hypopy-tutorial-P_S_waves.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# hypopy tutorial, P and S wave data\n",
    "\n",
    "## Outline\n",
    "\n",
    "- [Create synthetic data](#Create-synthetic-data)\n",
    "- [Initial hypocenter locations](#Initial-hypocenter-locations)\n",
    "- [Velocity data points](#Velocity-data-points)\n",
    "- [Calibration shots](#Calibration-shots)\n",
    "- [Constant velocity inversion](#Constant-velocity-inversion)\n",
    "- [Joint hypocenter-velocity inversion parameters](#Joint-hypocenter-velocity-inversion-parameters)\n",
    "- [Joint hypocenter-velocity inversion](#Joint-hypocenter-velocity-inversion)\n",
    "- [Results](#Results)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "import hypo\n",
    "from ttcrpy.rgrid import Grid3d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create first synthetic data\n",
    "\n",
    "Define limits and build grid.  In this example, we will use units of kilometers for distances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "xmin = 0.090\n",
    "xmax = 0.211\n",
    "ymin = 0.080\n",
    "ymax = 0.211\n",
    "zmin = 0.0\n",
    "zmax = 0.101\n",
    "\n",
    "dx = 0.0075   # grid cell size, we use cubic cells here\n",
    "\n",
    "# coordinates of the nodes\n",
    "x = np.arange(xmin, xmax, dx)\n",
    "y = np.arange(ymin, ymax, dx)\n",
    "z = np.arange(zmin, zmax, dx)\n",
    "\n",
    "# coordinates of the center of the voxels\n",
    "xx = np.arange(xmin+dx/2, xmax, dx)\n",
    "yy = np.arange(ymin+dx/2, ymax, dx)\n",
    "zz = np.arange(zmin+dx/2, zmax, dx)\n",
    "\n",
    "nthreads = 4   # do calculations in parallel with 4 processes\n",
    "nthreads = 1   # there is currently an issue with relocating events in parallel\n",
    "\n",
    "g = Grid3d(x, y, z, nthreads, cell_slowness=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need some receivers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "rcv = np.array([[0.112, 0.115, 0.013],\n",
    "                [0.111, 0.116, 0.040],\n",
    "                [0.111, 0.113, 0.090],\n",
    "                [0.151, 0.117, 0.017],\n",
    "                [0.180, 0.115, 0.016],\n",
    "                [0.113, 0.145, 0.011],\n",
    "                [0.160, 0.150, 0.017],\n",
    "                [0.185, 0.149, 0.015],\n",
    "                [0.117, 0.184, 0.011],\n",
    "                [0.155, 0.192, 0.009],\n",
    "                [0.198, 0.198, 0.010],\n",
    "                [0.198, 0.196, 0.040],\n",
    "                [0.198, 0.193, 0.090]])\n",
    "ircv = np.arange(rcv.shape[0]).reshape(-1,1)   # vector of rcv indices\n",
    "nsta = rcv.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create 15 events randomly located in the grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "nev = 15\n",
    "src = np.vstack((np.arange(nev),                                            # event ID\n",
    "                 np.linspace(0., 50., nev) + np.random.randn(nev),          # origin time\n",
    "                 0.160 + 0.005*np.random.randn(nev),                           # x\n",
    "                 0.140 + 0.005*np.random.randn(nev),                           # y\n",
    "                 0.060 + 0.010*np.random.randn(nev))).T                        # z\n",
    "\n",
    "h_true = src.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,8))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "ax.scatter(rcv[:,0], rcv[:,1], rcv[:,2], c='b', marker='o')\n",
    "ax.scatter(src[:,2], src[:,3], src[:,4], c='r', marker='^')\n",
    "ax.invert_zaxis()\n",
    "ax.set_xlabel('X')\n",
    "ax.set_ylabel('Y')\n",
    "ax.set_zlabel('Z')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For simplicity, we will use a velocity model with a small vertical gradient and mean Vp = 4000 m/s, and a constant velocity model for Vs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QWHZ2tg4dOqSTJ08qOTlZra2tmjt3rm6++ebQNvyEHIgvl5Hj1j9cKPQvYOhzYg+zJXDNmDFDLS0tKikpkTFGK1eu7PdTysWLF2vu3LkyxqioqEjjx4+3owwAgzFMZ7CioX8Bw4TDepgtgSshIUHLly/v99jXf0o5depUTZ069az7O+1n4sBFx7iG7RqtaOhfwDDgwB7GiU8BRDRc12gBuDg4rYcRuACEc+D6BwCImQN7GIELQBiXXHI5bDoeAGLlxB5G4AIQzjhvOh4AYubAHkbgAhCR05oVAAyE03rYkA5caQk+/X3S2/EuA3C8AwPdwYHrH4Yb+hcQmwH3L8mRPWxIBy4AdnHe+gcAiJ3zepizqgHgCK7/Wf9gxWUxjh8/roKCAnV0dPR7fPv27SoqKpLX69Wrr75qx8sAcJFyYg9jhgtARFasf+jp6VFlZaVGjRoV9nh1dbU2bNigpKQklZaW6qabbuJyOAAs47QexgwXgHDGJVfAHfUWTW1trUpKSjRu3Lh+j3d0dCgzM1OjR49WYmKi8vLy1NraaterAXCxcWAPI3ABiCiW6fjOzk4VFhaGbo2NjaH9m5qalJ6ervz8/LDn9vl8SktLC91PSUmRz+e7IK8LwMXBaT2MrxQBhIvxHDbp6elqamqKOLZx40a5XC7t2rVL+/fvV3l5uerq6pSRkaHU1FR1dXWFtu3q6urXvABgUBzYw846w+X1etXW1hb2+Lx586K+AABDm8u4lBBwR72dy/r16/Xb3/5W9fX1uuqqq1RbWxta35Cdna1Dhw7p5MmT8vv9am1t1TXXXGPpa6CHARcvJ/awswauEydOqLy8XK+99lq/x7+e6AAMT0ZS0CREvQ1Uc3OzGhsbNWLECC1evFhz585VSUmJioqKNH78eEtfAz0MuHg5sYed9SvFb37zm/qXf/kXzZ8/X3v37lVlZaUSEhLkcjnrzK0A7OBS73k0o7Opr6+X1Pep8CtTp07V1KlTLTvG36KHARcz5/Wwc1bzjW98Q6+88ookafbs2ers7DyfOgEMMUZSr9xRb05HDwMuTk7sYWed4TLG9G3g8aiqqkobN25UaWnpBSsMQPwYJchvEuNdxqDQw4CLlxN72FkD1/z58/vdLyoq0sSJE/Xss8/aXROAODOSAkNgButc6GHAxcuJPeysgevv//7vwx77/ve/r5dfftnWggA4gbXrH+KBHgZczJzXwzgPF4AwQeNy3HQ8AMTKiT1sSAeu3qQe/X9XfxbvMoBhaSgsih/K6F+AvZzWw4Z04AJgDyOX49Y/AECsnNjDCFwAwhjJcesfACBWTuxhBC4AYWL9SbWz2hkA9HFiDyNwAYgolvUPBC4ATuW0HkbgAhAm1vUPIy5ALQAwUE7sYQQuAGGCcqnXRG9FSRegFgAYKCf2MAIXgIh6jbN+4QMAA+G0HkbgAhDGyOW4c9gAQKyc2MMIXADCGOPSmRim46Pp7e3V0qVLdfDgQbndblVXVyszMzM0vmnTJr300ktKS0vT3XffrZ/85CeDPiYAWNHDrO5f/MgIQJivPh1Gu0WzY8cOSVJDQ4Pmz5+v6urq0FhnZ6dWrVql+vp6/fa3v1Vzc7M++4wzrwMYPCt6mNX9ixkuABFZsf5h+vTpmjJliiTpyJEjGjt2bGjss88+05VXXqkxY8ZIkr73ve/pvffe07e+9a1BHxcABtvDrO5fBC4AYWJd/9DZ2anCwsLQfa/XK6/X228bj8ej8vJybd26VatXrw49PmHCBH388cf64osvlJKSol27dunyyy+37DUAuHhZ1cOs7F+2BK5gMKiqqiodOHBAiYmJWrFihSZMmBAa3759u9asWSOPx6OioiIVFxdLktatW6ft27erp6dHpaWlrOcA4sQotvUP6enpampqirpdbW2tFi5cqOLiYm3evFnJyckaPXq0lixZon/6p3/SN7/5TV199dW69NJLrSh/UOhfwNBnZQ+zqn/ZEri2bdsmv9+vxsZGtbW1qaamRnV1dZKknp4eVVdXa8OGDUpKSlJpaaluuukmffLJJ3r33Xf1u9/9TqdOndLLL79sR2kAYmAU21mao9m0aZOOHj2qBx98UElJSXK5XHK7+543EAjovffe0/r16xUIBHT//ffrscceG/QxB4v+BQx9VvQwq/uXLYFrz549ys/PlyRNnjxZ7e3tobGOjg5lZmZq9OjRkqS8vDy1trZq3759ysnJ0cMPPyyfz6cnnngi6nHMKKPT3+214yUAFzUjlyVruGbOnKklS5aorKxMgUBAFRUV2rJli7q7u+X1ejVixAgVFhZq5MiRuv/++5Wenm5B9YND/wKGPit6mNX9y5bA5fP5lJqaGrrvdrsVCATk8Xjk8/mUlpYWGktJSZHP59OJEyd05MgRPf/88/rss8/00EMP6Q9/+INcLpcdJQI4ByOXzij6hV+jSU5O1qpVq846/sgjj+iRRx4Z9HGsRP8Chj4repjV/cuWwJWamqqurq7Q/WAwKI/HE3Gsq6tLaWlpGjNmjLKyspSYmKisrCyNHDlSnZ2d+sY3vmFHiQDOoe/T4cV51hj6FzD0ObGH2VJNbm6udu7cKUlqa2tTTk5OaCw7O1uHDh3SyZMn5ff71draqmuuuUZ5eXn605/+JGOMjh49qlOnToV+bgngwrPiPFxDEf0LGB6c1sNsmeGaMWOGWlpaVFJSImOMVq5cqebm5tD3nosXL9bcuXNljFFRUZHGjx+v8ePH65133tE999wjY4wqKytDi9MAXFjGuBR02KfDC4X+BQx9TuxhtgSuhIQELV++vN9j2dnZoX9PnTpVU6dODdsvloWmAOxn5FLPRXqaPvoXMPQ5sYc5qxoAjuG0T4cAMBBO62EELgBh+s5h46xmBQCxcmIPI3ABiMClgKE9ABiqnNfDnFUNAEfouw6Zsz4dAkCsnNjDCFwAInLa+gcAGAin9TACF4AwRi71OGw6HgBi5cQe5qxqADiCkRR02HQ8AMTKiT2MwAUgjBPXPwBArJzYwwhcAMIZ561/AICYObCHDe3AleSRrubisIDVnLj+YdihfwG2cWIPc1Y1ABzDivUPvb29Wrp0qQ4ePCi3263q6mplZmaGxl9//XX9+te/VkJCgoqKijRr1qxBHxMApMH3MKv7F4ELQBir1j/s2LFDktTQ0KDdu3erurpadXV1ofF//ud/1htvvKHk5GTddtttuu222zR69OhBHxfAxc2KHmZ1/yJwAQhjJEvO0jx9+nRNmTJFknTkyBGNHTu23/h3vvMd/eUvf5HH45ExRi6Xa9DHBAArepjV/YvABSACl3pjWHDa2dmpwsLC0H2v1yuv19tvG4/Ho/Lycm3dulWrV6/uNzZx4kQVFRUpKSlJM2bM0CWXXGJN+QAuctb0MCv7F4ELQJhYz2GTnp6upqamqNvV1tZq4cKFKi4u1ubNm5WcnKwPP/xQb731lv74xz8qOTlZixYt0u9//3vdcsstFrwCABczK3uYVf2LwAUgjDHW/MJn06ZNOnr0qB588EElJSXJ5XLJ7XZLktLS0jRq1CiNHDlSbrdb6enp+vLLLwd9TACwoodZ3b8IXAAisuJXijNnztSSJUtUVlamQCCgiooKbdmyRd3d3aGp+1mzZmnEiBHKzMzU3XffbUHlADD4HmZ1/yJwAQhjYlz/EE1ycrJWrVp11vHS0lKVlpYO+jgA8HVW9DCr+xeBC0BETrsOGQAMhNN6GIELQBgnnqUZAGLlxB7mrGoAOEKsv/ABACdyYg8jcAGIwJo1XAAQH87rYQQuAGGMjHrVG+8yAOC8OLGHEbgARGQUjHcJAHDenNbDhnbgGpEmjZkW7yoA5xvwBz3juGY17NC/gNic10SV83rY0A5cAGzjtGYFAAPhtB5G4AIQpm/9QyDeZQDAeXFiDyNwAYjIaZ8OAWAgnNbDCFwAInDe+gcAiJ3zehiBC0CYvpMGOms6HgBi5cQeRuACEIFR0GGfDgEgds7rYQQuABE5bToeAAbCaT3MlsAVDAZVVVWlAwcOKDExUStWrNCECRNC49u3b9eaNWvk8XhUVFSk4uJi9fT0aPHixTp8+LASEhL01FNPKTs7247yAETRt/ph8NPxvb29Wrp0qQ4ePCi3263q6mplZmZKko4dO6bHH388tO3+/fu1YMEClZaWDvq4g0H/AoY+K3qY1f3LlsC1bds2+f1+NTY2qq2tTTU1Naqrq5Mk9fT0qLq6Whs2bFBSUpJKS0t100036b333lMgEFBDQ4NaWlr07LPP6rnnnrOjPAAxsOLT4Y4dOyRJDQ0N2r17t6qrq0O9ICMjQ/X19ZKkd999V//yL/+i4uLiQR9zsOhfwPAw2B5mdf+yJXDt2bNH+fn5kqTJkyervb09NNbR0aHMzEyNHj1akpSXl6fW1lbl5OSot7dXwWBQPp9PHg/fdgLxY836h+nTp2vKlCmSpCNHjmjs2LHhRzJGTz31lH7+85/L7XYP+piDRf8ChoPB9zCr+5ctXcHn8yk1NTV03+12KxAIyOPxyOfzKS0tLTSWkpIin8+n5ORkHT58WLfccotOnDih559/3o7SAMTAKLZPh52dnSosLAzd93q98nq9/bbxeDwqLy/X1q1btXr16rDn2L59uyZOnKisrKxB120F+hcw9FnVw6zsX7YErtTUVHV1dYXuB4PB0Ce+vx3r6upSWlqafvOb3+jGG2/UggUL9Pnnn+vee+9Vc3OzRo4caUeJAM4ptvUP6enpampqirpdbW2tFi5cqOLiYm3evFnJycmhsddff11z5swZVLVWon8Bw4F1Pcyq/pUQ01YDlJubq507d0qS2tralJOTExrLzs7WoUOHdPLkSfn9frW2tuqaa67RJZdcEvrkOHr0aAUCAfX2ntcVKwFYwCgY9RbNpk2btG7dOklSUlKSXC5X2LT73r17lZuba8trOB/0L2B4GGwPs7p/2TLDNWPGDLW0tKikpETGGK1cuVLNzc3q7u6W1+vV4sWLNXfuXBljVFRUpPHjx+u+++5TRUWFZs2apZ6eHj322GP9UiSAC8dYtIZr5syZWrJkicrKyhQIBFRRUaEtW7aEekFnZ6dSUlLkcrksqNoa9C9g6LOih1ndv1zGGDOoiuLo/Xf/Qxkpr8a7DMDxTvaW66qrrop5+42vvaUVS9+Lut3/3jRzQM+Lv6J/AbEZaP+SnNnDhvRPaVyeMRqV8Y/xLgNwvv8e+C5OO2ngcEP/AmJ0Hv1Lcl4PG9KBC4B9nNasAGAgnNbDCFwAwli1hgsA4sGJPYzABSACay7tAwDx4bweRuACECbWkwYCgBM5sYcRuABE5LRmBQAD4bQeRuACEEFQvcYf7yIA4Dw5r4cRuACEMcbIGGetfwCAWDmxhxG4AETktGYFAAPhtB5G4AIQQVC9vafjXQQAnCfn9TACF4AwTpyOB4BYObGHEbgAROC8ZgUAsXNeDyNwAYjIac0KAAbCaT2MwAUgjDHOW/8AALFyYg8jcAGIwJrp+N7eXi1dulQHDx6U2+1WdXW1MjMzQ+Pvv/++ampqZIxRRkaGnnnmGY0cOXLQxwVwsRt8D7O6fw3pwOXxXKpLL70+3mUAjvff/71/gHtYE7h27NghSWpoaNDu3btVXV2turq6viMYo2XLlmn16tWaMGGCXnvtNR0+fFhZWVmDPu5QQP8CYjPw/iVZ0cOs7l9DOnABsItRMDj46fjp06drypQpkqQjR45o7NixobGDBw9qzJgxeuWVV/TRRx+poKDgoglbAOw2+B5mdf8icAEIY4xRMBj902FnZ6cKCwtD971er7xeb79tPB6PysvLtXXrVq1evTr0+IkTJ/Tuu+9q2bJlmjBhgubNm6dJkybpuuuus+6FALgoWdXDrOxfBC4AEcQ2HZ+enq6mpqao29XW1mrhwoUqLi7W5s2blZycrDFjxmjChAn6u7/7O0lSfn6+2tvbCVwALGBdD7OqfyUM7AUAuDj0Natot2g2bdqkdevWSZKSkpLkcrnkdrslSd/+9rfV1dWlQ4cOSZJaW1s1ceJE+14SgIvI4HuY1f2LGS4AYfqm4we/hmvmzJlasmSJysrKFAgEVFFRoS1btqi7u1ter1dPP/20FixYIGOMrrnmmtB6CQAYDCt6mNX9i8AFIILY1j9Ek5ycrFWrVp11/LrrrtOGDRsGfRwA6G/wPczq/kXgAhCB8y6LAQCxc14PI3ABCGNM0JKvFAEgHpzYwwhcACJy2qdDABgIp/UwAheACKxZwwUA8eG8HkbgAhDGidPxABArJ/YwAheACJy34BQAYue8HkbgAhCR05oVAAyE03oYgQtABM5b/wAAsXNeDyNwAQjjxPUPABArJ/awIR24zpw5o/3798e7DMDxzpw5M6Dtf/SjH2nfvn1Rt/N4hnQLiSv6FxCbgfYvyZk9zGWMMRfsaAAAABehhHgXAAAAMNwRuAAAAGxG4AIAALAZgQsAAMBmBC4AAACbDavfdAeDQVVVVenAgQNKTEzUihUrNGHChH7bnDp1Svfff7+efvppZWdnn3WfQ4cOafHixXK5XJo4caJ+9rOfKSHBunx6PrX29PSooqJChw8flt/v10MPPaRp06Zp7969mjdvni6//HJJUmlpqW699da41SlJd911l9LS0iRJ3/rWt1RdXW3re3o+dTY1Nenf/u3fJP31J/otLS369NNPbXs/Y6n1jTfe0CuvvCK3262cnBxVVVVJUlz+TjF0OL3/ObnnOb3POb2/0dNiZIaRN99805SXlxtjjHn33XfNvHnz+o2///775u677zbXX3+9+fjjj8+5z4MPPmj+67/+yxhjzLJly8yWLVviXuuGDRvMihUrjDHGdHZ2moKCAmOMMa+++qp56aWXLK1vMHWePn3a3HnnnWHPZed7ej51fl1VVZVpaGgwxtj7fkar9dSpU2batGmmu7vbGGPMY489ZrZt2xa3v1MMHU7vf07ueU7vc07vb/S02AyT2Nhnz549ys/PlyRNnjxZ7e3t/cb9fr/WrFmjrKysqPvs3btXP/zhDyVJ//AP/6C333477rXefPPN+ulPfxq673a7JUnt7e166623VFZWpoqKCvl8vrjW+eGHH+rUqVN64IEHNGfOHLW1tUmy9z09nzq/8sEHH+jjjz+W1+uVZO/7Ga3WxMRENTQ0KCkpSZIUCAQ0cuTIuP2dYuhwev9zcs9zep9zen+jp8VmWAUun8+n1NTU0H23261A4K/XUsrLy9Nll10W0z7GGLlcLklSSkqK/vKXv8S91pSUFKWmpsrn82n+/Pl69NFHJUnf//739cQTT2j9+vX69re/rTVr1sS1zlGjRmnu3Ll66aWX9OSTT2rhwoW2v6fnU+dX1q1bp4cffjh03873M1qtCQkJGjt2rCSpvr5e3d3duuGGG+L2d4qhw+n9z8k9z+l9zun9jZ4Wm2EVuFJTU9XV1RW6HwwGo562/2z7fP07466uLl1yySVxr1WSPv/8c82ZM0d33nmnbr/9dknSjBkzNGnSpNC/Y7mcgZ11XnHFFbrjjjvkcrl0xRVXaMyYMTp27Jit7+n5vp9ffvmlPvnkE/3oRz8KPWbn+xlLrcFgULW1tWppadFzzz0nl8sVt79TDB1O739O7nlO73NO72/0tNgMq8CVm5urnTt3SpLa2tqUk5Nz3vt897vf1e7duyVJO3fu1LXXXhv3Wr/44gs98MADWrRoke65557Q43PnztX7778vSdq1a5euvvrquNa5YcMG1dTUSJKOHj0qn8+njIwMW9/T86lTkt555x1df/31/R6z8/2MpdbKykqdOXNGa9euDU3Dx+vvFEOH0/ufk3ue0/uc0/sbPS02w+pail/9UuKjjz6SMUYrV67Uvn371N3dHfr+WpJmz56tqqqqfr/S+fo+2dnZOnjwoJYtW6aenh5lZWVpxYoVofUD8ap1xYoV+v3vf9/ve/oXX3xRHR0deuqppzRixAiNHTtWTz31VL+p2gtdp9/v15IlS3TkyBG5XC4tXLhQubm5tr6n51OnJP3qV7+Sx+PRfffdF9pm7969tr2f0WqdNGmSioqKdO2114am1efMmaNp06bF5e8UQ4fT+5+Te57T+5zT+xs9LTbDKnABAAA40bD6ShEAAMCJCFwAAAA2I3ABAADYjMAFAABgMwIXAACAzQhcGLSWlhbdcccdOn36tKS+c9LcfvvtOnr0aJwrA4Do5s+frxdeeCF0v6urSz/+8Y/14YcfxrEqDDcELgzaDTfcoBtvvFE1NTXq6enRY489psWLF2v8+PHxLg0AoqqqqtLvfvc7ffzxx5Kk2tpaeb1eXXnllXGuDMMJ5+GCJXp6ejRr1iyNGTNGP/jBD/TII4/EuyQAiNn27dv1wgsv6PHHH1ddXZ1efvnl0Ik6ASswwwVLjBgxQsXFxXr77bdVWFgY73IAYECmTp2qK664QosXL1ZNTQ1hC5YjcMEShw8f1q9+9SstWrRIixYtUm9vb7xLAoABueuuu/SDH/yA5RCwBYELg+b3+/Xoo4+qoqJC9913ny677DL98pe/jHdZAAA4BoELg1ZbW6u8vDwVFBRI6luAunnz5tAV3wEAuNixaB4AAMBmzHABAADYjMAFAABgMwIXAACAzQhcAAAANiNwAQAA2IzABQAAYDMCFwAAgM0IXAAAADYjcAEAANiMwAUAAGAzAhcAAIDNPPEuYDDadu7QyM4v4l0G4HhnLp+oyZMnx7z9n//8ZwUCgajbeTweTZw4cRCVAcDFYUgHrpGdX+iq1UvjXQbgePvXbBrQ9oFAj668Mifqdh9++OfzrAgALi5DOnABsIcxRsZEn+ECAMSGNVwAIjCSAjHcojt+/LgKCgrU0dHR7/E33nhDP/nJT1RSUqLKykoFg0EL6wcAZyFwAYjAKBg8HfUWTU9PjyorKzVq1Kh+j58+fVrPPvus/vVf/1UNDQ3y+XzasWOHXS8GAOKOwAUggr6vFKPdoqmtrVVJSYnGjRvX7/HExEQ1NDQoKSlJkhQIBDRy5EhbXgkAOAGBC0AEsQWuzs5OFRYWhm6NjY2hZ2hqalJ6erry8/PDnj0hIUFjx46VJNXX16u7u1s33HDDBXt1AHChsWgeQARfreE6t/T0dDU1NUUc27hxo1wul3bt2qX9+/ervLxcdXV1ysjIkCQFg0E988wzOnjwoJ577jm5XC4rXwAAOAqBC0AYY0xMa7TOZf369aF/z549W1VVVaGwJUmVlZVKTEzU2rVrlZDAZDuA4c2WwBUMBlVVVaUDBw4oMTFRK1as0IQJE0Ljb7zxhl555RW53W7l5OSoqqpKks65D4ALKdbTQrgH9KzNzc3q7u7WpEmTtGHDBl177bW69957JUlz5szRjBkzzqNWAHA+WwLXtm3b5Pf71djYqLa2NtXU1Kiurk7SX3+d1NzcrKSkJD3++OPasWOHent7z7oPgAvN2sBVX18vScrOzg499uGHH55PYQAwJNkSuPbs2RNaKDt58mS1t7eHxs7266Q//elPZ90HwIUWlDE9MWzHLwsBIBa2LJzw+XxKTU0N3Xe73aHrsp3t10nn2gfAhWWMLDktBACgjy0zXKmpqerq6grdDwaD8ng8/e7/7a+Tou0D4ELi0j4AYCVbZrhyc3O1c+dOSVJbW5tycvpfBLeyslJnzpzR2rVrQ18tRtsHwIVkzYlPAQB9bJlCmjFjhlpaWlRSUiJjjFauXBnTr5P+dh8A8RKUMYM7LQQA4K9sCVwJCQlavnx5v8di+XXS3+4DIH6YwQIA67BICkAYY1jDBQBWInABiICvFAHASgQuABEwwwUAViJwAYiIwAUA1iFwAQhjTHDQF68GAPwVgQtABEYSM1wAYBUCF4AIWMMFAFYicAGIiMAFANYhcAGIICjDGi4AsAyBC0A4YyTTG+8qAGDYIHABiIzABQCWIXABiCAoBc/EuwgAGDYS4l0AAAcykoK90W8xOH78uAoKCtTR0RE2durUKZWUlEQcA4DhhMAFIIL/WcMV7RZFT0+PKisrNWrUqLCxDz74QGVlZfr000/teAEA4CgELgARxBa4Ojs7VVhYGLo1Njb2e5ba2lqVlJRo3LhxYUfw+/1as2aNsrKyLtSLAoC4YQ0XgAhMTGu40tPT1dTUFHGsqalJ6enpys/P1wsvvBA2npeXN+gqAWCoIHABCGdMzGu0zmbjxo1yuVzatWuX9u/fr/LyctXV1SkjI8OiIgFg6CBwAYhskKeFWL9+fejfs2fPVlVVFWELwEWLwAUgAiMF/ZY/a3Nzs7q7u+X1ei1/bgBwMgIXgHDGSLFcS9EV29PV19dLkrKzs886BgDDGYELQAQxruFy218JAAwHBC4AEcT4lSKBCwBiQuACEI6LVwOApWw58WkwGFRlZaW8Xq9mz56tQ4cOhW3zt5f06Onp0YIFC1RSUqJZs2ZxqQ8g3kwg+g0AEBNbAte2bdvk9/vV2NioBQsWqKampt94pEt6/Od//qcCgYAaGhr08MMP69lnn7WjNAAxMZZdSxEAYFPg2rNnj/Lz8yVJkydPVnt7e7/xSJf0uOKKK9Tb26tgMCifzyePh287gbgx/7OGK9oNABATW1KNz+dTampq6L7b7VYgEAiFqEiX9EhOTtbhw4d1yy236MSJE3r++eftKA1ATFjDBQBWsmWGKzU1VV1dXaH7wWAw6ozVb37zG914441688039e///u9avHixzpyJfi03AHYwrOECAAvZErhyc3O1c+dOSVJbW5tycnKi7nPJJZcoLS1NkjR69GgFAgH19vIJG4gLY6Ref/QbACAmtnylOGPGDLW0tKikpETGGK1cuTLqJT3uu+8+VVRUaNasWerp6dFjjz2m5ORkO8oDEBVfKQKAlWwJXAkJCVq+fHm/x6Jd0iMlJUWrVq2yoxwA54PABQCW4aeAAMKZoNTLGkoAsAqBC0BkzHABgGUIXAAiYA0XAFiJwAUgnJEUjHcRADB8ELgARBYgcQGAVQhcAMIZSUET7yoAYNiw5cSnAIY60xe4ot1icPz4cRUUFKijo6Pf49u3b1dRUZG8Xq9effVVO14EADgGM1wAwhlJgcHPcPX09KiyslKjRo0Ke7y6ulobNmxQUlKSSktLddNNNykjI2PQxwQAJ2KGC0BkFsxw1dbWqqSkROPGjev3eEdHhzIzMzV69GglJiYqLy9Pra2tdr0SAIg7AheAcF+t4Ypy6+zsVGFhYejW2NgYeoqmpialp6crPz8/7Ol9Pl/o2qlS35UmfD7fhXhlABAXfKUIILIYZrDS09PV1NQUcWzjxo1yuVzatWuX9u/fr/LyctXV1SkjI0Opqanq6uoKbdvV1dUvgAHAcEPgAhDOmEGv4Vq/fn3o37Nnz1ZVVVVojVZ2drYOHTqkkydPKjk5Wa2trZo7d+6gjgcATkbgAhCZDaeFaG5uVnd3t7xerxYvXqy5c+fKGKOioiKNHz/e8uMBgFMQuACEs/g8XPX19ZL6Zra+MnXqVE2dOtWyYwCAkxG4AISz6LQQAIA+BC4AkXGmeQCwDIELQASxn0keABAdgQtAOCMuXg0AFiJwAQhnJJG3AMAyBC4AkfGVIgBYhsAFIDICFwBYhsAFIJwxrOECAAsRuACEYw0XAFgqwY4nDQaDqqyslNfr1ezZs3Xo0KGwbU6dOqWSkhJ1dHSEHlu3bp28Xq8KCwv12muv2VEagFgFTfQbACAmtgSubdu2ye/3q7GxUQsWLFBNTU2/8Q8++EBlZWX69NNPQ4/t3r1b7777rn73u9+pvr5e//3f/21HaQBi8dVpIaLdAAAxsSVw7dmzR/n5+ZKkyZMnq729vd+43+/XmjVrlJWVFXrs//yf/6OcnBw9/PDDmjdvnqZMmWJHaQBiEsPsFjNcABAzW9Zw+Xw+paamhu673W4FAgF5PH2Hy8vLC9vnxIkTOnLkiJ5//nl99tlneuihh/SHP/xBLpfLjhIBnAtruADAUrYErtTUVHV1dYXuB4PBUNg6mzFjxigrK0uJiYnKysrSyJEj1dnZqW984xt2lAggGmawAMAytnylmJubq507d0qS2tralJOTE3WfvLw8/elPf5IxRkePHtWpU6c0ZswYO8oDEI1xyRVwR70BAGJjywzXjBkz1NLSopKSEhljtHLlSjU3N6u7u1terzfiPjfddJPeeecd3XPPPTLGqLKyUm43DR2IF1eQr/MBwCq2BK6EhAQtX76832PZ2dlh29XX1/e7/8QTT9hRDoCBMgQuALASJz4FEMZlXErgK0MAsAyBC0BkFsxw9fb2aunSpTp48KDcbreqq6uVmZkZGt+0aZNeeuklpaWl6e6779ZPfvKTQR8TAJzIlkXzAIY+V9AV9RbNjh07JEkNDQ2aP3++qqurQ2OdnZ1atWqV6uvr9dvf/lbNzc367LPPbHs9ABBPzHABCGdcSggM/vPY9OnTQycxPnLkiMaOHRsa++yzz3TllVeGfo38ve99T++9956+9a1vDfq4AOA0BC4AYVxGcgWjB67Ozk4VFhaG7nu93rBfIns8HpWXl2vr1q1avXp16PEJEybo448/1hdffKGUlBTt2rVLl19+uWWvAQCchMAFILIYvjJMT09XU1NT1O1qa2u1cOFCFRcXa/PmzUpOTtbo0aO1ZMkS/dM//ZO++c1v6uqrr9all15qReUA4Dis4QIQkRVruDZt2qR169ZJkpKSkuRyuULn1wsEAnrvvfe0fv161dbW6pNPPlFubq6trwkA4oUZLgDhLFrDNXPmTC1ZskRlZWUKBAKqqKjQli1bQidBHjFihAoLCzVy5Ejdf//9Sk9Pt6B4AHAeAheAcDGu4YomOTlZq1atOuv4I488okceeWTQxwEApyNwAYiMM80DgGUIXADCuCz6ShEA0IfABSAirqUIANYhcAEIZ9EaLgBAHwIXgAhcrOECAAsRuACEcRmxhgsALETgAhARa7gAwDoELgDhjIs1XABgobN21AMHDlzIOgA4jCuQEPUGAIjNWTvmT3/6U/3mN7+5gKUAcAxjzbUUAQB9zhq4mpqadPDgQc2dO1fHjh27kDUBiDOXCFwAYKWzruFKTk7Wk08+qXfeeUelpaX6wQ9+EBr7xS9+cUGKAxAfxrhkelniCQBWOWdH7ejo0C9+8Qv98Ic/1F133XWBSgIQfy71GtZoAYBVzhq4XnjhBTU0NKiyslJTpky5gCUBiDcjqVfueJcBAMPGWT/Ctre3a+PGjecVtoLBoCorK+X1ejV79mwdOnQobJtTp06ppKREHR0d/R4/fvy4CgoKwh4HcCG51GvcUW8AgNicNXCtXr1al1566Xk96bZt2+T3+9XY2KgFCxaopqam3/gHH3ygsrIyffrpp/0e7+npUWVlpUaNGnVexwVgjaBcOqPEqDcAQGxsWaSxZ88e5efnS5ImT56s9vb2fuN+v19r1qxRVlZWv8dra2tVUlKicePG2VEWgJj1reGKdgMAxMaWjunz+ZSamhq673a7FQgEQvfz8vJ02WWX9dunqalJ6enpoaAGIH6+WsMV7QYAiI0tv/tOTU1VV1dX6H4wGJTHc+5Dbdy4US6XS7t27dL+/ftVXl6uuro6ZWRk2FEigHMwxiW/GfxXhr29vVq6dKkOHjwot9ut6upqZWZmhsZff/11/frXv1ZCQoKKioo0a9asQR8TAJzIlsCVm5urHTt26NZbb1VbW5tycnKi7rN+/frQv2fPnq2qqirCFhAnRi4FLJjB2rFjhySpoaFBu3fvVnV1terq6kLj//zP/6w33nhDycnJuu2223Tbbbdp9OjRgz4uADiNLYFrxowZamlpUUlJiYwxWrlypZqbm9Xd3S2v12vHIQFYyEiWrNGaPn166JfOR44c0dixY/uNf+c739Ff/vIXeTweGWPkcnH2egDDky2BKyEhQcuXL+/3WHZ2dth29fX1Efc/2+MALgyjhJi+UjzZ2anCwsLQfa/XG/ahyuPxqLy8XFu3btXq1av7jU2cOFFFRUVKSkrSjBkzdMkll1jzAgDAYbh2B4CIYlkUn56erqampqjb1dbWauHChSouLtbmzZuVnJysDz/8UG+99Zb++Mc/Kjk5WYsWLdLvf/973XLLLVaUDwCOwu+6AYT5ag1XtFs0mzZt0rp16yRJSUlJcrlccrv79ktLS9OoUaM0cuRIud1upaen68svv7T1dQFAvDDDBSCMkRS0YA3XzJkztWTJEpWVlSkQCKiiokJbtmwJref0er2aNWuWRowYoczMTN19992DLx4AHIjABSCMkTWnhUhOTtaqVavOOl5aWqrS0tJBHwcAnI7ABSCMkYsTmwKAhQhcAMJYdR4uAEAfAheAMH1nmh8R7zIAYNggcAGIqNcwwwUAViFwAQjDGi4AsBaBC0AYI7GGCwAsROACEKbvtBCs4QIAqxC4AIQxcrGGCwAsROACEAFruADASgQuAGGCcukMXykCgGUIXAAiYoYLAKxD4AIQxhiXJRevBgD0IXABCGPkUg/tAQAsQ0cFEBEzXABgHQIXgDBGUq8IXABgFQIXgAhYwwUAViJwAQjDGi4AsBYdFUBEzHABgHUIXADCGElBC9Zw9fb2aunSpTp48KDcbreqq6uVmZkpSTp27Jgef/zx0Lb79+/XggULVFpaOujjAoDT2PIRNhgMqrKyUl6vV7Nnz9ahQ4fCtjl16pRKSkrU0dEhSerp6dGiRYs0a9Ys3XPPPfrjH/9oR2kAYmDkUo/xRL1Fs2PHDklSQ0OD5s+fr+rq6tBYRkaG6uvrVV9fr8cff1zf/e53VVxcbNtrAoB4smWGa9u2bfL7/WpsbFRbW5tqampUV1cXGv/ggw/0s5/9TEePHg099vrrr2vMmDF65plndOLECd19992aNm2aHeUBiMLIFdOvFDs7O1VYWBi67/V65fV6Q/enT5+uKVOmSJKOHDmisWPHhh/LGD311FP6+c9/Lrebs9sDGJ5sCVx79uxRfn6+JGny5Mlqb2/vN+73+7VmzRo98cQTocduvvlm/fjHPw7dp/ECcWRiW8OVnp6upqamc27j8XhUXl6urVu3avXq1WHj27dv18SJE5WVlXXe5QKA09nylaLP51NqamrovtvtViAQCN3Py8vTZZdd1m+flJQUpaamyufzaf78+Xr00UftKA1ADL46D1e0W6xqa2v15ptvatmyZeru7u439vrrr/NVIoBhz5bAlZqaqq6urtD9YDAojyf6ZNrnn3+uOXPm6M4779Ttt99uR2kAYuJSwHii3qLZtGmT1q1bJ0lKSkqSy+UKm73eu3evcnNzbXkVAOAUtgSu3Nxc7dy5U5LU1tamnJycqPt88cUXeuCBB7Ro0SLdc889dpQFIEZfreEa7AzXzJkztW/fPpWVlWnu3LmqqKjQli1b1NjYKKlvDVhKSopcLpfdLwkA4sqWNVwzZsxQS0uLSkpKZIzRypUr1dzcrO7u7n4Lar/u+eef15dffqm1a9dq7dq1kqQXX3xRo0aNsqNEAFFYcR6u5ORkrVq16qzj6enp+vd///dBHwcAnM6WwJWQkKDly5f3eyw7Oztsu/r6+tC/ly5dqqVLl9pRDoAB+uq0EAAAa9BRAYSx6sSnAIA+BC4AEcR2Hi4AQGwIXADCGKOYfoUIAIgNHRVAGCOXerl4NQBYhsAFICLWcAGAdQhcAMLEei1FAEBsCFwAwhixhgsArERHBRABa7gAwEoELgARGZl4lwAAwwaBC0AYI6NeBeJdBgAMGwQuABEYGQXjXQQADBsELgAREbgAwDoELgBhjAhcAGAlAheACFjDBQBWInABiIA1XABgJQIXgDB8pQgA1iJwAYjAmq8Ue3t7tXTpUh08eFBut1vV1dXKzMwMjb///vuqqamRMUYZGRl65plnNHLkyEEfFwCchlNJA4jIKBj1Fs2OHTskSQ0NDZo/f76qq6v/+vzGaNmyZaqurtbvfvc75efn6/Dhw7a9HgCIJ2a4AIQxFq3hmj59uqZMmSJJOnLkiMaOHRsaO3jwoMaMGaNXXnlFH330kQoKCpSVlTXoYwKAExG4AEQUjOErxc7OThUWFobue71eeb3eftt4PB6Vl5dr69atWr16dejxEydO6N1339WyZcs0YcIEzZs3T5MmTdJ1111n3YsAAIcgcAGIwCgYwwxXenq6mpqaom5XW1urhQsXqri4WJs3b1ZycrLGjBmjCRMm6O/+7u8kSfn5+WpvbydwARiWWMMFIMxXv1Ic7BquTZs2ad26dZKkpKQkuVwuud1uSdK3v/1tdXV16dChQ5Kk1tZWTZw40bbXBADxxAwXgAisWcM1c+ZMLVmyRGVlZQoEAqqoqNCWLVvU3d0tr9erp59+WgsWLJAxRtdcc01ovRcADDe2BK5gMKiqqiodOHBAiYmJWrFihSZMmNBvm1OnTun+++/X008/rezs7Jj2AXDhxLKGK5rk5GStWrXqrOPXXXedNmzYMOjjAIDT2fKV4rZt2+T3+9XY2KgFCxaopqam3/gHH3ygsrIyffrppzHvA+DCMf+zhivafwCA2NgSuPbs2aP8/HxJ0uTJk9Xe3t5v3O/3a82aNf1+Ah5tHwAXlhVruAAAfWz5StHn8yk1NTV03+12KxAIyOPpO1xeXt6A9wFwIRlLvlIEAPSxJc2kpqaqq6srdD8YDEYNTuezDwD7MIMFANax5SvF3Nxc7dy5U5LU1tamnJwcW/YBYA/WcAGAtWyZQpoxY4ZaWlpUUlIiY4xWrlyp5ubm0E/BY90HQPwwwwUA1rElcCUkJGj58uX9HsvOzg7brr6+/pz7AIgPwxouALAUi6QARMQMFwBYh8AFIILYrqUIAIgNgQtAGGOCCgZPx7sMABg2CFwAIjKGNVwAYBUCF4AIDIELACxE4AIQxpigenv5ShEArELgAhABM1wAYCUCF4CICFwAYB0CF4AImOECACsRuACEYQ0XAFiLwAUgAma4AMBKBC4AEVkRuHp7e7V06VIdPHhQbrdb1dXVyszMDI3/+te/1oYNG5Seni5JevLJJ5WVlTXo4wKA0xC4AISx6ivFHTt2SJIaGhq0e/duVVdXq66uLjS+d+9e1dbWatKkSYM+FgA4GYELQATWfKU4ffp0TZkyRZJ05MgRjR07tt/43r179cILL+jYsWOaMmWKHnzwwUEfEwCciMAFIILYAldnZ6cKCwtD971er7xeb79tPB6PysvLtXXrVq1evbrf2G233aZZs2YpNTVVjzzyiHbs2KGbbrrJmpcAAA5C4AIQxhgT08Wr09PT1dTUFHW72tpaLVy4UMXFxdq8ebOSk5NljNG9996rtLQ0SVJBQYH27dtH4AIwLCXEuwAATmQUDAai3qLZtGmT1q1bJ0lKSkqSy+WS2+2WJPl8Pv3jP/6jurq6ZIzR7t27WcsFYNhihgtABNas4Zo5c6aWLFmisrIyBQIBVVRUaMuWLeru7pbX69Vjjz2mOXPmKDExUdddd50KCgosqB0AnIfABSACawJXcnKyVq1addbxu+66S3fdddegjwMATkfgAhAm1jVcAIDYELgARGBiWqMFAIgNgQtABFzaBwCsROACEMaYIF8pAoCFbAlcwWBQVVVVOnDggBITE7VixQpNmDAhNL59+3atWbNGHo9HRUVFKi4uVk9PjxYvXqzDhw8rISFBTz31lLKzs+0oD0AMmOECAOvYch6ubdu2ye/3q7GxUQsWLFBNTU1orKenR9XV1Xr55ZdVX1+vxsZGHTt2TP/5n/+pQCCghoYGPfzww3r22WftKA1ATKw5DxcAoI8tM1x79uxRfn6+JGny5Mlqb28PjXV0dCgzM1OjR4+WJOXl5am1tVU5OTnq7e1VMBiUz+eTx8O3nUC8GMMaLgCwki2pxufzKTU1NXTf7XYrEAjI4/HI5/OFLuUhSSkpKfL5fEpOTtbhw4d1yy236MSJE3r++eejHufM5RO1f80mO14CMKycOXNmQNv/6Ef/j/bufT/qdnwwAoDY2NItU1NT1dXVFbofDAZDjflvx7q6upSWlqbf/OY3uvHGG7VgwQJ9/vnnuvfee9Xc3KyRI0ee9TiTJ0+2o3zgojdx4sR4lwAAw4ota7hyc3O1c+dOSVJbW5tycnJCY9nZ2Tp06JBOnjwpv9+v1tZWXXPNNbrkkktCM1+jR49WIBBQb2+vHeUBAABcUC5jjLH6Sb/6leJHH30kY4xWrlypffv2ha6f9tWvFI0xKioqUllZmbq6ulRRUaFjx46pp6dHc+bM0e233251aQAAABecLYELAAAAf2XLV4oAAAD4KwIXAACAzQhcAAAANhtWJ9GJdkkhSTp16pTuv/9+Pf3008rOzj7rPocOHdLixYvlcrk0ceJE/exnP1NCgnX59Hxq7enpUUVFhQ4fPiy/36+HHnpI06ZN0969ezVv3jxdfvnlkqTS0lLdeuutcatTku66667Qr06/9a1vqbq62tb39HzqbGpq0r/9279J6jtP1f79+9XS0qJPP/3UtvczllrfeOMNvfLKK3K73crJyVFVVZUkxeXvFABgETOMvPnmm6a8vNwYY8y7775r5s2b12/8/fffN3fffbe5/vrrzccff3zOfR588EHzX//1X8YYY5YtW2a2bNkS91o3bNhgVqxYYYwxprOz0xQUFBhjjHn11VfNSy+9ZGl9g6nz9OnT5s477wx7Ljvf0/Op8+uqqqpMQ0ODMcbe9zNaradOnTLTpk0z3d3dxhhjHnvsMbNt27a4/Z0CAKwxrD4Kn+uSQpLk9/u1Zs0aZWVlRd1n7969+uEPfyhJ+od/+Ae9/fbbca/15ptv1k9/+tPQfbfbLUlqb2/XW2+9pbKyMlVUVMjn88W1zg8//FCnTp3SAw88oDlz5qitrU2Sve/p+dT5lQ8++EAff/yxvF6vJHvfz2i1JiYmqqGhQUlJSZKkQCCgkSNHxu3vFABgjWEVuM52SaGv5OXl6bLLLotpH2OMXC6XpL7LD/3lL3+Je60pKSlKTU2Vz+fT/Pnz9eijj0qSvv/97+uJJ57Q+vXr9e1vf1tr1qyJa52jRo3S3Llz9dJLL+nJJ5/UwoULbX9Pz6fOr6xbt04PP/xw6L6d72e0WhMSEjR27FhJUn19vbq7u3XDDTfE7e8UAGCNYRW4znVJoYHu8/V1MF1dXbrkkkviXqskff7555ozZ47uvPPO0IlhZ8yYoUmTJoX+vW/fvrjWecUVV+iOO+6Qy+XSFVdcoTFjxujYsWO2vqfn+35++eWX+uSTT/SjH/0o9Jid72cstQaDQdXW1qqlpUXPPfecXC5X3P5OAQDWGFaB61yXFBroPt/97ne1e/duSdLOnTt17bXXxr3WL774Qg888IAWLVqke+65J/T43Llz9f77fRca3rVrl66++uq41rlhwwbV1NRIko4ePSqfz6eMjAxb39PzqVOS3nnnHV1//fX9HrPz/Yyl1srKSp05c0Zr164NfbUYr79TAIA1htWZ5qNdUugrs2fPVlVVVb9fKX59n+zsbB08eFDLli1TT0+PsrKytGLFitCaqXjVumLFCv3+97/vtw7pxRdfVEdHh5566imNGDFCY8eO1VNPPdXv66cLXaff79eSJUt05MgRuVwuLVy4ULm5uba+p+dTpyT96le/ksfj0X333RfaZu/evba9n9FqnTRpkoqKinTttdeGviqcM2eOpk2bFpe/UwCANYZV4AIAAHCiYfWVIgAAgBMRuAAAAGxG4AIAALAZgQsAAMBmBC4AAACbEbgwaC0tLbrjjjt0+vRpSX3n3rr99tt19OjROFcGAIAzELgwaDfccINuvPFG1dTUqKenR4899pgWL16s8ePHx7s0AAAcgfNwwRI9PT2aNWuWxowZox/84Ad65JFH4l0SAACOwQwXLDFixAgVFxfr7bffVmFhYbzLAQDAUQhcsMThw4f1q1/9SosWLdKiRYvU29sb75IAAHAMAhcGze/369FHH1VFRYXuu+8+XXbZZfrlL38Z77IAAHAMAhcGrba2Vnl5eSooKJAkVVVVafPmzdq9e3ecKwMAwBlYNA8AAGAzZrgAAABsRuACAACwGYELAADAZgQuAAAAmxG4AAAAbEbgAgAAsBmBCwAAwGb/P4Osuo0Wb0pXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def Vz(z):\n",
    "    return 4.0 + 10.0*(z-0.050)\n",
    "Vp = np.kron(Vz(zz), np.ones((g.shape[0], g.shape[1], 1)))\n",
    "\n",
    "Vs = 2.1\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.subplot(221)\n",
    "plt.pcolor(x,z,np.squeeze(Vp[:,0,:].T), cmap='CMRmap',), plt.gca().invert_yaxis()\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Z')\n",
    "plt.colorbar()\n",
    "plt.subplot(222)\n",
    "plt.pcolor(y,z,np.squeeze(Vp[0,:,:].T), cmap='CMRmap'), plt.gca().invert_yaxis()\n",
    "plt.xlabel('Y')\n",
    "plt.ylabel('Z')\n",
    "plt.colorbar()\n",
    "plt.subplot(223)\n",
    "plt.pcolor(x,y,np.squeeze(Vp[:,:,4].T), cmap='CMRmap')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now compute the travel times\n",
    "\n",
    "The raytracing routine takes 3 input variables:\n",
    "- the slowness vector (ordered in 'C' order)\n",
    "- the matrix of hypocenter data (hypo)\n",
    "- the matrix of receiver coordonates (rcv)\n",
    "\n",
    "hypo and rcv must have the same number of rows, with each row corresponding to a source-receiver pair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "slowness = 1./Vp.flatten()\n",
    "slowness_s = 1./Vs + np.zeros(g.get_number_of_cells())\n",
    "\n",
    "src = np.kron(src,np.ones((nsta,1)))   # use kron to replicate src-rcv pairs correctly\n",
    "rcv_data = np.kron(np.ones((nev,1)), rcv)\n",
    "ircv_data = np.kron(np.ones((nev,1)), ircv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# call the actual raytracing method\n",
    "\n",
    "tt = g.raytrace(src, rcv_data, slowness)\n",
    "tt_s = g.raytrace(src, rcv_data, slowness_s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Add some noise to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise_variance = 1.e-3;  # 1 ms\n",
    "tt += noise_variance*np.random.randn(tt.size)\n",
    "tt_s += noise_variance*np.random.randn(tt_s.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine the event IDs, travel times, and receiver locations, after removing some values randomly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove some values\n",
    "ind_p = np.ones(tt.shape[0], dtype=bool)\n",
    "ind_p[np.random.randint(ind_p.size,size=25)] = False\n",
    "ind_s = np.ones(tt_s.shape[0], dtype=bool)\n",
    "ind_s[np.random.randint(ind_s.size,size=25)] = False\n",
    "\n",
    "data_p = np.hstack((src[ind_p,0].reshape((-1,1)), tt[ind_p].reshape((-1,1)), ircv_data[ind_p,:], np.zeros((np.sum(ind_p),1))))\n",
    "data_s = np.hstack((src[ind_s,0].reshape((-1,1)), tt_s[ind_s].reshape((-1,1)), ircv_data[ind_s,:], np.ones((np.sum(ind_s),1))))\n",
    "\n",
    "data = np.vstack((data_p, data_s))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial hypocenter locations\n",
    "\n",
    "Note that for efficiency reason when computing matrix M, initial hypocenters\n",
    "should _not_ be equal for any two event, e.g. they shoud all be different.\n",
    "\n",
    "Here we place the initial coordinates more or less in the center of the grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "hinit = np.vstack((np.arange(nev),\n",
    "                   np.linspace(0., 50., nev),\n",
    "                   0.150 + 0.0001*np.random.randn(nev),\n",
    "                   0.150 + 0.0001*np.random.randn(nev),\n",
    "                   0.050 + 0.0001*np.random.randn(nev))).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Velocity data points\n",
    "\n",
    "For illustrative purposes, let's add a couple of velocity data points to constrain the inversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "Vpts = np.array([[Vz(0.001), 0.100, 0.100, 0.001, 0.0],\n",
    "                 [Vz(0.001), 0.100, 0.200, 0.001, 0.0],\n",
    "                 [Vz(0.001), 0.200, 0.100, 0.001, 0.0],\n",
    "                 [Vz(0.001), 0.200, 0.200, 0.001, 0.0],\n",
    "                 [Vz(0.011), 0.112, 0.148, 0.011, 0.0],\n",
    "                 [Vz(0.005), 0.152, 0.108, 0.005, 0.0],\n",
    "                 [Vz(0.075), 0.152, 0.108, 0.075, 0.0],\n",
    "                 [Vz(0.011), 0.192, 0.148, 0.011, 0.0],\n",
    "                 [Vs, 0.100, 0.100, 0.001, 1.0],\n",
    "                 [Vs, 0.100, 0.200, 0.001, 1.0],\n",
    "                 [Vs, 0.200, 0.100, 0.001, 1.0],\n",
    "                 [Vs, 0.200, 0.200, 0.001, 1.0],\n",
    "                 [Vs, 0.112, 0.148, 0.011, 1.0],\n",
    "                 [Vs, 0.152, 0.108, 0.005, 1.0],\n",
    "                 [Vs, 0.152, 0.108, 0.075, 1.0],\n",
    "                 [Vs, 0.192, 0.148, 0.011, 1.0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calibration shots\n",
    "\n",
