWlJaysrNg6ADBr1iz07dsXdnZ2+PjxIzZt2oSUlBQMGDBAaTETbpSnr1hYWMDCwkKkjq6uLjsohqi38v5e+e2339C3b1/Y29sjJSUF8+fPh7W1Nby9vZUWM0AJlyhYZmYmAMmLM5uZmSElJYX9/s2bN/D398f79+9RpUoVtG3bFnFxcWjYsKFS4iXcKW9fIZqrvH0lOTkZgwcPRkZGBqytreHi4oKQkBAYGRkpJV4hSrhEpezevZvrEEgllZSUxHUIREXt37+f6xAA0Hu4RMGEf4EK/yL9nnABaUKorxBpVda+QgmXKJTwGcu3z1OEPn36hIyMjBKfwxDNQ32FSKuy9hVKuEShhNMxnjt3TqxMuI2mbCQA9RUivcraVyjhEoVyc3ODo6Mj9u3bh7t377Lbs7Oz8ccff0BHRweBgYGcxUdUB/UVIq3K2ld4DMMwXAdBKpdt27bh0qVLAIAHDx7g9u3b6Ny5M/s6Rr9+/dCvXz+2fnR0NDw9PaGvr48ff/wRZmZmCAsLQ2JiIpYuXYp58+ZxcRpECaivEGlpRF9R6rxWRC2MGDGCASDxv4ULF4rtc/36dcbLy4sxNzdnDA0NmTZt2jB79uxRfvBEqaivEGlpQl+hK1xCCCFECegZLiGEEKIElHAJIYQQJaCESwghhCgBJVxCCCFECSjhEkIIIUpACZcQQghRAkq4hBBCiBJQwiWEEEKUgBIuIYQQogSUcAkhhBAloIRLCFE7KSkpMDIywj///KPU4z5//hw6OjrYtGmTUo9LKgeaS5mQMiQlJaFOnTql1qEfI9UyfPhwnDlzBomJiTA2Nma3Ozg44O3bt8jPz5e4r4GBAWrUqIGkpCSZjj1ixAhERETg+fPnMDMzk6kNop50uA6AkMrihx9+wNChQ7kOg5QhPj4ee/bswYIFC0SSrbL89ttv2LVrF9avX4/ff/9d6ccnqosSLiFSqlu3LhYtWsR1GKQMW7ZsAcMwnP1x1KRJEzRv3hxbt27F3LlzoaVFT+7IV9QTCJGTRYsWgcfjISYmBjt37kTr1q1hZGQEV1dXtk52djYWLlyIxo0bw9DQEBYWFvDy8mIX3v7eo0eP0Lt3b5iamsLc3Bze3t54+PAhAgMDwePxRG57fnv874WEhIDH4yEkJESs7P79+/D394eNjQ309PRQu3ZtTJo0CR8+fBCpl5SUBB6Ph8DAQLx8+RJ+fn6wtLSEsbExevTogXv37pV4Du/fv8eMGTNQv359GBgYoEqVKujQoQNWrVoFAHj58iW0tLTQq1evEvf/9OkTDAwM0Lx58xLLv1VcXIxdu3ahZcuW7MLlFSU879L++/7286BBg/D69WtcuHBBLjEQ9UBXuITI2YoVKxAdHY2+ffvC3d0dOjpff8w+fvyIrl274tGjR3B2doanpycyMzNx/PhxdOvWDYcOHUK/fv3Ydh4+fIjOnTsjJycHvr6+cHJywo0bN9C5c2epko80Tpw4gUGDBkFbWxt9+/aFvb09Hj9+jI0bN+Ls2bO4fv06LC0tRfZJSkpC+/bt0ahRI4waNQovXrxgz+HJkyewtrZm6yYkJKBbt25ITU1Fly5d0K9fP3z58gUPHz7En3/+ienTp8PR0RE9evTAmTNnkJKSAjs7O5Hj7d69GwUFBRg7dmyZ53P//n1kZGRg4MCBcvl8AMDCwgILFy4U256Xl4fVq1ejuLgYBgYGImUdO3YEAERFRcHd3V1usZDKjRIuIVJ6/vx5ibeUvby80KFDB/b72NhYXL9+HU2bNhWpN2nSJDx69Ag7duzAyJEj2e3Lli1D27ZtMW7cOHh5ebG/vCdOnIisrCzs2bMHQ4YMYevPnTsXy5cvr/D5fPjwAcOGDUO1atVw+fJl1KpViy0LDQ1FQEAAFixYgA0bNojsFxsbi7/++guzZs1it82fPx9Lly5FcHAwZs+ezW4fOnQoUlNT8d9//4klzJSUFPbrcePGITIyEjt27MCCBQtE6m3fvh0GBgZS3SK+cuUKAKBVq1YS6xQVFZX6aKCoqEjkewsLC7H6DMPA398fRUVFWLFiBWrUqCFS3qZNG5F4CAEAMISQUiUmJjIAJP63Zs0ahmEYZuHChQwA5tdffxVrIz09ndHW1mbc3NxKPMb69esZAEx4eDjDMAzz6tUrBgDTrFkzsbrZ2dmMhYUFA4BJTExktwuPHx0dLbZPcHAwA4AJDg5mt61evZoBwOzevbvEmFq1asVYWVmJfQ516tRhBAJBiZ+Rr68vu+3GjRsMAKZr164ltv+twsJCxtramnFwcGCKi4vF2hg6dGiZbTAMw8yZM4cBwJw4caLE8tq1a5f6byn8r3bt2qUeZ/78+QwAZuTIkRLrGBgYMI6OjlLFTTQDXeESIiVPT0+cOXOmzHrt2rUT23bz5k0IBALk5+eXeHWVkJAAAHj69Cl69+7NPg/t0qWLWF0TExO0aNGixGe15XHt2jX2/8+fPxcrz8/PR0ZGBjIyMmBlZcVub968udhAIOFt4M+fP7Pbbty4AQDw8PAoMxZdXV2MGjUKy5cvR2RkJLvP9u3bAQBjxoyR6pyEz52/vw3+LX19/TJfCypNaGgo/vjjDzg7O+Pff/+VWK9KlSrIyMgoI2KiSSjhEiJn3z7DFPr48SMA4PLly7h8+bLEfb98+QIAyMzMBABUr15d6mOUlzCmoKCgUut9+fJFJOGam5uL1RE+pxYIBOw2YfK1tbWVKp6xY8fir7/+wrZt2+Dh4YHc3FyEhoaiXr16cHFxkaoNQ0NDAF+fryrCtWvXMGrUKDg6OiIsLAx6enoS6+bl5cHIyEghcZDKiUYpEyJnPB5PbJtwAoTp06eDYRiJ/wkH5wiT2vv370s8xrt378S2Ca86v38GCfxfAi8ppgcPHpQaU+3ataU5bTEWFhYAgNTUVKnq16lTB+7u7jh+/DgyMjJw8OBBZGVlSX11CwDVqlUD8H9/TMjT69ev0a9fP+jp6SE8PFzkj5DvFRcXIzMzk42HEIASLiFK0bZtW/B4PFy9elWq+sJRyCW9LpSTk4O7d++KbRfeRi0pwd25c0dsW/v27QFA6pjKS3hr/dy5c1LvM27cOBQWFmLXrl3Yvn07dHV1MWLECKn3Fw5UE96il5ecnBz06dMHGRkZOHDgABo1alRq/YSEBBQXF4sNnCOajRIuIUpQo0YNDBo0CFeuXMGKFStKnAry+vXryM3NBQDUqlULXbt2xf3797F3716ResuWLRN5ViokHBm7a9cuFBcXs9uvXr0q1gYAjBw5Eqamppg3bx4ePXokVp6bm8s+55VF27Zt0a5dO1y8eBFbt24VKy/pDwMfHx/UqFEDq1atwqVLl9C3b1+Jt9VL4uzsDC0tLfb5sTwUFxcjICAA9+/fx+rVq+Hl5VXmPtevXwcAqW+FE81Az3AJUZJNmzbh2bNnmDlzJnbv3o2OHTvC3NwcycnJuHXrFhISEpCWlsY+9wsKCkLnzp0xfPhwHDt2DE5OTrh58yZu3LgBZ2dnxMXFibTfoUMHdOzYEVFRUejYsSO6du2KV69e4cSJE+jTpw+OHj0qUr9atWoIDQ3FwIED0bx5c3h5eaFBgwbIz8/Hq1evEBsbi06dOkk1UEySPXv2wNXVFePGjWPPOT8/H48ePcKdO3fEJtfQ0dHBqFGjsGzZMgDSD5YSsrS0RNeuXREXF4eCggLo6+vLHLvQ4cOHER4eDhsbG3z8+LHEQW9Tp05lb6EDQGRkJLS1tdG7d+8KH5+oEQ5GRhNSqQhfefH09Cy1Xmmv5Qjl5uYy//zzD9O6dWvG2NiYMTQ0ZOrUqcP069eP2bVrF8Pn80XqP3jwgPH29mZMTEwYU1NTpmfPnsyDBw+YESNGiL0WxDBfXz8aNmwYU6VKFcbQ0JDp0KEDc/bs2RJfCxJ6+vQpM3r0aKZ27dqMnp4eY2lpyTRt2pSZPHkyc+PGDbHPYcSIESWeGwDGxcVFbPvbt2+ZKVOmMI6Ojoyenh5TpUoVpn379szq1atLbOfZs2cMAKZWrVpirx9JIzQ0lAHAHDlyRKysdu3ajL6+fqn76+vri7wWJPzsSvvv23+HL1++MCYmJky/fv3KHTtRb7RaECGVUGBgIHbu3InExEQ4ODhwHY5cHTx4EIMHD8bixYvFJsGQRmFhIerVq4eGDRsiIiJCARGWbseOHRg9ejRiY2PRtWtXpR+fqC56hksIURkMw2D16tXQ0dHB6NGjZWpDT08Py5Ytw5kzZ9hnqcpSVFSEZcuWoW/fvpRsiRh6hksI4dyDBw9w8uRJXLlyBdevX8f48eOlfn+3JD/++CNSUlKUPvFESkoKhg4dimHDhin1uKRyoFvKhFRC6nZLOSQkBCNHjoSFhQX69u2LoKAgmJiYcB0WIXJFCZcQQghRAnqGSwghhCgBJVxCCCFECSjhEkIIIUpACZcQQghRAkq4hBBCiBJQwiWEEEKUgBIuIYQQogSUcAkhhBAl+H9q/rW+rT+jdAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot data\n",
    "fig, ax = plt.subplots(1, 1, figsize=(5, 5))\n",
    "ax.loglog(frequencies, np.abs(dobs[0::2]), \"k-o\", lw=2)\n",
    "ax.loglog(frequencies, np.abs(dobs[1::2]), \"k:o\", lw=2)\n",
    "ax.grid(which=\"both\")\n",
    "ax.set_xlabel(\"Frequency (Hz)\")\n",
    "ax.set_ylabel(\"|Hs/Hp| (ppm)\")\n",
    "ax.set_title(\"Sounding Data\")\n",
    "ax.legend([\"Real\", \"Imaginary\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining the Survey\n",
    "\n",
    "Here, we define the survey geometry. For a comprehensive description of constructing FDEM surveys in SimPEG, see the [1D Forward Simulation of Frequency-Domain EM Data for a Single Sounding](fwd_fdem_1d.ipynb) tutorial.\n",
    "\n",
    "Here, the survey consisted of a vertical magnetic dipole source located 30 m above the surface. The receiver measured the vertical component of the secondary field at a 10 m offset from the source in ppm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:46.158339Z",
     "iopub.status.busy": "2024-11-20T02:13:46.158080Z",
     "iopub.status.idle": "2024-11-20T02:13:46.165579Z",
     "shell.execute_reply": "2024-11-20T02:13:46.164951Z"
    }
   },
   "outputs": [],
   "source": [
    "source_location = np.array([0.0, 0.0, 30.0])\n",
    "source_orientation = \"z\"\n",
    "moment = 1.0\n",
    "\n",
    "receiver_location = np.array([10.0, 0.0, 30.0])\n",
    "receiver_orientation = \"z\"\n",
    "data_type = \"ppm\"\n",
    "\n",
    "# Receiver list\n",
    "receiver_list = []\n",
    "receiver_list.append(\n",
    "    fdem.receivers.PointMagneticFieldSecondary(\n",
    "        receiver_location,\n",
    "        orientation=receiver_orientation,\n",
    "        data_type=data_type,\n",
    "        component=\"real\",\n",
    "    )\n",
    ")\n",
    "receiver_list.append(\n",
    "    fdem.receivers.PointMagneticFieldSecondary(\n",
    "        receiver_location,\n",
    "        orientation=receiver_orientation,\n",
    "        data_type=data_type,\n",
    "        component=\"imag\",\n",
    "    )\n",
    ")\n",
    "\n",
    "# Define source list\n",
    "source_list = []\n",
    "for freq in frequencies:\n",
    "    source_list.append(\n",
    "        fdem.sources.MagDipole(\n",
    "            receiver_list=receiver_list,\n",
    "            frequency=freq,\n",
    "            location=source_location,\n",
    "            orientation=source_orientation,\n",
    "            moment=moment,\n",
    "        )\n",
    "    )\n",
    "\n",
    "# Survey\n",
    "survey = fdem.survey.Survey(source_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assign Uncertainties\n",
    "\n",
