Extract seismic amplitudes onto a well path

Back-interpolate_seismic_onto_wellbore.ipynb

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   "source": [
    "# Back-interpolate seismic onto wellbore\n",
    "\n",
    "`xarray` is really just pandas-like indexing for NumPy's n-dimensional arrays.\n",
    "\n",
    "This means we can index into it with real-world coordinates, without having to figure out what indices we need.\n",
    "\n",
    "So for example, if we have a seismic volume as an `xarray`, and a seismic horizon as a 2D `xarray.DataArray`, we can easily extract amplitudes from the seismic onto the horizon. Likewise, if we have a 1D array representing a wellbore, for example, we can back the seismic onto that.\n",
    "\n",
    "## Scenario\n",
    "\n",
    "We have a deviated well in a 3D seismic volume. We want to get amplitudes from along the wellbore.\n",
    "\n",
    "## Make fake data\n",
    "\n",
    "We'll do everything in this notebook with fake data. We will need:\n",
    "\n",
    "- A 3D seismic volume with 3 known cornerpoints (i.e. we know (UTMx, UTMy) for three (inline, xline) locations).\n",
    "- A wellbore with:\n",
    "  - Known tophole location.\n",
    "  - A deviation survey or position log.\n",
    "  - Known two-way time (or whatever basis corresponds to the vertical dimension of the seismic), eg TWT at every MD location. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02b5c424",
   "metadata": {},
   "source": [
    "### Seismic data as `xarray.DataArray`\n",
    "\n",
    "We have a seismic volume with these (completely made up) cornerpoint coordinates (with very small bins!):\n",
    "\n",
    "```\n",
    "corner_ix = [[0,  0], [0, 150], [100, 150]]\n",
    "corner_xy = [[10_000, 10_000],\n",
    "             [10_050, 15_000],\n",
    "             [11_000, 14_850]]\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "72db832e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Use random numbers but add a ramp so we can tell how deep we are.\n",
    "seismic_ = 10 * np.random.random((100, 150, 200)) + np.arange(200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d168f85d",
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 2,
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     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.imshow(seismic_[50].T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3bf3d8db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "  * iline    (iline) int64 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99\n",
       "  * xline    (xline) int64 0 1 2 3 4 5 6 7 8 ... 142 143 144 145 146 147 148 149\n",
       "  * twt      (twt) float64 0.0 0.004 0.008 0.012 ... 0.784 0.788 0.792 0.796"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "\n",
    "i, x, t = seismic_.shape\n",
    "\n",
    "seismic = xr.DataArray(seismic_,\n",
    "                       name='amplitude',\n",
    "                       coords=[np.arange(i), np.arange(x), np.arange(t) * 0.004],\n",
    "                       dims=['iline', 'xline', 'twt']\n",
    "                      )\n",
    "\n",
    "seismic.coords"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5818e362",
   "metadata": {},
   "source": [
    "We can't do anything with the UTM coordinates yet, we'll deal with those later."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92afbe62",
   "metadata": {},
   "source": [
    "### Well log as `pandas.DataFrame`\n",
    "\n",
    "We'll use this dataframe as the main data structure for the well(s).\n",
    "\n",
    "This well has `DEPT` and `TIME` coordinates. We'll use `DEPT` as the index. (Be wary of using `TIME` as an index because they times in a deviated wellbore may not be unique.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3fe26354",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DEPT</th>\n",
       "      <th>TIME</th>\n",
       "      <th>GR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>0.120</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110</td>\n",
       "      <td>0.131</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>120</td>\n",
       "      <td>0.142</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>130</td>\n",
