b_value_estimation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This script estimates the b values from the TexNet catalog\n",
    "# Author: Kaushik Pradhan (kpradhan@miners.utep.edu)\n",
    "# Last updated - 05/10/2024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from scipy.optimize import minimize\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Local Magnitude</th>\n",
       "      <th>Moment Magnitude</th>\n",
       "      <th>Latitude (WGS84)</th>\n",
       "      <th>Latitude Error (km)</th>\n",
       "      <th>Longitude (WGS84)</th>\n",
       "      <th>Longitude Error (km)</th>\n",
       "      <th>Depth of Hypocenter (Km.  Rel to MSL)</th>\n",
       "      <th>Depth of Hypocenter (Km. Rel to Ground Surface)</th>\n",
       "      <th>Depth Uncertainty (Km. Corresponds to 1 st dev)</th>\n",
       "      <th>RMS</th>\n",
       "      <th>...</th>\n",
       "      <th>Nodal Plane 1 - Strike</th>\n",
       "      <th>Nodal Plane 1 - Dip</th>\n",
       "      <th>Nodal Plane 1 - Rake</th>\n",
       "      <th>Nodal Plane 2 - Strike</th>\n",
       "      <th>Nodal Plane 2 - Dip</th>\n",
       "      <th>Nodal Plane 2 - Rake</th>\n",
       "      <th>Focal Mechanism - Azimuthal Gap</th>\n",
       "      <th>Focal Mechanism - Misfit</th>\n",
       "      <th>Maximum Station Distance (km)</th>\n",
       "      <th>Minimum Station Distance (km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>24822.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>24822.000000</td>\n",
       "      <td>24822.000000</td>\n",
       "      <td>24822.000000</td>\n",
       "      <td>24822.000000</td>\n",
       "      <td>24822.000000</td>\n",
       "      <td>24822.000000</td>\n",
       "      <td>24822.000000</td>\n",
       "      <td>24822.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>24822.000000</td>\n",
       "      <td>2908.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.848606</td>\n",
       "      <td>3.886957</td>\n",
       "      <td>31.552585</td>\n",
       "      <td>0.813001</td>\n",
       "      <td>-103.237675</td>\n",
       "      <td>0.749295</td>\n",
       "      <td>6.186838</td>\n",
       "      <td>7.131355</td>\n",
       "      <td>1.585759</td>\n",
       "      <td>0.229313</td>\n",
       "      <td>...</td>\n",
       "      <td>145.151899</td>\n",
       "      <td>58.746835</td>\n",
       "      <td>-45.531646</td>\n",
       "      <td>177.531646</td>\n",
       "      <td>55.588608</td>\n",
       "      <td>-36.031646</td>\n",
       "      <td>107.083026</td>\n",
       "      <td>0.277959</td>\n",
       "      <td>86.406529</td>\n",
       "      <td>5.382143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.608922</td>\n",
       "      <td>0.436964</td>\n",
       "      <td>0.778395</td>\n",
       "      <td>1.652299</td>\n",
       "      <td>1.741507</td>\n",
       "      <td>1.250060</td>\n",
       "      <td>1.974341</td>\n",
       "      <td>2.052153</td>\n",
       "      <td>0.906521</td>\n",
       "      <td>0.086200</td>\n",
       "      <td>...</td>\n",
       "      <td>82.633422</td>\n",
       "      <td>20.614427</td>\n",
       "      <td>89.281040</td>\n",
       "      <td>121.260191</td>\n",
       "      <td>19.933689</td>\n",
       "      <td>99.786678</td>\n",
       "      <td>54.227949</td>\n",
       "      <td>0.190858</td>\n",
       "      <td>61.724266</td>\n",
       "      <td>5.084319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>28.225700</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>-106.003600</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>-0.900000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-178.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-179.000000</td>\n",
       "      <td>41.192801</td>\n",
       "      <td>0.096772</td>\n",
       "      <td>0.057707</td>\n",
       "      <td>0.053489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>31.406500</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>-104.300275</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>...</td>\n",
       "      <td>92.250000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>-107.000000</td>\n",
       "      <td>43.250000</td>\n",
       "      <td>42.250000</td>\n",
       "      <td>-104.500000</td>\n",
       "      <td>70.437708</td>\n",
       "      <td>0.147308</td>\n",
       "      <td>36.242117</td>\n",