    "Inversion with SimPEG requires that we define the uncertainties on our data; that is, an estimate of the standard deviation of the noise on our data assuming it is uncorrelated Gaussian with zero mean. An online resource explaining uncertainties and their role in the inversion can be found [here](https://giftoolscookbook.readthedocs.io/en/latest/content/fundamentals/Uncertainties.html).\n",
    "\n",
    "**For secondary field and ppm data:** In this case, we generally apply a percent uncertainty. Depending on many factors, a percent uncertainty between 5\\% and 20\\% may be applied. For systems where multiple field directions are measured for the same source, we may not want to apply a uniform percent uncertainty to all data. Doing so may cause the inversion to overfit weaker signals. In this case, the uncertainty may be a percent of the amplitude of the secondary field. If the data contain very small values, a small floor value should be added to ensure stability of the inversion.\n",
    "\n",
    "**For total field data:** In this case, the real component contains both the primary and secondary fields, whereas the imaginary component contains only secondary fields. Because the primary field is orders of magnitude larger than the secondary field, applying a simple percent uncertainty to the real component data may result in us underfitting the real component of the EM response. Ideally, we would have defined the survey to perfectly simulate the primary fields, and assign uncertainties based on the secondary fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:46.168803Z",
     "iopub.status.busy": "2024-11-20T02:13:46.168476Z",
     "iopub.status.idle": "2024-11-20T02:13:46.171929Z",
     "shell.execute_reply": "2024-11-20T02:13:46.171306Z"
    }
   },
   "outputs": [],
   "source": [
    "# 5% of the absolute value\n",
    "uncertainties = 0.05 * np.abs(dobs) * np.ones(np.shape(dobs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining the Data\n",
    "\n",
    "The SimPEG [Data](xref:simpeg#simpeg.data.Data) class is required for inversion and connects the observed data, uncertainties and survey geometry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:46.175044Z",
     "iopub.status.busy": "2024-11-20T02:13:46.174739Z",
     "iopub.status.idle": "2024-11-20T02:13:46.179384Z",
     "shell.execute_reply": "2024-11-20T02:13:46.178669Z"
    }
   },
   "outputs": [],
   "source": [
    "data_object = data.Data(survey, dobs=dobs, noise_floor=uncertainties)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weighted Least-Squares Inversion\n",
    "\n",
    "Here, we use the weighted least-squares inversion approach to recover the log-conductivities on a 1D layered Earth. We impose no a-priori information about the number of layers (geological units) or their thicknesses. Instead, we define a large number of layers with exponentially increasing thicknesses. And the depth, thickness and electrical properties of the Earth are inferred from the recovered model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining a 1D Layered Earth\n",
    "\n",
    "Let us assume we have a reasonable estimate of the regional conductivity within our area of interest. For the highest frequency and the estimated conductivity, we compute the minimum skin depth:\n",
    "\n",
    "$$\n",
    "d_{min} \\approx 500 \\sqrt{\\dfrac{1}{\\sigma \\, f_{max}}}\n",
    "$$\n",
    "\n",
    "The minimum layer thickness is some fraction of the minimum skin depth. Next, we use the minimum frequency and the estimated conductivity to compute the maximum skin depth:\n",
    "\n",
    "$$\n",
    "d_{max} \\approx 500 \\sqrt{\\dfrac{1}{\\sigma \\, f_{min}}}\n",
    "$$\n",
    "\n",
    "Starting from our minimum layer thickness, we continue to add layers with exponentially increasing thicknesses. We do so until the layers extend to some multiple of the maximum skin depth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:46.182803Z",
     "iopub.status.busy": "2024-11-20T02:13:46.182483Z",
     "iopub.status.idle": "2024-11-20T02:13:46.188305Z",
     "shell.execute_reply": "2024-11-20T02:13:46.187561Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MINIMUM SKIN DEPTH: 4.383604418942005 m\n",
      "MAXIMUM SKIN DEPTH: 80.89810021132169 m\n"
     ]
    }
   ],
   "source": [
    "# estimated host conductivity (S/m)\n",
    "estimated_conductivity = 0.1\n",
    "\n",
    "# minimum skin depth\n",
    "d_min = 500.0 / np.sqrt(estimated_conductivity * frequencies.max())\n",
    "print(\"MINIMUM SKIN DEPTH: {} m\".format(d_min))\n",
    "\n",
    "# maximum skin depth\n",
    "d_max = 500.0 / np.sqrt(estimated_conductivity * frequencies.min())\n",
    "print(\"MAXIMUM SKIN DEPTH: {} m\".format(d_max))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:46.191196Z",
     "iopub.status.busy": "2024-11-20T02:13:46.190914Z",
     "iopub.status.idle": "2024-11-20T02:13:46.195298Z",
     "shell.execute_reply": "2024-11-20T02:13:46.194525Z"
    }
   },
   "outputs": [],
   "source": [
    "depth_min = 0.5  # top layer thickness\n",
    "depth_max = 200.0  # depth to lowest layer\n",
    "geometric_factor = 1.1  # rate of thickness increase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:46.198550Z",
     "iopub.status.busy": "2024-11-20T02:13:46.198188Z",
     "iopub.status.idle": "2024-11-20T02:13:46.204443Z",
     "shell.execute_reply": "2024-11-20T02:13:46.203416Z"
    }
   },
   "outputs": [],
   "source": [
    "# Increase subsequent layer thicknesses by the geometric factors until\n",
    "# it reaches the maximum layer depth.\n",
    "layer_thicknesses = [depth_min]\n",
    "while np.sum(layer_thicknesses) < depth_max:\n",
    "    layer_thicknesses.append(geometric_factor * layer_thicknesses[-1])\n",
    "\n",
    "n_layers = len(layer_thicknesses) + 1  # Number of layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model and Mapping to Layer Conductivities\n",
    "\n",
    "Recall from the [1D Forward Simulation of Frequency-Domain EM Data for a Single Sounding](fwd_fdem_1d.ipynb) tutorial that the 'model' is not necessarily synonymous with physical property values. And that we need to define a mapping from the model to the set of input parameters required by the forward simulation. When inverting to recover electrical conductivities (or resistivities), it is best to use the log-value, as electrical conductivities of rocks span many order of magnitude.\n",
    "\n",
    "Here, the model defines the log-conductivity values for a defined set of subsurface layers. And we use the [simpeg.maps.ExpMap](xref:simpeg#simpeg.maps.ExpMap) to map from the model parameters to the conductivity values required by the forward simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:46.207983Z",
     "iopub.status.busy": "2024-11-20T02:13:46.207615Z",
     "iopub.status.idle": "2024-11-20T02:13:46.212182Z",
     "shell.execute_reply": "2024-11-20T02:13:46.211389Z"
    }
   },
   "outputs": [],
   "source": [
    "log_conductivity_map = maps.ExpMap(nP=n_layers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Starting/Reference Models\n",
    "\n",