    "Let's use 5 shots recorded at most receivers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "ncal = 5\n",
    "src_cal = np.vstack((5+np.arange(ncal),\n",
    "                 np.zeros(ncal),\n",
    "                 0.160 +  0.005*np.random.randn(ncal),\n",
    "                 0.130 +  0.005*np.random.randn(ncal),\n",
    "                 0.045 +  0.005*np.random.randn(ncal))).T\n",
    "\n",
    "src_cal = np.kron(src_cal,np.ones((nsta,1)))\n",
    "rcv_cal = np.kron(np.ones((ncal,1)), rcv)\n",
    "ircv_cal = np.kron(np.ones((ncal,1)), ircv)\n",
    "\n",
    "ind = np.ones(rcv_cal.shape[0], dtype=bool)\n",
    "ind[3] = 0\n",
    "ind[13] = 0\n",
    "ind[15] = 0\n",
    "src_cal = src_cal[ind,:]\n",
    "rcv_cal = rcv_cal[ind,:]\n",
    "ircv_cal = ircv_cal[ind,:]\n",
    "\n",
    "tcal = g.raytrace(src_cal, rcv_cal, slowness)\n",
    "tcal_s = g.raytrace(src_cal, rcv_cal, slowness_s)\n",
    "caldata_p = np.column_stack((src_cal[:,0], tcal, ircv_cal, src_cal[:,2:], np.zeros(tcal.shape)))\n",
    "caldata_s = np.column_stack((src_cal[:,0], tcal_s, ircv_cal, src_cal[:,2:], np.ones(tcal_s.shape)))\n",
    "caldata = np.vstack((caldata_p, caldata_s))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constant velocity inversion\n",
    "\n",
    "Before running the joint hypocenter-velocity inversion (JHVI), we use the constant velocity location algorithm.  This will improve convergence of the JHVI.\n",
    "\n",
    "Initial velocity model is of course homogeneous.  We take the mean value of the data points for Vp and a value a bit off for Vs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " *** Hypocenter inversion  --  P and S-wave data ***\n",
      "\n",
      "Locating hypocenters no 0\n",
      "     Converged at iteration 3\n",
      "Locating hypocenters no 1\n",
      "     Converged at iteration 3\n",
      "Locating hypocenters no 2\n",
      "     Converged at iteration 3\n",
      "Locating hypocenters no 3\n",
      "     Converged at iteration 3\n",
      "Locating hypocenters no 4\n",
      "     Converged at iteration 2\n",
      "Locating hypocenters no 5\n",
      "     Converged at iteration 3\n",
      "Locating hypocenters no 6\n",
      "     Converged at iteration 2\n",
      "Locating hypocenters no 7\n",
      "     Converged at iteration 3\n",
      "Locating hypocenters no 8\n",
      "     Converged at iteration 3\n",
      "Locating hypocenters no 9\n",
      "     Converged at iteration 2\n",
      "Locating hypocenters no 10\n",
      "     Converged at iteration 3\n",
      "Locating hypocenters no 11\n",
      "     Converged at iteration 3\n",
      "Locating hypocenters no 12\n",
      "     Converged at iteration 3\n",
      "Locating hypocenters no 13\n",
      "     Converged at iteration 3\n",
      "Locating hypocenters no 14\n",
      "     Converged at iteration 3\n",
      "\n",
      " ** Inversion complete **\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Vinit = (np.mean(Vpts[:8,0]), 2.0)\n",
    "\n",
    "hinit2, res = hypo.hypolocPS(data, rcv, V=Vinit, hinit=hinit, maxit=15, convh=0.001, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look now at error on hypocenter location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "err_x = hinit2[:,2:5] - h_true[:,2:5]\n",
    "err_x = np.sqrt(np.sum(err_x**2, axis=1))\n",
    "err_t = hinit2[:,1] - h_true[:,1]\n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.subplot(121)\n",
    "plt.plot(err_x,'r*')\n",
    "plt.ylabel(r'$\\Delta x$')\n",
    "plt.xlabel('Event ID')\n",
    "plt.title(r'$\\|\\|\\Delta x\\|\\|$ = {0:6.5f}'.format(np.linalg.norm(err_x)))\n",
    "plt.subplot(122)\n",
    "plt.plot(err_t,'r*')\n",
    "plt.ylabel(r'$\\Delta t$')\n",
    "plt.xlabel('Event ID')\n",
    "plt.title(r'$\\|\\|\\Delta t\\|\\|$ = {0:6.5f}'.format(np.linalg.norm(err_t)))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Joint hypocenter-velocity inversion parameters\n",
    "\n",
    "Define inversion parameters.  We will invert for $V_S$ rather than for $V_S/V_P$, the latter not being fully tested at this time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "Vpmin = 3.5\n",
    "Vpmax = 4.5\n",
    "PAp = 1.0\n",
    "Vsmin = 1.9\n",
    "Vsmax = 2.3\n",
    "PAs = 1.0\n",
    "Vlim = (Vpmin, Vpmax, PAp, Vsmin, Vsmax, PAs)\n",
    "        \n",
    "dVp_max = 0.1\n",
    "dx_max = 0.010\n",
    "dt_max = 0.01\n",
    "dVs_max = 0.1\n",
    "dmax = (dVp_max, dx_max, dt_max, dVs_max)\n",
    "        \n",
    "λ = 2.\n",
    "γ = 1.\n",
    "α = 1.\n",
    "wzK = 0.1\n",
    "lagran = (λ, γ, α, wzK)\n",
    "\n",
    "par = hypo.InvParams(maxit=5, maxit_hypo=10, conv_hypo=0.001, Vlim=Vlim, dmax=dmax,\n",
    "                     lagrangians=lagran, invert_vel=True, invert_VsVp=False, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's build an initial velocity model slightly off the true one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Vz2(z):\n",
    "    return 4. + 7.5*(z-0.050)\n",
    "\n",
    "Vpinit = np.kron(Vz2(zz), np.ones((g.shape[0], g.shape[1], 1)))\n",
    "Vpinit = Vpinit.flatten()\n",
    "Vinit = (Vpinit, 2.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Joint hypocenter-velocity inversion\n",
    "\n",
    "Run the inversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " *** Joint hypocenter-velocity inversion -- P and S-wave data ***\n",
      "\n",
      "Building velocity data point matrix D\n",
      "Building regularization matrix K\n",
      "\n",
      "Starting iterations\n",
      "\n",
      "Iteration 1 - Updating velocity model\n",
      "\n",
      "  Updating penalty vector\n",
      "  Raytracing\n",
      "  Building matrix M\n",
      "    Event ID 0\n",
      "    Event ID 1\n",
      "    Event ID 2\n",
      "    Event ID 3\n",