       "      <td>0.151</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>140</td>\n",
       "      <td>0.163</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>150</td>\n",
       "      <td>0.172</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>160</td>\n",
       "      <td>0.181</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>170</td>\n",
       "      <td>0.192</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>180</td>\n",
       "      <td>0.200</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>190</td>\n",
       "      <td>0.208</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>200</td>\n",
       "      <td>0.213</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>210</td>\n",
       "      <td>0.217</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>220</td>\n",
       "      <td>0.218</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>230</td>\n",
       "      <td>0.219</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>240</td>\n",
       "      <td>0.219</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    DEPT   TIME  GR\n",
       "0    100  0.120  32\n",
       "1    110  0.131  28\n",
       "2    120  0.142  27\n",
       "3    130  0.151  30\n",
       "4    140  0.163  35\n",
       "5    150  0.172  38\n",
       "6    160  0.181  32\n",
       "7    170  0.192  29\n",
       "8    180  0.200  26\n",
       "9    190  0.208  26\n",
       "10   200  0.213  23\n",
       "11   210  0.217  18\n",
       "12   220  0.218  16\n",
       "13   230  0.219  12\n",
       "14   240  0.219  18"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = [[100, 0.120, 32],\n",
    "        [110, 0.131, 28],\n",
    "        [120, 0.142, 27],\n",
    "        [130, 0.151, 30],\n",
    "        [140, 0.163, 35],\n",
    "        [150, 0.172, 38],\n",
    "        [160, 0.181, 32],\n",
    "        [170, 0.192, 29],\n",
    "        [180, 0.200, 26],\n",
    "        [190, 0.208, 26],\n",
    "        [200, 0.213, 23],\n",
    "        [210, 0.217, 18],\n",
    "        [220, 0.218, 16],\n",
    "        [230, 0.219, 12],\n",
    "        [240, 0.219, 18],\n",
    "       ]\n",
    "\n",
    "well = pd.DataFrame(data, columns=['DEPT', 'TIME', 'GR'])\n",
    "well"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "131ed2c3",
   "metadata": {},
   "source": [
    "### Position log from deviation\n",
    "\n",
    "We have a deviation survey for the well. It has MD, INCL, AZI.\n",
    "\n",
    "Ultimately we need TWT, inline, xline to index into the seismic. We already know the TWT at each MD (see dataframe above), now we need inline and xline.\n",
    "\n",
    "We can't get inline and xline directly from the deviation survewy, obviously, but we can get UTMx and UTMy.\n",
    "\n",
    "**So from this exercise we need MD (to map to TWT using the dataframe above), and (UTMx, UTMy) to convert to (inline, xline).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "219f746f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import wellpathpy as wp\n",
    "\n",
    "#                      MDrKB, INCL, AZI\n",
    "deviation = np.array([[  0,    0,    0],\n",
    "                      [ 20,    0,    0],\n",
    "                      [ 40,    0,    0],\n",
    "                      [ 60,    0,    0],\n",
    "                      [ 80,    8,    0],\n",
    "                      [100,   19,    0],\n",
    "                      [120,   30,  190],\n",
    "                      [140,   40,  170],\n",
    "                      [160,   51,  165],\n",
    "                      [180,   68,  153],\n",
    "                      [200,   75,  150],\n",
    "                      [220,   78,  148],\n",
    "                      [240,   81,  149],\n",
    "                      [250,   85,  148],\n",
    "                     ]).T\n",
    "\n",
    "dev = wp.deviation(*deviation)\n",
    "\n",
    "pos = dev.minimum_curvature().resample(depths=well.DEPT)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "020c68dd",
   "metadata": {},
   "source": [
    "Now we can get the (x, y) locations of the MD points in the GR curve:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "83f62dfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = pos.northing  # UTMx\n",