    "The **starting model** defines a reasonable starting point for the inversion. Because electromagnetic problems are non-linear, your choice in starting model does have an impact on the recovered model. For DC resistivity inversion, we generally choose our starting model based on apparent resistivities. For the tutorial example, the apparent resistivities were near 1000 $\\Omega m$. It should be noted that the starting model **cannot be vector of zeros,** otherwise the inversion will be unable to compute a gradient direction at the first iteration.\n",
    "\n",
    "The **reference model** is used to include a-priori information. The impact of the reference model on the inversion will be discussed in another tutorial. The reference model for basic inversion approaches is either zero or equal to the starting model.\n",
    "\n",
    "Notice that the length of the starting and reference models is equal to the number of model parameters!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:46.215746Z",
     "iopub.status.busy": "2024-11-20T02:13:46.215421Z",
     "iopub.status.idle": "2024-11-20T02:13:46.220611Z",
     "shell.execute_reply": "2024-11-20T02:13:46.219571Z"
    }
   },
   "outputs": [],
   "source": [
    "# Starting model is log-conductivity values (S/m)\n",
    "starting_conductivity_model = np.log(1e-1 * np.ones(n_layers))\n",
    "\n",
    "# Reference model is also log-resistivity values (S/m)\n",
    "reference_conductivity_model = starting_conductivity_model.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the Forward Simulation\n",
    "\n",
    "A simulation object defining the forward problem is required in order to predict data and calculate misfits for recovered models. A comprehensive description of the simulation object for 1D DC resistivity was discussed in the [1D Forward Simulation of Frequency-Domain EM Data for a Single Sounding](fwd_fdem_1d.ipynb) tutorial. Here, we use the [Simulation1DLayered](xref:simpeg#simpeg.electromagnetics.frequency_domain.Simulation1DLayered) which simulates the data according to a 1D Hankel transform solution.\n",
    "\n",
    "The layer thicknesses are a static property of the simulation, and we set them using the ``thicknessess`` keyword argument. Since our model consists of log-conductivities, we use ``sigmaMap`` to set the mapping from the model to the layer conductivities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:46.223818Z",
     "iopub.status.busy": "2024-11-20T02:13:46.223460Z",
     "iopub.status.idle": "2024-11-20T02:13:46.231234Z",
     "shell.execute_reply": "2024-11-20T02:13:46.230306Z"
    }
   },
   "outputs": [],
   "source": [
    "simulation_L2 = fdem.Simulation1DLayered(\n",
    "    survey=survey, thicknesses=layer_thicknesses, sigmaMap=log_conductivity_map\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Misfit\n",
    "\n",
    "To understand the role of the data misfit in the inversion, please visit [this online resource](https://giftoolscookbook.readthedocs.io/en/latest/content/fundamentals/Uncertainties.html).\n",
    "Here, we use the [L2DataMisfit](xref:simpeg#simpeg.data_misfit.L2DataMisfit) class to define the data misfit. In this case, the data misfit is the L2 norm of the weighted residual between the observed data and the data predicted for a given model. When instantiating the data misfit object within SimPEG, we must assign an appropriate *data object* and *simulation object* as properties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:46.234574Z",
     "iopub.status.busy": "2024-11-20T02:13:46.234275Z",
     "iopub.status.idle": "2024-11-20T02:13:46.238798Z",
     "shell.execute_reply": "2024-11-20T02:13:46.237960Z"
    }
   },
   "outputs": [],
   "source": [
    "dmis_L2 = data_misfit.L2DataMisfit(simulation=simulation_L2, data=data_object)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regularization\n",
    "\n",
    "To understand the role of the regularization in the inversion, please visit [this online resource](https://giftoolscookbook.readthedocs.io/en/latest/content/fundamentals/ObjectiveFunction.html). \n",
    "\n",
    "To define the regularization within SimPEG, we must define a 1D [tensor mesh](xref:discretize#discretize.TensorMesh). Meshes are designed using the [discretize package](https://discretize.simpeg.xyz). Whereas layer *thicknesses* and our *model* are defined from our top-layer down, tensor meshes are defined from the bottom up. So to define a 1D tensor mesh for the regularization, we:\n",
    "\n",
    "- add an extra layer to the end of our thicknesses so that the number of cells in the 1D mesh equals the number of model parameters\n",
    "- reverse the order so that the model parameters in the regularization match up with the appropriate cell\n",
    "- define the tensor mesh from the cell widths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:46.242531Z",
     "iopub.status.busy": "2024-11-20T02:13:46.242126Z",
     "iopub.status.idle": "2024-11-20T02:13:46.249141Z",
     "shell.execute_reply": "2024-11-20T02:13:46.248186Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  TensorMesh: 40 cells\n",
      "\n",
      "                      MESH EXTENT             CELL WIDTH      FACTOR\n",
      "  dir    nC        min           max         min       max      max\n",
      "  ---   ---  ---------------------------  ------------------  ------\n",
      "   x     40       -219.43         -0.00      0.50     18.70    1.10\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Define 1D cell widths\n",
    "h = np.r_[layer_thicknesses, layer_thicknesses[-1]]\n",
    "h = np.flipud(h)\n",
    "\n",
    "# Create regularization mesh\n",
    "regularization_mesh = TensorMesh([h], \"N\")\n",
    "print(regularization_mesh)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the regularization acts on the model parameters. In this case, the model parameters are the log-resistivities, not the electric resistivities!!! Here, we use the [WeightedLeastSquares](xref:simpeg#simpeg.regularization.WeightedLeastSquares) regularization class to constrain the inversion result. Here, length scale along x are used to balance the smallness and smoothness terms; yes, x is smoothness along the vertical direction. And the reference model is only applied to the smallness term. If we wanted to apply the regularization to a function of the model parameters, we would need to set an approprate mapping object using the ``mapping`` keyword argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:46.252573Z",
     "iopub.status.busy": "2024-11-20T02:13:46.252215Z",
     "iopub.status.idle": "2024-11-20T02:13:46.260234Z",
     "shell.execute_reply": "2024-11-20T02:13:46.259484Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_L2 = regularization.WeightedLeastSquares(\n",
    "    regularization_mesh,\n",
    "    length_scale_x=10.0,\n",
    "    reference_model=reference_conductivity_model,\n",
    "    reference_model_in_smooth=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimization\n",
    "\n",
    "Here, we use the [InexactGaussNewton](xref:simpeg#simpeg.optimization.InexactGaussNewton) class to solve the optimization problem using the inexact Gauss Newton with conjugate gradient solver. Reasonable default values have generally been set for the properties of each optimization class. However, the user may choose to set custom values; e.g. the accuracy tolerance for the conjugate gradient solver or the number of line searches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:46.263660Z",