       "      <td>2.775968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.900000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>31.641900</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>-103.975650</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>...</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>59.500000</td>\n",
       "      <td>-71.000000</td>\n",
       "      <td>227.500000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>-68.000000</td>\n",
       "      <td>90.751899</td>\n",
       "      <td>0.206923</td>\n",
       "      <td>78.693490</td>\n",
       "      <td>4.244110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.200000</td>\n",
       "      <td>3.975000</td>\n",
       "      <td>31.698775</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>-103.270125</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>...</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>76.750000</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>276.750000</td>\n",
       "      <td>71.750000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>127.426466</td>\n",
       "      <td>0.340810</td>\n",
       "      <td>122.578007</td>\n",
       "      <td>6.730274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.400000</td>\n",
       "      <td>5.300000</td>\n",
       "      <td>36.263100</td>\n",
       "      <td>168.400000</td>\n",
       "      <td>-93.934700</td>\n",
       "      <td>84.700000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>26.900000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>...</td>\n",
       "      <td>359.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>267.028454</td>\n",
       "      <td>0.904890</td>\n",
       "      <td>710.796869</td>\n",
       "      <td>81.613691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Local Magnitude  Moment Magnitude  Latitude (WGS84)  \\\n",
       "count     24822.000000         46.000000      24822.000000   \n",
       "mean          1.848606          3.886957         31.552585   \n",
       "std           0.608922          0.436964          0.778395   \n",
       "min          -1.000000          3.200000         28.225700   \n",
       "25%           1.500000          3.700000         31.406500   \n",
       "50%           1.900000          3.800000         31.641900   \n",
       "75%           2.200000          3.975000         31.698775   \n",
       "max           5.400000          5.300000         36.263100   \n",
       "\n",
       "       Latitude Error (km)  Longitude (WGS84)  Longitude Error (km)  \\\n",
       "count         24822.000000       24822.000000          24822.000000   \n",
       "mean              0.813001        -103.237675              0.749295   \n",
       "std               1.652299           1.741507              1.250060   \n",
       "min               0.200000        -106.003600              0.100000   \n",
       "25%               0.600000        -104.300275              0.500000   \n",
       "50%               0.700000        -103.975650              0.600000   \n",
       "75%               0.900000        -103.270125              0.900000   \n",
       "max             168.400000         -93.934700             84.700000   \n",
       "\n",
       "       Depth of Hypocenter (Km.  Rel to MSL)  \\\n",
       "count                           24822.000000   \n",
       "mean                                6.186838   \n",
       "std                                 1.974341   \n",
       "min                                -0.900000   \n",
       "25%                                 5.100000   \n",
       "50%                                 6.400000   \n",
       "75%                                 7.300000   \n",
       "max                                26.000000   \n",
       "\n",
       "       Depth of Hypocenter (Km. Rel to Ground Surface)  \\\n",
       "count                                     24822.000000   \n",
       "mean                                          7.131355   \n",
       "std                                           2.052153   \n",
       "min                                          -0.000000   \n",
       "25%                                           6.000000   \n",
       "50%                                           7.400000   \n",
       "75%                                           8.400000   \n",
       "max                                          26.900000   \n",