    "x = pos.easting   # UTMy\n",
    "z = pos.depth     # TVDrKB"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99fc4e7e",
   "metadata": {},
   "source": [
    "Adjust these to the well location:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e7607946",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([10500.        , 10499.7579693 , 10499.0751327 , 10498.70300275,\n",
       "        10499.32452795, 10500.66965382, 10502.46276588, 10505.05377257,\n",
       "        10508.74649919, 10513.11740523, 10517.79857492, 10522.71826269,\n",
       "        10527.81484653, 10532.97543483, 10538.08780929]),\n",
       " array([12506.05573664, 12507.24357514, 12504.27856966, 12498.97065223,\n",
       "        12492.95862254, 12486.31098971, 12479.07464213, 12471.29995764,\n",
       "        12463.14701887, 12454.84771947, 12446.49645404, 12438.14608611,\n",
       "        12429.83072148, 12421.4906963 , 12413.06517601]))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x += 10_500\n",
    "y += 12_500\n",
    "\n",
    "x, y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c27517f",
   "metadata": {},
   "source": [
    "Now we need to convert these to inline, xline 'coordinates':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e2423b44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10500.        , 12506.05573664],\n",
       "       [10499.7579693 , 12507.24357514],\n",
       "       [10499.0751327 , 12504.27856966],\n",
       "       [10498.70300275, 12498.97065223],\n",
       "       [10499.32452795, 12492.95862254],\n",
       "       [10500.66965382, 12486.31098971],\n",
       "       [10502.46276588, 12479.07464213],\n",
       "       [10505.05377257, 12471.29995764],\n",
       "       [10508.74649919, 12463.14701887],\n",
       "       [10513.11740523, 12454.84771947],\n",
       "       [10517.79857492, 12446.49645404],\n",
       "       [10522.71826269, 12438.14608611],\n",
       "       [10527.81484653, 12429.83072148],\n",
       "       [10532.97543483, 12421.4906963 ],\n",
       "       [10538.08780929, 12413.06517601]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points = np.array([x, y]).T\n",
    "points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b5c19f5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[50, 77],\n",
       "       [50, 77],\n",
       "       [50, 77],\n",
       "       [50, 77],\n",
       "       [50, 77],\n",
       "       [50, 77],\n",
       "       [50, 77],\n",
       "       [50, 76],\n",
       "       [51, 76],\n",
       "       [51, 76],\n",
       "       [52, 76],\n",
       "       [52, 76],\n",
       "       [53, 75],\n",
       "       [53, 75],\n",
       "       [54, 75]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import bruges as bg\n",
    "\n",
    "corner_ix = [[0,  0], [0, 150], [100, 150]]\n",
    "corner_xy = [[10_000, 10_000],\n",
    "             [10_050, 15_000],\n",
    "             [11_000, 14_850]]\n",
    "\n",
    "transform = bg.transform.CoordTransform(corner_ix, corner_xy)\n",
    "\n",
    "locs = np.array([transform.reverse(p) for p in points])\n",
    "locs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "530bfe8c",
   "metadata": {},
   "source": [
    "Now we can add these to the well log's `xarray` representation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebef8275",
   "metadata": {},
   "source": [
    "### The well log as an `xarray.DataArray`\n",
    "\n",
    "Now we can make the 1D `xarray` of the well log. At first, this might be a bit confusing. This is a 1D array... but we can label its points with no fewer than 7 different coordinates:\n",
    "\n",
    "- MD: the original depths of the measurements.\n",
    "- TVD: the TVDs we just computed from the deviation.\n",
    "- twt: seismic time, which we had in the original log, already sampled at the MD locations.\n",
    "- iline: the seismic inline we got from the affine transformation.\n",
    "- xline: the seismic xline we got from the affine transformation.\n",
    "- UTMx: the eastings, again from deviation.\n",
    "- UTMy: the northings, again from deviation.\n",
    "\n",
    "Note that only MD is a linear space, the others are all nonlinear.\n",
    "\n",