     "iopub.status.busy": "2024-11-20T02:13:46.263367Z",
     "iopub.status.idle": "2024-11-20T02:13:46.267983Z",
     "shell.execute_reply": "2024-11-20T02:13:46.267189Z"
    }
   },
   "outputs": [],
   "source": [
    "opt_L2 = optimization.InexactGaussNewton(\n",
    "    maxIter=100, maxIterLS=20, maxIterCG=20, tolCG=1e-3\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inverse Problem\n",
    "\n",
    "We use the [BaseInvProblem](xref:simpeg#simpeg.inverse_problem.BaseInvProblem) class to fully define the inverse problem that is solved at each beta (trade-off parameter) iteration. The inverse problem requires appropriate *data misfit*, *regularization* and *optimization* objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:46.271476Z",
     "iopub.status.busy": "2024-11-20T02:13:46.271146Z",
     "iopub.status.idle": "2024-11-20T02:13:46.276197Z",
     "shell.execute_reply": "2024-11-20T02:13:46.275129Z"
    }
   },
   "outputs": [],
   "source": [
    "inv_prob_L2 = inverse_problem.BaseInvProblem(dmis_L2, reg_L2, opt_L2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inversion Directives\n",
    "\n",
    "To understand the role of directives in the inversion, please visit this online resource. Here, we apply common directives for weighted least-squares inversion of gravity data and describe their roles. These are:\n",
    "\n",
    "- [UpdatePreconditioner](xref:simpeg#simpeg.directives.UpdatePreconditioner): Apply Jacobi preconditioner when solving optimization problem to reduce the number of conjugate gradient iterations. We set `update_every_iteration=True` because the ideal preconditioner is model-dependent.\n",
    "\n",
    "- [BetaEstimate_ByEig](xref:simpeg#simpeg.directives.BetaEstimate_ByEig): Compute and set starting trade-off parameter (beta) based on largest eigenvalues.\n",
    "\n",
    "- [BetaSchedule](xref:simpeg#simpeg.directives.BetaSchedule): Size reduction of the trade-off parameter at every beta iteration, and the number of Gauss-Newton iterations for each beta. In general, a `coolingFactor` between 1.5 and 2.5, and `coolingRate` of 3 works well for FDEM inversion. Cooling beta too quickly will result in portions of the model getting trapped in local minima. And we will not be finding the solution that minimizes the optimization problem if the cooling rate is too small.\n",
    "\n",
    "- [TargetMisfit](xref:simpeg#simpeg.directives.TargetMisfit): Terminates the inversion when the data misfit equals the target misfit. A `chifact=1` terminates the inversion when the data misfit equals the number of data.\n",
    "\n",
    "\n",
    "The directive objects are organized in a ``list``. Upon starting the inversion or updating the recovered model at each iteration, the inversion will call each directive within the list **in order**. The order of the directives matters, and SimPEG will throw an error if directives are organized into an improper order. Some directives, like the ``BetaEstimate_ByEig`` are only used when starting the inversion. Other directives, like ``UpdatePreconditionner``, are used whenever the model is updated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:46.279452Z",
     "iopub.status.busy": "2024-11-20T02:13:46.279120Z",
     "iopub.status.idle": "2024-11-20T02:13:46.285426Z",
     "shell.execute_reply": "2024-11-20T02:13:46.284480Z"
    }
   },
   "outputs": [],
   "source": [
    "update_jacobi = directives.UpdatePreconditioner(update_every_iteration=True)\n",
    "starting_beta = directives.BetaEstimate_ByEig(beta0_ratio=5)\n",
    "beta_schedule = directives.BetaSchedule(coolingFactor=2.0, coolingRate=3)\n",
    "target_misfit = directives.TargetMisfit(chifact=1.0)\n",
    "\n",
    "directives_list_L2 = [update_jacobi, starting_beta, beta_schedule, target_misfit]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define and Run the Inversion\n",
    "\n",
    "We define the inversion using the [BaseInversion](xref:simpeg#simpeg.inversion.BaseInversion) class. The inversion class must be instantiated with an appropriate *inverse problem* object and *directives list*. The ``run`` method, along with a starting model, is respondible for running the inversion. The output is a 1D numpy.ndarray containing the recovered model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:46.289078Z",
     "iopub.status.busy": "2024-11-20T02:13:46.288728Z",
     "iopub.status.idle": "2024-11-20T02:13:47.794105Z",
     "shell.execute_reply": "2024-11-20T02:13:47.793399Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running inversion with SimPEG v0.23.0\n",
      "\n",
      "                        simpeg.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
      "                        ***Done using same Solver, and solver_opts as the Simulation1DLayered problem***\n",
      "                        \n",
      "model has any nan: 0\n",
      "============================ Inexact Gauss Newton ============================\n",
      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
      "-----------------------------------------------------------------------------\n",
      "x0 has any nan: 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0  1.43e+00  2.86e+02  0.00e+00  2.86e+02    5.81e+01      0              \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/t40array/ssoler/miniforge3/envs/simpeg-user-tutorials/lib/python3.10/site-packages/simpeg/simulation.py:197: DefaultSolverWarning: Using the default solver: Pardiso. \n",
      "\n",
      "If you would like to suppress this notification, add \n",
      "warnings.filterwarnings('ignore', simpeg.utils.solver_utils.DefaultSolverWarning)\n",
      " to your script.\n",
      "  return get_default_solver(warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1  1.43e+00  1.17e+02  3.60e+01  1.69e+02    2.11e+01      0              \n",
      "   2  1.43e+00  8.17e+01  5.10e+01  1.55e+02    9.07e+00      0   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   3  7.17e-01  7.10e+01  5.66e+01  1.12e+02    2.38e+01      0   Skip BFGS  \n",
      "   4  7.17e-01  3.14e+01  9.14e+01  9.69e+01    5.71e+00      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   5  7.17e-01  2.73e+01  9.51e+01  9.54e+01    2.75e+00      0   Skip BFGS  \n",
      "   6  3.59e-01  2.59e+01  9.65e+01  6.05e+01    1.47e+01      0   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   7  3.59e-01  1.27e+01  1.21e+02  5.60e+01    1.78e+00      0              \n",
      "   8  3.59e-01  1.21e+01  1.22e+02  5.58e+01    7.11e-01      0   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   9  1.79e-01  1.20e+01  1.22e+02  3.39e+01    8.04e+00      0   Skip BFGS  \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 2.8679e+01\n",
      "1 : |xc-x_last| = 4.9329e-01 <= tolX*(1+|x0|) = 1.5563e+00\n",
      "0 : |proj(x-g)-x|    = 8.0394e+00 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 8.0394e+00 <= 1e3*eps       = 1.0000e-02\n",
      "0 : maxIter   =     100    <= iter          =     10\n",
      "------------------------- DONE! -------------------------\n"
     ]
    }
   ],
   "source": [
    "# Here we combine the inverse problem and the set of directives\n",
    "inv_L2 = inversion.BaseInversion(inv_prob_L2, directives_list_L2)\n",