      "    Event ID 4\n",
      "    Event ID 5\n",
      "    Event ID 6\n",
      "    Event ID 7\n",
      "    Event ID 8\n",
      "    Event ID 9\n",
      "    Event ID 10\n",
      "    Event ID 11\n",
      "    Event ID 12\n",
      "    Event ID 13\n",
      "    Event ID 14\n",
      "  Assembling matrices and solving system\n",
      "    calling minres with system of size 7098 x 7098\n",
      "  Scaling P slowness perturbations by 3.731028e-01\n",
      "  Scaling S slowness perturbations by 7.226520e-01\n",
      "Iteration 1 - Relocating events\n",
      "  Updating event ID 0 (1/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 1 (2/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 2 (3/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 3 (4/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 4 (5/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 5 (6/15)\n",
      "    Updating all hypocenter params - converged at iteration 3\n",
      "  Updating event ID 6 (7/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 7 (8/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 8 (9/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 9 (10/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 10 (11/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 11 (12/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 12 (13/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 13 (14/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 14 (15/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "\n",
      "Iteration 2 - Updating velocity model\n",
      "\n",
      "  Updating penalty vector\n",
      "  Raytracing\n",
      "  Building matrix M\n",
      "    Event ID 0\n",
      "    Event ID 1\n",
      "    Event ID 2\n",
      "    Event ID 3\n",
      "    Event ID 4\n",
      "    Event ID 5\n",
      "    Event ID 6\n",
      "    Event ID 7\n",
      "    Event ID 8\n",
      "    Event ID 9\n",
      "    Event ID 10\n",
      "    Event ID 11\n",
      "    Event ID 12\n",
      "    Event ID 13\n",
      "    Event ID 14\n",
      "  Assembling matrices and solving system\n",
      "    calling minres with system of size 7098 x 7098\n",
      "  Scaling P slowness perturbations by 6.947401e-01\n",
      "Iteration 2 - Relocating events\n",
      "  Updating event ID 0 (1/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 1 (2/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 2 (3/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 3 (4/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 4 (5/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 5 (6/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 6 (7/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 7 (8/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 8 (9/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 9 (10/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 10 (11/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 11 (12/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 12 (13/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 13 (14/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 14 (15/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "\n",
      "Iteration 3 - Updating velocity model\n",
      "\n",
      "  Updating penalty vector\n",
      "  Raytracing\n",
      "  Building matrix M\n",
      "    Event ID 0\n",
      "    Event ID 1\n",
      "    Event ID 2\n",
      "    Event ID 3\n",
      "    Event ID 4\n",
      "    Event ID 5\n",
      "    Event ID 6\n",
      "    Event ID 7\n",
      "    Event ID 8\n",
      "    Event ID 9\n",
      "    Event ID 10\n",
      "    Event ID 11\n",
      "    Event ID 12\n",
      "    Event ID 13\n",
      "    Event ID 14\n",
      "  Assembling matrices and solving system\n",
      "    calling minres with system of size 7098 x 7098\n",
      "Iteration 3 - Relocating events\n",
      "  Updating event ID 0 (1/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 1 (2/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 2 (3/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 3 (4/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 4 (5/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 5 (6/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 6 (7/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 7 (8/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 8 (9/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 9 (10/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 10 (11/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 11 (12/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 12 (13/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 13 (14/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 14 (15/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "\n",
      "Iteration 4 - Updating velocity model\n",
      "\n",
      "  Updating penalty vector\n",
      "    P-wave penalties applied at 17 nodes\n",
      "  Raytracing\n",
      "  Building matrix M\n",
      "    Event ID 0\n",
      "    Event ID 1\n",
      "    Event ID 2\n",
      "    Event ID 3\n",
      "    Event ID 4\n",
      "    Event ID 5\n",
      "    Event ID 6\n",
      "    Event ID 7\n",
      "    Event ID 8\n",
      "    Event ID 9\n",
      "    Event ID 10\n",
      "    Event ID 11\n",
      "    Event ID 12\n",
      "    Event ID 13\n",
      "    Event ID 14\n",
      "  Assembling matrices and solving system\n",
      "    calling minres with system of size 7098 x 7098\n",
      "Iteration 4 - Relocating events\n",