    "`xarray` needs one 'reference' coordinate system; this will be MD. But we can have as many alternative coordinates as we want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "96a9d147",
   "metadata": {},
   "outputs": [],
   "source": [
    "iline, xline = locs.T\n",
    "\n",
    "coords = {'MD':    well.DEPT,\n",
    "          'TVD':   ('MD', z),\n",
    "          'twt':   ('MD', well.TIME),  # Essential.\n",
    "          'iline': ('MD', iline),      # Essential.\n",
    "          'xline': ('MD', xline),      # Essential.\n",
    "          'UTMx':  ('MD', x),\n",
    "          'UTMy':  ('MD', y),\n",
    "         }\n",
    "\n",
    "log = xr.DataArray(well.GR,\n",
    "                   coords=coords,\n",
    "                   dims=['MD'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d7ebe3cc",
   "metadata": {},
   "outputs": [
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       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;GR&#x27; (MD: 15)&gt;\n",
       "array([32, 28, 27, 30, 35, 38, 32, 29, 26, 26, 23, 18, 16, 12, 18])\n",
       "Coordinates:\n",
       "  * MD       (MD) int64 100 110 120 130 140 150 160 ... 190 200 210 220 230 240\n",
       "    TVD      (MD) float64 99.35 109.2 118.6 127.1 ... 168.0 170.2 172.2 173.9\n",
       "    twt      (MD) float64 0.12 0.131 0.142 0.151 ... 0.217 0.218 0.219 0.219\n",
       "    iline    (MD) int64 50 50 50 50 50 50 50 50 51 51 52 52 53 53 54\n",
       "    xline    (MD) int64 77 77 77 77 77 77 77 76 76 76 76 76 75 75 75\n",
       "    UTMx     (MD) float64 1.05e+04 1.05e+04 1.05e+04 ... 1.053e+04 1.054e+04\n",
       "    UTMy     (MD) float64 1.251e+04 1.251e+04 1.25e+04 ... 1.242e+04 1.241e+04</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'GR'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>MD</span>: 15</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0cdf6251-56fb-4c3a-9605-f1a74537e710' class='xr-array-in' type='checkbox' checked><label for='section-0cdf6251-56fb-4c3a-9605-f1a74537e710' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>32 28 27 30 35 38 32 29 26 26 23 18 16 12 18</span></div><div class='xr-array-data'><pre>array([32, 28, 27, 30, 35, 38, 32, 29, 26, 26, 23, 18, 16, 12, 18])</pre></div></div></li><li class='xr-section-item'><input id='section-904c4f83-5b82-4907-86f9-cc841f01052e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-904c4f83-5b82-4907-86f9-cc841f01052e' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>MD</span></div><div class='xr-var-dims'>(MD)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>100 110 120 130 ... 210 220 230 240</div><input id='attrs-70f86d16-9ea3-409f-be66-5213d0747b53' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-70f86d16-9ea3-409f-be66-5213d0747b53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69afc18c-b7d4-4801-819c-6764e1241698' class='xr-var-data-in' type='checkbox'><label for='data-69afc18c-b7d4-4801-819c-6764e1241698' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230,\n",
       "       240])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TVD</span></div><div class='xr-var-dims'>(MD)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>99.35 109.2 118.6 ... 172.2 173.9</div><input id='attrs-de7dad5f-e66c-4c32-9edb-751ff60b3447' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-de7dad5f-e66c-4c32-9edb-751ff60b3447' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a88ae75c-2e18-4740-a9c3-cea4aa37e770' class='xr-var-data-in' type='checkbox'><label for='data-a88ae75c-2e18-4740-a9c3-cea4aa37e770' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 99.35262352, 109.20287862, 118.64954689, 127.10783968,\n",
       "       135.06550205, 142.40817745, 149.06665256, 154.77550978,\n",
       "       159.2076024 , 162.66890143, 165.55105291, 168.01274273,\n",
       "       170.21987513, 172.17076815, 173.86425876])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>twt</span></div><div class='xr-var-dims'>(MD)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.12 0.131 0.142 ... 0.219 0.219</div><input id='attrs-9ab8d82c-b99f-47a0-a643-142ad8e41a62' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9ab8d82c-b99f-47a0-a643-142ad8e41a62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-edd3ac34-2216-4a9a-9e8c-df430a3d5b08' class='xr-var-data-in' type='checkbox'><label for='data-edd3ac34-2216-4a9a-9e8c-df430a3d5b08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.12 , 0.131, 0.142, 0.151, 0.163, 0.172, 0.181, 0.192, 0.2  ,\n",