    "\n",
    "# Run the inversion\n",
    "recovered_model_L2 = inv_L2.run(starting_conductivity_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inversion Outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Misfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:47.798013Z",
     "iopub.status.busy": "2024-11-20T02:13:47.797678Z",
     "iopub.status.idle": "2024-11-20T02:13:48.511405Z",
     "shell.execute_reply": "2024-11-20T02:13:48.510743Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dpred_L2 = simulation_L2.dpred(recovered_model_L2)\n",
    "\n",
    "fig = plt.figure(figsize=(10, 5))\n",
    "ax = [fig.add_axes([0.1 + ii * 0.5, 0.1, 0.37, 0.85]) for ii in range(2)]\n",
    "for ii in range(2):\n",
    "    ax[ii].loglog(frequencies, np.abs(dobs[ii::2]), \"k-o\", lw=2)\n",
    "    ax[ii].loglog(frequencies, np.abs(dpred_L2[ii::2]), \"b-o\", lw=2)\n",
    "    ax[ii].grid(which=\"both\")\n",
    "    ax[ii].set_xlabel(\"Frequency (Hz)\")\n",
    "    ax[ii].set_ylabel(\"|Hs/Hp| (ppm)\")\n",
    "    ax[ii].legend([\"Observed\", \"L2 Inversion\"])\n",
    "    if ii == 1:\n",
    "        ax[ii].set_ylabel(\"\")\n",
    "\n",
    "ax[0].set_title(\"Real Component\")\n",
    "ax[1].set_title(\"Imaginary Component\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recovered Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:48.515163Z",
     "iopub.status.busy": "2024-11-20T02:13:48.514893Z",
     "iopub.status.idle": "2024-11-20T02:13:48.518783Z",
     "shell.execute_reply": "2024-11-20T02:13:48.518067Z"
    }
   },
   "outputs": [],
   "source": [
    "# true conductivities and layer thicknesses\n",
    "true_conductivities = np.array([0.1, 1.0, 0.1])\n",
    "true_layers = np.r_[20.0, 40.0, 160.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:48.522032Z",
     "iopub.status.busy": "2024-11-20T02:13:48.521722Z",
     "iopub.status.idle": "2024-11-20T02:13:48.756344Z",
     "shell.execute_reply": "2024-11-20T02:13:48.755700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot true model and recovered model\n",
    "fig = plt.figure(figsize=(6, 6))\n",
    "\n",
    "ax1 = fig.add_axes([0.2, 0.15, 0.7, 0.7])\n",
    "plot_1d_layer_model(true_layers, true_conductivities, ax=ax1, color=\"k\")\n",
    "plot_1d_layer_model(\n",
    "    layer_thicknesses, log_conductivity_map * recovered_model_L2, ax=ax1, color=\"b\"\n",
    ")\n",
    "ax1.grid()\n",
    "ax1.set_xlabel(r\"Resistivity ($\\Omega m$)\")\n",
    "x_min, x_max = true_conductivities.min(), true_conductivities.max()\n",
    "ax1.set_xlim(0.9 * x_min, 1.5 * x_max)\n",
    "ax1.legend([\"True Model\", \"L2-Model\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iteratively Re-weighted Least-Squares Inversion\n",
    "\n",
    "Here, we use the iteratively reweighted least-squares (IRLS) inversion approach to recover sparse and/or blocky models on the set layers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the Forward Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:48.759957Z",
     "iopub.status.busy": "2024-11-20T02:13:48.759679Z",
     "iopub.status.idle": "2024-11-20T02:13:48.763782Z",
     "shell.execute_reply": "2024-11-20T02:13:48.763152Z"
    }
   },
   "outputs": [],
   "source": [
    "simulation_irls = fdem.simulation_1d.Simulation1DLayered(\n",
    "    survey=survey,\n",
    "    sigmaMap=log_conductivity_map,\n",
    "    thicknesses=layer_thicknesses,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the Data Misfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:48.767242Z",
     "iopub.status.busy": "2024-11-20T02:13:48.766976Z",
     "iopub.status.idle": "2024-11-20T02:13:48.770901Z",
     "shell.execute_reply": "2024-11-20T02:13:48.770196Z"
    }
   },
   "outputs": [],
   "source": [
    "dmis_irls = data_misfit.L2DataMisfit(simulation=simulation_irls, data=data_object)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regularization\n",
    "\n",
    "Here, we use the [Sparse](xref:simpeg#simpeg.regularization.Sparse) regularization class to constrain the inversion result using an IRLS approach. Here, the scaling constants that balance the smallness and smoothness terms are set directly. Equal emphasis on smallness and smoothness is generally applied by using the inverse square of the smallest cell dimension. The reference model is only applied to the smallness term; which is redundant for the tutorial example since we have set the reference model to an array of zeros. Here, we apply a 1-norm to the smallness term and a 1-norm to first-order smoothness along the x (vertical direction)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:48.773513Z",
     "iopub.status.busy": "2024-11-20T02:13:48.773224Z",
     "iopub.status.idle": "2024-11-20T02:13:48.780192Z",
     "shell.execute_reply": "2024-11-20T02:13:48.779469Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_irls = regularization.Sparse(\n",
    "    regularization_mesh,\n",
    "    alpha_s=0.01,\n",
    "    alpha_x=1,\n",
    "    reference_model_in_smooth=False,\n",
    "    norms=[1.0, 1.0],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:48.783231Z",
     "iopub.status.busy": "2024-11-20T02:13:48.782868Z",
     "iopub.status.idle": "2024-11-20T02:13:48.786894Z",
     "shell.execute_reply": "2024-11-20T02:13:48.786166Z"
    }
   },
   "outputs": [],
   "source": [
    "opt_irls = optimization.InexactGaussNewton(\n",
    "    maxIter=100, maxIterLS=20, maxIterCG=30, tolCG=1e-3\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inverse Problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:48.790027Z",
     "iopub.status.busy": "2024-11-20T02:13:48.789657Z",
     "iopub.status.idle": "2024-11-20T02:13:48.793600Z",
     "shell.execute_reply": "2024-11-20T02:13:48.792853Z"
    }
   },
   "outputs": [],
   "source": [
    "inv_prob_irls = inverse_problem.BaseInvProblem(dmis_irls, reg_irls, opt_irls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Directives\n",
    "\n",
    "Here, we create common directives for IRLS inversion of total magnetic intensity data and describe their roles. In additon to the [UpdateSensitivityWeights](xref:simpeg#simpeg.directives.UpdateSensitivityWeights), [UpdatePreconditioner](xref:simpeg#simpeg.directives.UpdatePreconditioner) and [BetaEstimate_ByEig](xref:simpeg#simpeg.directives.BetaEstimate_ByEig)  (described before), inversion with sparse-norms requires the [`UpdateIRLS`](xref:simpeg#simpeg.directives.UpdateIRLS) directive.\n",
    "\n",
    "You will notice that we don't use the [BetaSchedule](xref:simpeg#simpeg.directives.BetaSchedule) and [TargetMisfit](xref:simpeg#simpeg.directives.TargetMisfit) directives. Here, the beta cooling schedule is set in the [`UpdateIRLS`](xref:simpeg#simpeg.directives.UpdateIRLS) directive using the `coolingFactor` and `coolingRate` properties. The target misfit for the L2 portion of the IRLS approach is set with the `chifact_start` property. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:48.796873Z",
     "iopub.status.busy": "2024-11-20T02:13:48.796560Z",
     "iopub.status.idle": "2024-11-20T02:13:48.802425Z",
     "shell.execute_reply": "2024-11-20T02:13:48.801560Z"
    }
   },
   "outputs": [],
   "source": [
    "starting_beta_irls = directives.BetaEstimate_ByEig(beta0_ratio=5)\n",