       "\n",
       "       Depth Uncertainty (Km. Corresponds to 1 st dev)           RMS  ...  \\\n",
       "count                                     24822.000000  24822.000000  ...   \n",
       "mean                                          1.585759      0.229313  ...   \n",
       "std                                           0.906521      0.086200  ...   \n",
       "min                                           0.300000      0.000000  ...   \n",
       "25%                                           1.000000      0.200000  ...   \n",
       "50%                                           1.500000      0.200000  ...   \n",
       "75%                                           1.900000      0.300000  ...   \n",
       "max                                          31.000000      1.100000  ...   \n",
       "\n",
       "       Nodal Plane 1 - Strike  Nodal Plane 1 - Dip  Nodal Plane 1 - Rake  \\\n",
       "count              158.000000           158.000000            158.000000   \n",
       "mean               145.151899            58.746835            -45.531646   \n",
       "std                 82.633422            20.614427             89.281040   \n",
       "min                  0.000000             2.000000           -178.000000   \n",
       "25%                 92.250000            45.000000           -107.000000   \n",
       "50%                113.000000            59.500000            -71.000000   \n",
       "75%                188.500000            76.750000              5.750000   \n",
       "max                359.000000            90.000000            180.000000   \n",
       "\n",
       "       Nodal Plane 2 - Strike  Nodal Plane 2 - Dip  Nodal Plane 2 - Rake  \\\n",
       "count              158.000000           158.000000            158.000000   \n",
       "mean               177.531646            55.588608            -36.031646   \n",
       "std                121.260191            19.933689             99.786678   \n",
       "min                  0.000000             1.000000           -179.000000   \n",
       "25%                 43.250000            42.250000           -104.500000   \n",
       "50%                227.500000            54.000000            -68.000000   \n",
       "75%                276.750000            71.750000              5.500000   \n",
       "max                360.000000            90.000000            179.000000   \n",
       "\n",
       "       Focal Mechanism - Azimuthal Gap  Focal Mechanism - Misfit  \\\n",
       "count                        66.000000                 66.000000   \n",
       "mean                        107.083026                  0.277959   \n",
       "std                          54.227949                  0.190858   \n",
       "min                          41.192801                  0.096772   \n",
       "25%                          70.437708                  0.147308   \n",
       "50%                          90.751899                  0.206923   \n",
       "75%                         127.426466                  0.340810   \n",
       "max                         267.028454                  0.904890   \n",
       "\n",
       "       Maximum Station Distance (km)  Minimum Station Distance (km)  \n",
       "count                   24822.000000                    2908.000000  \n",
       "mean                       86.406529                       5.382143  \n",
       "std                        61.724266                       5.084319  \n",
       "min                         0.057707                       0.053489  \n",
       "25%                        36.242117                       2.775968  \n",
       "50%                        78.693490                       4.244110  \n",
       "75%                       122.578007                       6.730274  \n",
       "max                       710.796869                      81.613691  \n",
       "\n",
       "[8 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading the texnet catalog data\n",
    "tex_net_events = pd.read_csv('texnet_events.csv')\n",
    "tex_net_events.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EventID</th>\n",
       "      <th>Evaluation Status</th>\n",
       "      <th>Origin Date</th>\n",
       "      <th>Origin Time</th>\n",
       "      <th>Local Magnitude</th>\n",
       "      <th>Moment Magnitude</th>\n",
       "      <th>Latitude (WGS84)</th>\n",