       "       0.208, 0.213, 0.217, 0.218, 0.219, 0.219])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>iline</span></div><div class='xr-var-dims'>(MD)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>50 50 50 50 50 ... 52 52 53 53 54</div><input id='attrs-262fe1c7-8b3a-4fdf-b568-ac8116a43e5d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-262fe1c7-8b3a-4fdf-b568-ac8116a43e5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ac4ac83-bd41-4d94-89fe-29b14fed1679' class='xr-var-data-in' type='checkbox'><label for='data-0ac4ac83-bd41-4d94-89fe-29b14fed1679' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([50, 50, 50, 50, 50, 50, 50, 50, 51, 51, 52, 52, 53, 53, 54])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xline</span></div><div class='xr-var-dims'>(MD)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>77 77 77 77 77 ... 76 76 75 75 75</div><input id='attrs-e1841184-fcbc-43e0-961e-119f9b111fc3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e1841184-fcbc-43e0-961e-119f9b111fc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b994dd7-bf29-46e7-8105-b04e472ee61a' class='xr-var-data-in' type='checkbox'><label for='data-1b994dd7-bf29-46e7-8105-b04e472ee61a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([77, 77, 77, 77, 77, 77, 77, 76, 76, 76, 76, 76, 75, 75, 75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UTMx</span></div><div class='xr-var-dims'>(MD)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.05e+04 1.05e+04 ... 1.054e+04</div><input id='attrs-75e2b5bb-fa89-4c57-934c-6bb431c15f75' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-75e2b5bb-fa89-4c57-934c-6bb431c15f75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b907b18-8b87-4c39-8a05-068f8b6deb7a' class='xr-var-data-in' type='checkbox'><label for='data-4b907b18-8b87-4c39-8a05-068f8b6deb7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([10500.        , 10499.7579693 , 10499.0751327 , 10498.70300275,\n",
       "       10499.32452795, 10500.66965382, 10502.46276588, 10505.05377257,\n",
       "       10508.74649919, 10513.11740523, 10517.79857492, 10522.71826269,\n",
       "       10527.81484653, 10532.97543483, 10538.08780929])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UTMy</span></div><div class='xr-var-dims'>(MD)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.251e+04 1.251e+04 ... 1.241e+04</div><input id='attrs-56d34767-e1e5-4802-9845-c5c142dfe4be' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-56d34767-e1e5-4802-9845-c5c142dfe4be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0da80060-6aff-429e-ae37-64a3ff5a0953' class='xr-var-data-in' type='checkbox'><label for='data-0da80060-6aff-429e-ae37-64a3ff5a0953' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([12506.05573664, 12507.24357514, 12504.27856966, 12498.97065223,\n",
       "       12492.95862254, 12486.31098971, 12479.07464213, 12471.29995764,\n",
       "       12463.14701887, 12454.84771947, 12446.49645404, 12438.14608611,\n",
       "       12429.83072148, 12421.4906963 , 12413.06517601])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2d9be18d-a3fe-4225-b1c9-7c7280c108e2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2d9be18d-a3fe-4225-b1c9-7c7280c108e2' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'GR' (MD: 15)>\n",
       "array([32, 28, 27, 30, 35, 38, 32, 29, 26, 26, 23, 18, 16, 12, 18])\n",
       "Coordinates:\n",
       "  * MD       (MD) int64 100 110 120 130 140 150 160 ... 190 200 210 220 230 240\n",
       "    TVD      (MD) float64 99.35 109.2 118.6 127.1 ... 168.0 170.2 172.2 173.9\n",
       "    twt      (MD) float64 0.12 0.131 0.142 0.151 ... 0.217 0.218 0.219 0.219\n",
       "    iline    (MD) int64 50 50 50 50 50 50 50 50 51 51 52 52 53 53 54\n",
       "    xline    (MD) int64 77 77 77 77 77 77 77 76 76 76 76 76 75 75 75\n",
       "    UTMx     (MD) float64 1.05e+04 1.05e+04 1.05e+04 ... 1.053e+04 1.054e+04\n",