    "update_jacobi_irls = directives.UpdatePreconditioner(update_every_iteration=True)\n",
    "update_irls = directives.UpdateIRLS(\n",
    "    cooling_factor=2,\n",
    "    cooling_rate=3,\n",
    "    f_min_change=1e-4,\n",
    "    max_irls_iterations=30,\n",
    "    chifact_start=1.0,\n",
    ")\n",
    "\n",
    "directives_list_irls = [update_irls, starting_beta_irls, update_jacobi_irls]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define and Run the Inversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:48.806325Z",
     "iopub.status.busy": "2024-11-20T02:13:48.805980Z",
     "iopub.status.idle": "2024-11-20T02:13:55.319032Z",
     "shell.execute_reply": "2024-11-20T02:13:55.318327Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running inversion with SimPEG v0.23.0\n",
      "simpeg.InvProblem will set Regularization.reference_model to m0.\n",
      "simpeg.InvProblem will set Regularization.reference_model to m0.\n",
      "\n",
      "                        simpeg.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
      "                        ***Done using same Solver, and solver_opts as the Simulation1DLayered problem***\n",
      "                        \n",
      "model has any nan: 0\n",
      "============================ Inexact Gauss Newton ============================\n",
      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
      "-----------------------------------------------------------------------------\n",
      "x0 has any nan: 0\n",
      "   0  3.11e+01  2.86e+02  0.00e+00  2.86e+02    5.81e+01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1  3.11e+01  2.94e+01  1.68e+00  8.18e+01    2.24e+01      0              \n",
      "   2  3.11e+01  1.72e+01  1.42e+00  6.14e+01    5.54e+00      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   3  1.56e+01  1.40e+01  1.50e+00  3.73e+01    8.92e+00      0   Skip BFGS  \n",
      "Reached starting chifact with l2-norm regularization: Start IRLS steps...\n",
      "irls_threshold 2.5194963943229616\n",
      "   4  1.56e+01  7.99e+00  1.94e+00  3.82e+01    1.55e+00      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   5  1.56e+01  8.44e+00  1.89e+00  3.78e+01    6.28e-01      0              \n",
      "   6  1.97e+01  8.26e+00  1.90e+00  4.57e+01    2.59e+00      0   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   7  1.97e+01  9.69e+00  1.88e+00  4.68e+01    5.89e-01      0              \n",
      "   8  1.97e+01  1.00e+01  1.86e+00  4.68e+01    1.82e-01      0   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   9  2.50e+01  9.96e+00  1.87e+00  5.67e+01    3.39e+00      0   Skip BFGS  \n",
      "  10  2.50e+01  1.21e+01  1.81e+00  5.74e+01    5.89e-01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  11  2.50e+01  1.25e+01  1.79e+00  5.74e+01    1.18e-01      0   Skip BFGS  \n",
      "  12  1.54e+01  1.24e+01  1.80e+00  4.02e+01    6.15e+00      0   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  13  1.54e+01  8.64e+00  2.04e+00  4.01e+01    7.07e-01      0              \n",
      "  14  1.54e+01  8.75e+00  2.03e+00  4.01e+01    2.39e-01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  15  1.91e+01  8.69e+00  2.03e+00  4.74e+01    2.46e+00      0   Skip BFGS  \n",
      "  16  1.91e+01  1.00e+01  2.01e+00  4.84e+01    6.74e-01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  17  1.91e+01  1.03e+01  1.99e+00  4.83e+01    1.71e-01      0              \n",
      "  18  2.36e+01  1.03e+01  1.99e+00  5.72e+01    3.08e+00      0   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  19  2.36e+01  1.22e+01  1.95e+00  5.80e+01    7.71e-01      0              \n",
      "  20  2.36e+01  1.26e+01  1.93e+00  5.80e+01    1.59e-01      0   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  21  1.45e+01  1.25e+01  1.93e+00  4.05e+01    6.30e+00      0   Skip BFGS  \n",
      "  22  1.45e+01  8.84e+00  2.17e+00  4.02e+01    7.78e-01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  23  1.45e+01  8.91e+00  2.16e+00  4.02e+01    1.92e-01      0              \n",
      "  24  1.77e+01  8.87e+00  2.16e+00  4.71e+01    2.34e+00      0   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  25  1.77e+01  1.01e+01  2.14e+00  4.79e+01    7.81e-01      0              \n",
      "  26  1.77e+01  1.04e+01  2.12e+00  4.79e+01    1.69e-01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  27  2.16e+01  1.04e+01  2.12e+00  5.62e+01    2.88e+00      0   Skip BFGS  \n",
      "  28  2.16e+01  1.20e+01  2.08e+00  5.69e+01    8.85e-01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  29  2.16e+01  1.24e+01  2.06e+00  5.68e+01    1.61e-01      0              \n",
      "  30  1.34e+01  1.23e+01  2.06e+00  3.98e+01    6.14e+00      0   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  31  1.34e+01  8.99e+00  2.28e+00  3.94e+01    7.83e-01      0              \n",
      "  32  1.34e+01  9.00e+00  2.27e+00  3.94e+01    1.23e-01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  33  1.62e+01  8.97e+00  2.28e+00  4.59e+01    2.23e+00      0   Skip BFGS  \n",
      "  34  1.62e+01  1.00e+01  2.25e+00  4.65e+01    7.95e-01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  35  1.62e+01  1.03e+01  2.23e+00  4.64e+01    1.27e-01      0              \n",
      "  36  1.97e+01  1.03e+01  2.23e+00  5.42e+01    2.71e+00      0   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  37  1.97e+01  1.17e+01  2.18e+00  5.47e+01    9.16e-01      0              \n",
      "  38  1.97e+01  1.20e+01  2.17e+00  5.47e+01    1.29e-01      0   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  39  1.23e+01  1.20e+01  2.17e+00  3.87e+01    5.80e+00      0   Skip BFGS  \n",
      "  40  1.23e+01  9.06e+00  2.37e+00  3.82e+01    7.66e-01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  41  1.23e+01  9.01e+00  2.37e+00  3.82e+01    7.90e-02      0              \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 2.0199e-02 <= tolF*(1+|f0|) = 2.8679e+01\n",
      "1 : |xc-x_last| = 7.9955e-02 <= tolX*(1+|x0|) = 1.5563e+00\n",
      "1 : |proj(x-g)-x|    = 7.8972e-02 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 7.8972e-02 <= 1e3*eps       = 1.0000e-02\n",
      "0 : maxIter   =     100    <= iter          =     41\n",
      "------------------------- DONE! -------------------------\n"
     ]
    }
   ],
   "source": [
    "# Here we combine the inverse problem and the set of directives\n",
    "inv_irls = inversion.BaseInversion(inv_prob_irls, directives_list_irls)\n",
    "\n",
    "# Run the inversion\n",
    "recovered_model_irls = inv_irls.run(starting_conductivity_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Misfit and Recovered Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:55.322993Z",
     "iopub.status.busy": "2024-11-20T02:13:55.322739Z",
     "iopub.status.idle": "2024-11-20T02:13:56.045920Z",
     "shell.execute_reply": "2024-11-20T02:13:56.045163Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dpred_irls = simulation_irls.dpred(recovered_model_irls)\n",
    "\n",
    "fig = plt.figure(figsize=(10, 5))\n",
    "ax = [fig.add_axes([0.1 + ii * 0.5, 0.1, 0.37, 0.85]) for ii in range(2)]\n",
    "for ii in range(2):\n",
    "    ax[ii].loglog(frequencies, np.abs(dobs[ii::2]), \"k-o\", lw=2)\n",