       "      <th>Latitude Error (km)</th>\n",
       "      <th>Longitude (WGS84)</th>\n",
       "      <th>Longitude Error (km)</th>\n",
       "      <th>...</th>\n",
       "      <th>Nodal Plane 1 - Rake</th>\n",
       "      <th>Nodal Plane 2 - Strike</th>\n",
       "      <th>Nodal Plane 2 - Dip</th>\n",
       "      <th>Nodal Plane 2 - Rake</th>\n",
       "      <th>Focal Mechanism Method ID</th>\n",
       "      <th>Focal Mechanism - Azimuthal Gap</th>\n",
       "      <th>Focal Mechanism - Misfit</th>\n",
       "      <th>Maximum Station Distance (km)</th>\n",
       "      <th>Minimum Station Distance (km)</th>\n",
       "      <th>Last Updated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>texnet2024dikg</td>\n",
       "      <td>final</td>\n",
       "      <td>2024-02-17</td>\n",
       "      <td>06:52:16</td>\n",
       "      <td>3.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.9719</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-97.9650</td>\n",
       "      <td>0.9</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.292870</td>\n",
       "      <td>6.755903</td>\n",
       "      <td>2024-02-19 17:26:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>texnet2024dijs</td>\n",
       "      <td>final</td>\n",
       "      <td>2024-02-17</td>\n",
       "      <td>06:34:18</td>\n",
       "      <td>3.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.9627</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-97.9360</td>\n",
       "      <td>1.1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.994875</td>\n",
       "      <td>4.425812</td>\n",
       "      <td>2024-02-19 17:25:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>texnet2024dijp</td>\n",
       "      <td>final</td>\n",
       "      <td>2024-02-17</td>\n",
       "      <td>06:32:54</td>\n",
       "      <td>4.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.9581</td>\n",
       "      <td>0.6</td>\n",
       "      <td>-98.0053</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.083000</td>\n",
       "      <td>2.722672</td>\n",
       "      <td>2024-02-19 17:22:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>texnet2024dijf</td>\n",
       "      <td>final</td>\n",
       "      <td>2024-02-17</td>\n",
       "      <td>06:20:21</td>\n",
       "      <td>4.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.9545</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-98.0053</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.689021</td>\n",
       "      <td>2.563408</td>\n",
       "      <td>2024-02-19 17:24:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>texnet2024diek</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>2024-02-17</td>\n",
       "      <td>03:53:14</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.6708</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-104.4363</td>\n",
       "      <td>1.4</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>111.074852</td>\n",
       "      <td>1.672291</td>\n",
       "      <td>2024-02-17 04:33:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          EventID Evaluation Status Origin Date Origin Time  Local Magnitude  \\\n",
       "0  texnet2024dikg             final  2024-02-17    06:52:16              3.3   \n",
       "1  texnet2024dijs             final  2024-02-17    06:34:18              3.7   \n",
       "2  texnet2024dijp             final  2024-02-17    06:32:54              4.7   \n",
       "3  texnet2024dijf             final  2024-02-17    06:20:21              4.4   \n",
       "4  texnet2024diek         Automatic  2024-02-17    03:53:14              1.5   \n",
       "\n",
       "   Moment Magnitude  Latitude (WGS84)  Latitude Error (km)  Longitude (WGS84)  \\\n",
       "0               NaN           28.9719                  1.0           -97.9650   \n",
       "1               NaN           28.9627                  1.7           -97.9360   \n",
       "2               NaN           28.9581                  0.6           -98.0053   \n",
       "3               NaN           28.9545                  0.5           -98.0053   \n",
       "4               NaN           31.6708                  1.5          -104.4363   \n",
       "\n",
       "   Longitude Error (km)  ...  Nodal Plane 1 - Rake  Nodal Plane 2 - Strike  \\\n",
       "0                   0.9  ...                   NaN                     NaN   \n",