       "    UTMy     (MD) float64 1.251e+04 1.251e+04 1.25e+04 ... 1.242e+04 1.241e+04"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96988da3",
   "metadata": {},
   "source": [
    "## Back-interpolate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a80da332",
   "metadata": {},
   "outputs": [
    {
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       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;amplitude&#x27; (MD: 15)&gt;\n",
       "array([31.50960949, 34.12308355, 43.66196924, 44.6205473 , 47.71998434,\n",
       "       52.93252089, 50.15644413, 56.62537331, 56.20786439, 61.22501453,\n",
       "       60.3777204 , 61.16075302, 61.19214089, 61.54500028, 59.22034153])\n",
       "Coordinates:\n",
       "    iline    (MD) int64 50 50 50 50 50 50 50 50 51 51 52 52 53 53 54\n",
       "    xline    (MD) int64 77 77 77 77 77 77 77 76 76 76 76 76 75 75 75\n",
       "    twt      (MD) float64 0.12 0.131 0.142 0.151 ... 0.217 0.218 0.219 0.219\n",
       "  * MD       (MD) int64 100 110 120 130 140 150 160 ... 190 200 210 220 230 240\n",
       "    TVD      (MD) float64 99.35 109.2 118.6 127.1 ... 168.0 170.2 172.2 173.9\n",
       "    UTMx     (MD) float64 1.05e+04 1.05e+04 1.05e+04 ... 1.053e+04 1.054e+04\n",
       "    UTMy     (MD) float64 1.251e+04 1.251e+04 1.25e+04 ... 1.242e+04 1.241e+04</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'amplitude'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>MD</span>: 15</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-62958b7d-4de1-44c4-b747-f04683a36ae6' class='xr-array-in' type='checkbox' checked><label for='section-62958b7d-4de1-44c4-b747-f04683a36ae6' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>31.51 34.12 43.66 44.62 47.72 52.93 ... 60.38 61.16 61.19 61.55 59.22</span></div><div class='xr-array-data'><pre>array([31.50960949, 34.12308355, 43.66196924, 44.6205473 , 47.71998434,\n",
       "       52.93252089, 50.15644413, 56.62537331, 56.20786439, 61.22501453,\n",
       "       60.3777204 , 61.16075302, 61.19214089, 61.54500028, 59.22034153])</pre></div></div></li><li class='xr-section-item'><input id='section-1ae8523b-7417-442a-a377-ce32131bf639' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1ae8523b-7417-442a-a377-ce32131bf639' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>iline</span></div><div class='xr-var-dims'>(MD)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>50 50 50 50 50 ... 52 52 53 53 54</div><input id='attrs-7818571f-7693-469c-8a40-85e21532a01f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7818571f-7693-469c-8a40-85e21532a01f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f485e23b-b396-4140-9cf8-5134dbb5ffed' class='xr-var-data-in' type='checkbox'><label for='data-f485e23b-b396-4140-9cf8-5134dbb5ffed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([50, 50, 50, 50, 50, 50, 50, 50, 51, 51, 52, 52, 53, 53, 54])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xline</span></div><div class='xr-var-dims'>(MD)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>77 77 77 77 77 ... 76 76 75 75 75</div><input id='attrs-feba2d07-500c-41b6-b19a-d2980f2347c0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-feba2d07-500c-41b6-b19a-d2980f2347c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ddcf49a-e891-41d6-88c1-154ac0f99c71' class='xr-var-data-in' type='checkbox'><label for='data-4ddcf49a-e891-41d6-88c1-154ac0f99c71' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([77, 77, 77, 77, 77, 77, 77, 76, 76, 76, 76, 76, 75, 75, 75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>twt</span></div><div class='xr-var-dims'>(MD)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.12 0.131 0.142 ... 0.219 0.219</div><input id='attrs-08f2f237-51d7-4e2c-abbc-2bbb7f597e17' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08f2f237-51d7-4e2c-abbc-2bbb7f597e17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9809d47d-81ac-496f-b117-b0f4344aabc0' class='xr-var-data-in' type='checkbox'><label for='data-9809d47d-81ac-496f-b117-b0f4344aabc0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.12 , 0.131, 0.142, 0.151, 0.163, 0.172, 0.181, 0.192, 0.2  ,\n",