    "    ax[ii].loglog(frequencies, np.abs(dpred_L2[ii::2]), \"b-o\", lw=2)\n",
    "    ax[ii].loglog(frequencies, np.abs(dpred_irls[ii::2]), \"r-o\", lw=2)\n",
    "    ax[ii].grid(which=\"both\")\n",
    "    ax[ii].set_xlabel(\"Frequency (Hz)\")\n",
    "    ax[ii].set_ylabel(\"|Hs/Hp| (ppm)\")\n",
    "    ax[ii].legend([\"True Sounding\", \"Predicted (L2-model)\", \"Predicted (IRLS)\"])\n",
    "    if ii == 1:\n",
    "        ax[ii].set_ylabel(\"\")\n",
    "\n",
    "ax[0].set_title(\"Real Component\")\n",
    "ax[1].set_title(\"Imaginary Component\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:56.050460Z",
     "iopub.status.busy": "2024-11-20T02:13:56.050191Z",
     "iopub.status.idle": "2024-11-20T02:13:56.294802Z",
     "shell.execute_reply": "2024-11-20T02:13:56.294175Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot true model and recovered model\n",
    "fig = plt.figure(figsize=(6, 6))\n",
    "\n",
    "ax1 = fig.add_axes([0.2, 0.15, 0.7, 0.7])\n",
    "plot_1d_layer_model(true_layers, true_conductivities, ax=ax1, color=\"k\")\n",
    "plot_1d_layer_model(\n",
    "    layer_thicknesses, log_conductivity_map * recovered_model_L2, ax=ax1, color=\"b\"\n",
    ")\n",
    "plot_1d_layer_model(\n",
    "    layer_thicknesses, log_conductivity_map * recovered_model_irls, ax=ax1, color=\"r\"\n",
    ")\n",
    "ax1.grid()\n",
    "ax1.set_xlabel(r\"Conductivity ($S/m$)\")\n",
    "x_min, x_max = true_conductivities.min(), true_conductivities.max()\n",
    "ax1.set_xlim(0.8 * x_min, 2 * x_max)\n",
    "ax1.legend([\"True Model\", \"L2 Model\", \"IRLS Model\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parametric Inversion\n",
    "\n",
    "Here, we assume the subsurface is defined by a 3-layered Earth. However, the electrical properties and thicknesses of the layers are unknown. Here, we define our model to include log-conductivities and log-thicknesses. When including quantities that span different scales, it is frequently best to define the model in terms of log values so that each quantity influences the predicted data evenly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model and Mapping\n",
    "\n",
    "For a 3-layered Earth model, the model consists of 2 log-thicknesses and 3 log-conductivities. Similar to the [1D Forward Simulation of Frequency Domain EM Data for a Single Sounding](fwd_fdem_1d.ipynb) tutorial, need a mapping that extract log-thicknesses and log-resistivities from the model, and mappings that convert log-values to property values. For this, we require the [simpeg.maps.Wires](xref:simpeg#simpeg.maps.Wires) mapping and [simpeg.maps.ExpMap](xref:simpeg#simpeg.maps.ExpMap) mapping classes. Note that successive mappings can be chained together using the $*$ operator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:56.298665Z",
     "iopub.status.busy": "2024-11-20T02:13:56.298392Z",
     "iopub.status.idle": "2024-11-20T02:13:56.303814Z",
     "shell.execute_reply": "2024-11-20T02:13:56.303229Z"
    }
   },
   "outputs": [],
   "source": [
    "# Wire maps to extract log-thicknesses and log-conductivities\n",
    "wire_map = maps.Wires((\"log_thicknesses\", 2), (\"log_resistivity\", 3))\n",
    "\n",
    "# Maping for layer thicknesses\n",
    "log_thicknesses_map = maps.ExpMap() * wire_map.log_thicknesses\n",
    "\n",
    "# Mapping for conductivities\n",
    "log_resistivity_map = maps.ExpMap() * wire_map.log_resistivity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Starting and Reference Model\n",
    "\n",
    "This problem is highly non-linear so it is important to have a reasonable estimate of the true model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:56.307143Z",
     "iopub.status.busy": "2024-11-20T02:13:56.306914Z",
     "iopub.status.idle": "2024-11-20T02:13:56.310770Z",
     "shell.execute_reply": "2024-11-20T02:13:56.310093Z"
    }
   },
   "outputs": [],
   "source": [
    "starting_parametric_model = np.log(np.r_[30.0, 20.0, 20, 0.5, 5])\n",
    "\n",
    "reference_parametric_model = starting_parametric_model.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forward Simulation\n",
    "\n",
    "Because the layer thicknesses are part of the model, we define the ``thicknessesMap``. Because we are working in terms of electrical resistivity, we must define the ``rhoMap``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:56.313474Z",
     "iopub.status.busy": "2024-11-20T02:13:56.313118Z",
     "iopub.status.idle": "2024-11-20T02:13:56.318130Z",
     "shell.execute_reply": "2024-11-20T02:13:56.317427Z"
    }
   },
   "outputs": [],
   "source": [
    "simulation_parametric = fdem.simulation_1d.Simulation1DLayered(\n",
    "    survey=survey,\n",
    "    rhoMap=log_resistivity_map,\n",
    "    thicknessesMap=log_thicknesses_map,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Misfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:56.321056Z",
     "iopub.status.busy": "2024-11-20T02:13:56.320771Z",
     "iopub.status.idle": "2024-11-20T02:13:56.324923Z",
     "shell.execute_reply": "2024-11-20T02:13:56.324188Z"
    }
   },
   "outputs": [],
   "source": [
    "dmis_parametric = data_misfit.L2DataMisfit(\n",
    "    simulation=simulation_parametric, data=data_object\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (Combo) Regularization\n",
    "\n",
    "We need to define a regularization for each model parameter type. In this case, we have log-thicknesses and log-conductivities. For each model parameter type, we create a 1D [tensor mesh](xref:discretize#discretize.TensorMesh) with length equal to the number of parameters. In the ``mapping`` keyword argument, we used the wire map that extracts the specific model parameters from the model.\n",
    "\n",
    "Using the $*$ operator, separate regularizations can be summed to form a regularization that is also a [ComboObjectiveFunction](xref:simpeg#simpeg.objective_function.ComboObjectiveFunction). By setting the ``multipliers`` property, we can emphasize the relative contributions of the log-thicknesses and log-conductivities regularizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:56.327962Z",
     "iopub.status.busy": "2024-11-20T02:13:56.327649Z",
     "iopub.status.idle": "2024-11-20T02:13:56.334002Z",
     "shell.execute_reply": "2024-11-20T02:13:56.333271Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_1 = regularization.Smallness(\n",
    "    TensorMesh([(np.ones(2))], \"0\"),\n",
    "    mapping=wire_map.log_thicknesses,\n",
    "    reference_model=reference_parametric_model,\n",
    ")\n",
    "\n",
    "reg_2 = regularization.Smallness(\n",
    "    TensorMesh([(np.ones(3))], \"0\"),\n",
    "    mapping=wire_map.log_resistivity,\n",
    "    reference_model=reference_parametric_model,\n",
    ")\n",
    "\n",
    "reg_parametric = reg_1 + reg_2\n",
    "reg_parametric.multipliers = [1.0, 0.1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:56.337446Z",
     "iopub.status.busy": "2024-11-20T02:13:56.337123Z",
     "iopub.status.idle": "2024-11-20T02:13:56.341846Z",
     "shell.execute_reply": "2024-11-20T02:13:56.340966Z"
    }
   },
   "outputs": [],
   "source": [
    "opt_parametric = optimization.InexactGaussNewton(\n",