      "  Updating event ID 0 (1/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 1 (2/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 2 (3/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 3 (4/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 4 (5/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 5 (6/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 6 (7/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 7 (8/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 8 (9/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 9 (10/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 10 (11/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 11 (12/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 12 (13/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 13 (14/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 14 (15/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "\n",
      "Iteration 5 - Updating velocity model\n",
      "\n",
      "  Updating penalty vector\n",
      "    P-wave penalties applied at 8 nodes\n",
      "  Raytracing\n",
      "  Building matrix M\n",
      "    Event ID 0\n",
      "    Event ID 1\n",
      "    Event ID 2\n",
      "    Event ID 3\n",
      "    Event ID 4\n",
      "    Event ID 5\n",
      "    Event ID 6\n",
      "    Event ID 7\n",
      "    Event ID 8\n",
      "    Event ID 9\n",
      "    Event ID 10\n",
      "    Event ID 11\n",
      "    Event ID 12\n",
      "    Event ID 13\n",
      "    Event ID 14\n",
      "  Assembling matrices and solving system\n",
      "    calling minres with system of size 7098 x 7098\n",
      "Iteration 5 - Relocating events\n",
      "  Updating event ID 0 (1/15)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 1 (2/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 2 (3/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 3 (4/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 4 (5/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 5 (6/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 6 (7/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 7 (8/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 8 (9/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 9 (10/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 10 (11/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 11 (12/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 12 (13/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 13 (14/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 14 (15/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "\n",
      " ** Inversion complete **\n",
      "\n"
     ]
    }
   ],
   "source": [
    "h, V, sc, res = hypo.jointHypoVelPS(par, g, data, rcv, Vinit, hinit2, caldata=caldata, Vpts=Vpts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(res[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "err_xc = hinit2[:,2:5] - h_true[:,2:5]\n",
    "err_xc = np.sqrt(np.sum(err_xc**2, axis=1))\n",
    "err_tc = hinit2[:,1] - h_true[:,1]\n",
    "\n",
    "err_x = h[:,2:5] - h_true[:,2:5]\n",
    "err_x = np.sqrt(np.sum(err_x**2, axis=1))\n",
    "err_t = h[:,1] - h_true[:,1]\n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.subplot(121)\n",
    "plt.plot(err_x,'o',label=r'$\\|\\|\\Delta x\\|\\|$ = {0:6.5f}'.format(np.linalg.norm(err_x)))\n",
    "plt.plot(err_xc,'r*',label=r'$\\|\\|\\Delta x\\|\\|$ = {0:6.5f}'.format(np.linalg.norm(err_xc)))\n",
    "plt.ylabel(r'$\\Delta x$')\n",
    "plt.xlabel('Event ID')\n",
    "plt.legend()\n",
    "plt.subplot(122)\n",
    "plt.plot(np.abs(err_t),'o',label=r'$\\|\\|\\Delta t\\|\\|$ = {0:6.5f}'.format(np.linalg.norm(err_t)))\n",
    "plt.plot(np.abs(err_tc),'r*',label=r'$\\|\\|\\Delta t\\|\\|$ = {0:6.5f}'.format(np.linalg.norm(err_tc)))\n",
    "plt.ylabel(r'$\\Delta t$')\n",
    "plt.xlabel('Event ID')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see a little improvement on relocation over constant velocity inversion, mainly for the origin time.  What about the velocity model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "V3d = V[0].reshape(g.shape)\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.subplot(221)\n",
    "plt.pcolor(x,z,np.squeeze(V3d[:,9,:].T), cmap='CMRmap',), plt.gca().invert_yaxis()\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Z')\n",
    "plt.colorbar()\n",
    "plt.subplot(222)\n",
    "plt.pcolor(y,z,np.squeeze(V3d[8,:,:].T), cmap='CMRmap'), plt.gca().invert_yaxis()\n",
    "plt.xlabel('Y')\n",
    "plt.ylabel('Z')\n",
    "plt.colorbar()\n",
    "plt.subplot(223)\n",
    "plt.pcolor(x,y,np.squeeze(V3d[:,:,4].T), cmap='CMRmap')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.colorbar()\n",
    "plt.suptitle('V_p')\n",
    "\n",
    "V3d = V[1].reshape(g.shape)\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.subplot(221)\n",
    "plt.pcolor(x,z,np.squeeze(V3d[:,9,:].T), cmap='CMRmap',), plt.gca().invert_yaxis()\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Z')\n",
    "plt.colorbar()\n",
    "plt.subplot(222)\n",
    "plt.pcolor(y,z,np.squeeze(V3d[8,:,:].T), cmap='CMRmap'), plt.gca().invert_yaxis()\n",
    "plt.xlabel('Y')\n",
    "plt.ylabel('Z')\n",
    "plt.colorbar()\n",
    "plt.subplot(223)\n",
    "plt.pcolor(x,y,np.squeeze(V3d[:,:,4].T), cmap='CMRmap')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.colorbar()\n",
    "plt.suptitle('V_s')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Let's look at station corrections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sc[0],'o',label='P-wave')\n",
    "plt.plot(sc[1],'r*',label='s-wave')\n",
    "plt.xlabel('Station no')\n",
    "plt.ylabel('Correction')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "These results show that efforts should be put in estimating the initial the S wave velocity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}