       "       0.208, 0.213, 0.217, 0.218, 0.219, 0.219])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>MD</span></div><div class='xr-var-dims'>(MD)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>100 110 120 130 ... 210 220 230 240</div><input id='attrs-9e72784e-d198-4e69-8028-d87b7fd90f71' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e72784e-d198-4e69-8028-d87b7fd90f71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f12422f-ae51-4569-a336-3681316208e3' class='xr-var-data-in' type='checkbox'><label for='data-1f12422f-ae51-4569-a336-3681316208e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230,\n",
       "       240])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TVD</span></div><div class='xr-var-dims'>(MD)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>99.35 109.2 118.6 ... 172.2 173.9</div><input id='attrs-52b1839f-5850-4f52-bfb3-afdc9fd4050c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-52b1839f-5850-4f52-bfb3-afdc9fd4050c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bc01776-b0c6-4590-a519-f05d20fca3e1' class='xr-var-data-in' type='checkbox'><label for='data-6bc01776-b0c6-4590-a519-f05d20fca3e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 99.35262352, 109.20287862, 118.64954689, 127.10783968,\n",
       "       135.06550205, 142.40817745, 149.06665256, 154.77550978,\n",
       "       159.2076024 , 162.66890143, 165.55105291, 168.01274273,\n",
       "       170.21987513, 172.17076815, 173.86425876])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UTMx</span></div><div class='xr-var-dims'>(MD)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.05e+04 1.05e+04 ... 1.054e+04</div><input id='attrs-3095bd27-0aff-4a5f-80c8-d8e0ee61c76a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3095bd27-0aff-4a5f-80c8-d8e0ee61c76a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb38ad98-2110-4779-a95d-0fb80f2c3550' class='xr-var-data-in' type='checkbox'><label for='data-fb38ad98-2110-4779-a95d-0fb80f2c3550' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([10500.        , 10499.7579693 , 10499.0751327 , 10498.70300275,\n",
       "       10499.32452795, 10500.66965382, 10502.46276588, 10505.05377257,\n",
       "       10508.74649919, 10513.11740523, 10517.79857492, 10522.71826269,\n",
       "       10527.81484653, 10532.97543483, 10538.08780929])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UTMy</span></div><div class='xr-var-dims'>(MD)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.251e+04 1.251e+04 ... 1.241e+04</div><input id='attrs-5eade477-7857-4bc3-8c6a-cec8b4ff06ef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5eade477-7857-4bc3-8c6a-cec8b4ff06ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f5fbb69-0a86-4db2-9af7-6f6fb2ff7fc5' class='xr-var-data-in' type='checkbox'><label for='data-2f5fbb69-0a86-4db2-9af7-6f6fb2ff7fc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([12506.05573664, 12507.24357514, 12504.27856966, 12498.97065223,\n",
       "       12492.95862254, 12486.31098971, 12479.07464213, 12471.29995764,\n",
       "       12463.14701887, 12454.84771947, 12446.49645404, 12438.14608611,\n",
       "       12429.83072148, 12421.4906963 , 12413.06517601])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9e84ddd5-72d6-4b12-8bc6-6479dafa1ae8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9e84ddd5-72d6-4b12-8bc6-6479dafa1ae8' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'amplitude' (MD: 15)>\n",
       "array([31.50960949, 34.12308355, 43.66196924, 44.6205473 , 47.71998434,\n",
       "       52.93252089, 50.15644413, 56.62537331, 56.20786439, 61.22501453,\n",
       "       60.3777204 , 61.16075302, 61.19214089, 61.54500028, 59.22034153])\n",
       "Coordinates:\n",
       "    iline    (MD) int64 50 50 50 50 50 50 50 50 51 51 52 52 53 53 54\n",
       "    xline    (MD) int64 77 77 77 77 77 77 77 76 76 76 76 76 75 75 75\n",
       "    twt      (MD) float64 0.12 0.131 0.142 0.151 ... 0.217 0.218 0.219 0.219\n",
       "  * MD       (MD) int64 100 110 120 130 140 150 160 ... 190 200 210 220 230 240\n",
       "    TVD      (MD) float64 99.35 109.2 118.6 127.1 ... 168.0 170.2 172.2 173.9\n",
       "    UTMx     (MD) float64 1.05e+04 1.05e+04 1.05e+04 ... 1.053e+04 1.054e+04\n",
       "    UTMy     (MD) float64 1.251e+04 1.251e+04 1.25e+04 ... 1.242e+04 1.241e+04"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amplitude = seismic.interp(iline=log.iline, xline=log.xline, twt=log.twt)\n",