    "    maxIter=100, maxIterLS=20, maxIterCG=20, tolCG=1e-3\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inverse Problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:56.345442Z",
     "iopub.status.busy": "2024-11-20T02:13:56.345100Z",
     "iopub.status.idle": "2024-11-20T02:13:56.349893Z",
     "shell.execute_reply": "2024-11-20T02:13:56.348877Z"
    }
   },
   "outputs": [],
   "source": [
    "inv_prob_parametric = inverse_problem.BaseInvProblem(\n",
    "    dmis_parametric, reg_parametric, opt_parametric\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Directives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:56.353001Z",
     "iopub.status.busy": "2024-11-20T02:13:56.352663Z",
     "iopub.status.idle": "2024-11-20T02:13:56.358687Z",
     "shell.execute_reply": "2024-11-20T02:13:56.357967Z"
    }
   },
   "outputs": [],
   "source": [
    "update_jacobi = directives.UpdatePreconditioner(update_every_iteration=True)\n",
    "starting_beta = directives.BetaEstimate_ByEig(beta0_ratio=5)\n",
    "beta_schedule = directives.BetaSchedule(coolingFactor=2.0, coolingRate=3)\n",
    "target_misfit = directives.TargetMisfit(chifact=1.0)\n",
    "\n",
    "directives_list_parametric = [\n",
    "    update_jacobi,\n",
    "    starting_beta,\n",
    "    beta_schedule,\n",
    "    target_misfit,\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define and Run Inversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:56.362047Z",
     "iopub.status.busy": "2024-11-20T02:13:56.361636Z",
     "iopub.status.idle": "2024-11-20T02:13:58.514634Z",
     "shell.execute_reply": "2024-11-20T02:13:58.513802Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running inversion with SimPEG v0.23.0\n",
      "\n",
      "                        simpeg.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
      "                        ***Done using same Solver, and solver_opts as the Simulation1DLayered problem***\n",
      "                        \n",
      "model has any nan: 0\n",
      "============================ Inexact Gauss Newton ============================\n",
      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
      "-----------------------------------------------------------------------------\n",
      "x0 has any nan: 0\n",
      "   0  3.18e+03  2.87e+02  0.00e+00  2.87e+02    6.58e+02      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1  3.18e+03  1.07e+02  1.82e-02  1.65e+02    7.94e+01      0              \n",
      "   2  3.18e+03  9.89e+01  2.04e-02  1.64e+02    1.48e+01      0   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   3  1.59e+03  9.77e+01  2.07e-02  1.31e+02    2.04e+02      0   Skip BFGS  \n",
      "   4  1.59e+03  6.06e+01  3.68e-02  1.19e+02    1.41e+01      0              \n",
      "   5  1.59e+03  5.90e+01  3.78e-02  1.19e+02    3.10e+00      0   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   6  7.96e+02  5.88e+01  3.79e-02  8.90e+01    1.57e+02      0   Skip BFGS  \n",
      "   7  7.96e+02  3.59e+01  5.78e-02  8.19e+01    6.20e+00      0              \n",
      "   8  7.96e+02  3.52e+01  5.87e-02  8.19e+01    3.78e-01      0   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   9  3.98e+02  3.51e+01  5.87e-02  5.85e+01    1.15e+02      0   Skip BFGS  \n",
      "  10  3.98e+02  2.14e+01  8.26e-02  5.43e+01    8.19e+00      0              \n",
      "  11  3.98e+02  2.10e+01  8.36e-02  5.42e+01    1.18e+00      0   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  12  1.99e+02  2.09e+01  8.38e-02  3.76e+01    7.98e+01      0   Skip BFGS  \n",
      "  13  1.99e+02  1.32e+01  1.10e-01  3.52e+01    7.78e+00      0              \n",
      "  14  1.99e+02  1.30e+01  1.11e-01  3.51e+01    1.00e+00      0   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  15  9.95e+01  1.30e+01  1.11e-01  2.41e+01    5.04e+01      0   Skip BFGS  \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 2.8794e+01\n",
      "1 : |xc-x_last| = 8.4799e-02 <= tolX*(1+|x0|) = 6.7086e-01\n",
      "0 : |proj(x-g)-x|    = 5.0407e+01 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 5.0407e+01 <= 1e3*eps       = 1.0000e-02\n",
      "0 : maxIter   =     100    <= iter          =     16\n",
      "------------------------- DONE! -------------------------\n"
     ]
    }
   ],
   "source": [
    "inv_parametric = inversion.BaseInversion(\n",
    "    inv_prob_parametric, directives_list_parametric\n",
    ")\n",
    "recovered_model_parametric = inv_parametric.run(starting_parametric_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Misfit and Recovered Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:58.518286Z",
     "iopub.status.busy": "2024-11-20T02:13:58.518006Z",
     "iopub.status.idle": "2024-11-20T02:13:59.270471Z",
     "shell.execute_reply": "2024-11-20T02:13:59.269676Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dpred_parametric = simulation_parametric.dpred(recovered_model_parametric)\n",
    "\n",
    "fig = plt.figure(figsize=(10, 5))\n",
    "ax = [fig.add_axes([0.1 + ii * 0.5, 0.1, 0.37, 0.85]) for ii in range(2)]\n",
    "for ii in range(2):\n",
    "    ax[ii].loglog(frequencies, np.abs(dobs[ii::2]), \"k-o\", lw=2)\n",
    "    ax[ii].loglog(frequencies, np.abs(dpred_L2[ii::2]), \"b-o\", lw=2)\n",
    "    ax[ii].loglog(frequencies, np.abs(dpred_irls[ii::2]), \"r-o\", lw=2)\n",
    "    ax[ii].loglog(frequencies, np.abs(dpred_parametric[ii::2]), \"g-o\", lw=2)\n",
    "    ax[ii].grid(which=\"both\")\n",
    "    ax[ii].set_xlabel(\"Frequency (Hz)\")\n",
    "    ax[ii].set_ylabel(\"|Hs/Hp| (ppm)\")\n",
    "    ax[ii].legend(\n",
    "        [\n",
    "            \"True Sounding\",\n",
    "            \"Predicted (L2-model)\",\n",
    "            \"Predicted (IRLS)\",\n",
    "            \"Predicted (Parametric)\",\n",
    "        ]\n",
    "    )\n",
    "    if ii == 1:\n",
    "        ax[ii].set_ylabel(\"\")\n",
    "\n",
    "ax[0].set_title(\"Real Component\")\n",
    "ax[1].set_title(\"Imaginary Component\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T02:13:59.273672Z",
     "iopub.status.busy": "2024-11-20T02:13:59.273406Z",
     "iopub.status.idle": "2024-11-20T02:13:59.531550Z",
     "shell.execute_reply": "2024-11-20T02:13:59.530878Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6, 6))\n",
    "\n",
    "ax1 = fig.add_axes([0.2, 0.15, 0.7, 0.7])\n",
    "plot_1d_layer_model(true_layers, true_conductivities, ax=ax1, color=\"k\")\n",
    "plot_1d_layer_model(\n",
    "    layer_thicknesses, log_conductivity_map * recovered_model_L2, ax=ax1, color=\"b\"\n",
    ")\n",
    "plot_1d_layer_model(\n",
    "    layer_thicknesses, log_conductivity_map * recovered_model_irls, ax=ax1, color=\"r\"\n",
    ")\n",
    "plot_1d_layer_model(\n",
    "    log_thicknesses_map * recovered_model_parametric,\n",
    "    1 / (log_resistivity_map * recovered_model_parametric),\n",
    "    ax=ax1,\n",
    "    color=\"g\",\n",
    ")\n",
    "ax1.grid()\n",
    "ax1.set_xlabel(r\"Resistivity ($\\Omega m$)\")\n",
    "x_min, x_max = true_conductivities.min(), true_conductivities.max()\n",
    "ax1.set_xlim(0.9 * x_min, 2 * x_max)\n",
    "ax1.set_ylim([np.sum(layer_thicknesses), 0])\n",
    "ax1.legend([\"True Model\", \"L2 Model\", \"IRLS Model\", \"Parametric Model\"])\n",
    "plt.show()"
   ]
  }
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