       "1                   1.1  ...                   NaN                     NaN   \n",
       "2                   0.6  ...                   NaN                     NaN   \n",
       "3                   0.5  ...                   NaN                     NaN   \n",
       "4                   1.4  ...                   NaN                     NaN   \n",
       "\n",
       "   Nodal Plane 2 - Dip  Nodal Plane 2 - Rake  Focal Mechanism Method ID  \\\n",
       "0                  NaN                   NaN                        NaN   \n",
       "1                  NaN                   NaN                        NaN   \n",
       "2                  NaN                   NaN                        NaN   \n",
       "3                  NaN                   NaN                        NaN   \n",
       "4                  NaN                   NaN                        NaN   \n",
       "\n",
       "   Focal Mechanism - Azimuthal Gap Focal Mechanism - Misfit  \\\n",
       "0                              NaN                      NaN   \n",
       "1                              NaN                      NaN   \n",
       "2                              NaN                      NaN   \n",
       "3                              NaN                      NaN   \n",
       "4                              NaN                      NaN   \n",
       "\n",
       "  Maximum Station Distance (km) Minimum Station Distance (km)  \\\n",
       "0                     59.292870                      6.755903   \n",
       "1                     57.994875                      4.425812   \n",
       "2                     55.083000                      2.722672   \n",
       "3                     51.689021                      2.563408   \n",
       "4                    111.074852                      1.672291   \n",
       "\n",
       "          Last Updated  \n",
       "0  2024-02-19 17:26:46  \n",
       "1  2024-02-19 17:25:56  \n",
       "2  2024-02-19 17:22:46  \n",
       "3  2024-02-19 17:24:48  \n",
       "4  2024-02-17 04:33:21  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tex_net_events.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['EventID', 'Evaluation Status', 'Origin Date', 'Origin Time',\n",
       "       'Local Magnitude', 'Moment Magnitude', 'Latitude (WGS84)',\n",
       "       'Latitude Error (km)', 'Longitude (WGS84)', 'Longitude Error (km)',\n",
       "       'Depth of Hypocenter (Km.  Rel to MSL)',\n",
       "       'Depth of Hypocenter (Km. Rel to Ground Surface)',\n",
       "       'Depth Uncertainty (Km. Corresponds to 1 st dev)', 'RMS',\n",
       "       'UsedPhaseCount', 'UsedStationCount', 'Author', 'EventType',\n",
       "       'LocationName', 'Nodal Plane 1 - Strike', 'Nodal Plane 1 - Dip',\n",
       "       'Nodal Plane 1 - Rake', 'Nodal Plane 2 - Strike', 'Nodal Plane 2 - Dip',\n",
       "       'Nodal Plane 2 - Rake', 'Focal Mechanism Method ID',\n",
       "       'Focal Mechanism - Azimuthal Gap', 'Focal Mechanism - Misfit',\n",
       "       'Maximum Station Distance (km)', 'Minimum Station Distance (km)',\n",
       "       'Last Updated'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tex_net_events.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the magnitudes only in a list\n",
    "magnitudes = tex_net_events['Local Magnitude'].values\n",
    "\n",
    "sns.set(style='whitegrid', palette='colorblind')\n",
    "\n",
    "plt.figure(figsize=(6, 4)) \n",
    "sns.histplot(magnitudes, bins=20, kde=True) \n",
    "plt.xlabel('Local Magnitude', fontsize=12)  \n",
    "plt.ylabel('Frequency', fontsize=12)  \n",
    "plt.title('Histogram of Local Magnitude', fontsize=12) \n",
    "plt.xticks(fontsize=12)  \n",
    "plt.yticks(fontsize=12)  \n",
    "plt.grid(True) \n",
    "sns.despine() \n",
    "plt.tight_layout()  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the magnitudes only in a list\n",
    "magnitudes = tex_net_events['Local Magnitude'].values\n",
    "\n",
    "sns.set(style='whitegrid', palette='colorblind')\n",
    "\n",
    "plt.figure(figsize=(6, 4)) \n",
    "sns.histplot(magnitudes, bins=20, kde=True) \n",
    "plt.xlabel('Local Magnitude', fontsize=12)  \n",
    "plt.ylabel('Frequency', fontsize=12)  \n",
    "plt.title('Histogram of Local Magnitude', fontsize=12) \n",
    "plt.xticks(fontsize=12)  \n",
    "plt.yticks(fontsize=12)  \n",
    "plt.grid(True) \n",
    "sns.despine() \n",