    "amplitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1f3d0a0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f092edf91c0>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "amplitude.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e26b9fe",
   "metadata": {},
   "source": [
    "This is essentially a new 'log' we can add back to the original dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "864de545",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DEPT</th>\n",
       "      <th>TIME</th>\n",
       "      <th>GR</th>\n",
       "      <th>amplitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>0.120</td>\n",
       "      <td>32</td>\n",
       "      <td>31.509609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110</td>\n",
       "      <td>0.131</td>\n",
       "      <td>28</td>\n",
       "      <td>34.123084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>120</td>\n",
       "      <td>0.142</td>\n",
       "      <td>27</td>\n",
       "      <td>43.661969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>130</td>\n",
       "      <td>0.151</td>\n",
       "      <td>30</td>\n",
       "      <td>44.620547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>140</td>\n",
       "      <td>0.163</td>\n",
       "      <td>35</td>\n",
       "      <td>47.719984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>150</td>\n",
       "      <td>0.172</td>\n",
       "      <td>38</td>\n",
       "      <td>52.932521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>160</td>\n",
       "      <td>0.181</td>\n",
       "      <td>32</td>\n",
       "      <td>50.156444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>170</td>\n",
       "      <td>0.192</td>\n",
       "      <td>29</td>\n",
       "      <td>56.625373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>180</td>\n",
       "      <td>0.200</td>\n",
       "      <td>26</td>\n",
       "      <td>56.207864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>190</td>\n",
       "      <td>0.208</td>\n",
       "      <td>26</td>\n",
       "      <td>61.225015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>200</td>\n",
       "      <td>0.213</td>\n",
       "      <td>23</td>\n",
       "      <td>60.377720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>210</td>\n",
       "      <td>0.217</td>\n",
       "      <td>18</td>\n",
       "      <td>61.160753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>220</td>\n",
       "      <td>0.218</td>\n",
       "      <td>16</td>\n",
       "      <td>61.192141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>230</td>\n",
       "      <td>0.219</td>\n",
       "      <td>12</td>\n",
       "      <td>61.545000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>240</td>\n",
       "      <td>0.219</td>\n",
       "      <td>18</td>\n",
       "      <td>59.220342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    DEPT   TIME  GR  amplitude\n",
       "0    100  0.120  32  31.509609\n",
       "1    110  0.131  28  34.123084\n",
       "2    120  0.142  27  43.661969\n",
       "3    130  0.151  30  44.620547\n",
       "4    140  0.163  35  47.719984\n",
       "5    150  0.172  38  52.932521\n",
       "6    160  0.181  32  50.156444\n",
       "7    170  0.192  29  56.625373\n",
       "8    180  0.200  26  56.207864\n",
       "9    190  0.208  26  61.225015\n",
       "10   200  0.213  23  60.377720\n",
       "11   210  0.217  18  61.160753\n",
       "12   220  0.218  16  61.192141\n",
       "13   230  0.219  12  61.545000\n",
       "14   240  0.219  18  59.220342"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well['amplitude'] = amplitude\n",
    "\n",
    "well"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23df6d3c",
   "metadata": {},
   "source": [
    "Away we go with seismic analysis!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "69691818",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f092ed6cfa0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(well.GR, well.amplitude)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f618176c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}