    "plt.yscale('log')  \n",
    "plt.tight_layout()  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''The Gutenberg-Richter magnitude-frequency relationship for earthquakes is given by \n",
    "log(N(M)) = a - bM where M is magnitude, N(M) is the number of earthquakes of magnitude ≥ M, \n",
    "a is a constant, and b determines the relative number of earthquakes of different magnitudes. \n",
    " \n",
    "The maximum likelihood method of Aki (1965) is the most accurate way to calculate b, but large data \n",
    "sets are required. Monte Carlo simulations and equations from Aki (1965) indicate that a minimum of 2000 earthquakes \n",
    "are needed to calculate b to within 0.05 at 98% confidence. Also, as is well known, significant problems for b value \n",
    "calculation can be caused by choosing a minimum magnitude of completeness, M_C, that is too low. Often M_C is solved \n",
    "for by visual or quantitative inspection of logarithmic cumulative magnitude frequency plots for the point of maximum \n",
    "curvature. \n",
    "'''\n",
    "\n",
    "# for each m we need to N(M) and then log(N(M))\n",
    "\n",
    "mag = -1.0\n",
    "mag_store = []\n",
    "log_nm_store = []\n",
    "\n",
    "while mag < np.max(magnitudes):\n",
    "    mag_store.append(mag)\n",
    "    \n",
    "    log_nm_store.append(len([i for i in magnitudes if(i) >= mag]))\n",
    "\n",
    "    mag = mag + 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(mag_store, log_nm_store)\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.7175122523969178 4.672889373590741 -0.9390664970342958 6.443146994619819e-31 0.033089265311145005\n",
      "The b value is  0.7175122523969178\n"
     ]
    }
   ],
   "source": [
    "# find the best fitting line\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(mag_store,np.log(log_nm_store)/np.log(10))\n",
    "print(slope, intercept, r_value, p_value, std_err)\n",
    "print('The b value is ', -slope)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting scatter data and best fitting line\n",
    "pred_y =  [i * slope + intercept for i in mag_store]\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter(mag_store, np.log(log_nm_store))\n",
    "plt.plot(mag_store, np.log(pred_y), color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# only take magnitude greater than 2\n",
    "new_magnitudes = [i for i in magnitudes if i>=2.0]\n",
    "\n",
    "sns.set(style='whitegrid', palette='colorblind')\n",
    "\n",
    "plt.figure(figsize=(6, 4)) \n",
    "sns.histplot(new_magnitudes, bins=20, kde=True) \n",
    "plt.xlabel('Local Magnitude', fontsize=12)  \n",
    "plt.ylabel('Frequency', fontsize=12)  \n",
    "plt.title('Histogram of Local Magnitude', fontsize=12) \n",
    "plt.xticks(fontsize=12)  \n",
    "plt.yticks(fontsize=12)  \n",
    "plt.grid(True) \n",
    "sns.despine() \n",
    "plt.yscale('log')  \n",
    "plt.tight_layout()  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "mag = 2.0\n",
    "mag_store = []\n",
    "log_nm_store = []\n",
    "\n",
    "while mag < np.max(magnitudes):\n",
    "    mag_store.append(mag)\n",
    "    \n",
    "    log_nm_store.append(len([i for i in magnitudes if(i) >= mag]))\n",
    "\n",
    "    mag = mag + 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(mag_store, log_nm_store)\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.1552487264561473 6.308563971309367 -0.9984811385481098 3.950175359639074e-43 0.011096544777651858\n",
      "The b value is  1.1552487264561473\n"
     ]
    }
   ],
   "source": [
    "# find the best fitting line\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(mag_store,np.log(log_nm_store)/np.log(10))\n",
    "print(slope, intercept, r_value, p_value, std_err)\n",
    "print('The b value is ', -slope)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting scatter data and best fitting line\n",
    "pred_y =  [i * slope + intercept for i in mag_store]\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter(mag_store, np.log(log_nm_store))\n",
    "plt.plot(mag_store, np.log(pred_y), color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting scatter data and best fitting line\n",
    "pred_y =  [i * slope + intercept for i in mag_store]\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter(mag_store, (log_nm_store))\n",
    "plt.plot(mag_store, (pred_y), color='red')\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}