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RiskEngineering notebooks
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Analyzing earthquake data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "<img src=\"https://risk-engineering.org/static/img/logo-RE.png\" width=\"100\" alt=\"\" style=\"float:right;margin:15px;\">\n", | |
| "\n", | |
| "This notebook is an element of the [risk-engineering.org courseware](https://risk-engineering.org/). It can be distributed under the terms of the [Creative Commons Attribution-ShareAlike licence](https://creativecommons.org/licenses/by-sa/4.0/).\n", | |
| "\n", | |
| "Author: Eric Marsden <[email protected]>. \n", | |
| "\n", | |
| "---\n", | |
| "\n", | |
| "This notebook contains an introduction to the use of Python and SciPy to analyze data concerning earthquakes. See the [associated course materials](https://risk-engineering.org/statistical-modelling/) for background information and to download this content as a Jupyter notebook." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import numpy\n", | |
| "import scipy.stats\n", | |
| "import pandas\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "plt.style.use(\"bmh\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "The USGS provides data on earthquakes that occurred throughout the world at https://earthquake.usgs.gov/earthquakes/search/. For convenience, we have extracted data for 2017 earthquakes of magnitude larger than 5 in CSV format." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "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>time</th>\n", | |
| " <th>latitude</th>\n", | |
| " <th>longitude</th>\n", | |
| " <th>depth</th>\n", | |
| " <th>mag</th>\n", | |
| " <th>magType</th>\n", | |
| " <th>nst</th>\n", | |
| " <th>gap</th>\n", | |
| " <th>dmin</th>\n", | |
| " <th>rms</th>\n", | |
| " <th>...</th>\n", | |
| " <th>updated</th>\n", | |
| " <th>place</th>\n", | |
| " <th>type</th>\n", | |
| " <th>horizontalError</th>\n", | |
| " <th>depthError</th>\n", | |
| " <th>magError</th>\n", | |
| " <th>magNst</th>\n", | |
| " <th>status</th>\n", | |
| " <th>locationSource</th>\n", | |
| " <th>magSource</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>2017-12-31T20:59:02.500Z</td>\n", | |
| " <td>-53.0266</td>\n", | |
| " <td>-118.3468</td>\n", | |
| " <td>10.00</td>\n", | |
| " <td>5.1</td>\n", | |
| " <td>mb</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>37.0</td>\n", | |
| " <td>30.620</td>\n", | |
| " <td>0.85</td>\n", | |
| " <td>...</td>\n", | |
| " <td>2018-03-17T01:54:41.040Z</td>\n", | |
| " <td>Southern East Pacific Rise</td>\n", | |
| " <td>earthquake</td>\n", | |
| " <td>13.7</td>\n", | |
| " <td>1.8</td>\n", | |
| " <td>0.053</td>\n", | |
| " <td>117.0</td>\n", | |
| " <td>reviewed</td>\n", | |
| " <td>us</td>\n", | |
| " <td>us</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>2017-12-31T20:27:49.390Z</td>\n", | |
| " <td>-8.1161</td>\n", | |
| " <td>68.0644</td>\n", | |
| " <td>10.00</td>\n", | |
| " <td>5.1</td>\n", | |
| " <td>mww</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>59.0</td>\n", | |
| " <td>12.965</td>\n", | |
| " <td>0.72</td>\n", | |
| " <td>...</td>\n", | |
| " <td>2018-03-17T01:54:41.040Z</td>\n", | |
| " <td>Chagos Archipelago region</td>\n", | |
| " <td>earthquake</td>\n", | |
| " <td>6.5</td>\n", | |
| " <td>1.8</td>\n", | |
| " <td>0.062</td>\n", | |
| " <td>25.0</td>\n", | |
| " <td>reviewed</td>\n", | |
| " <td>us</td>\n", | |
| " <td>us</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>2017-12-31T14:53:31.580Z</td>\n", | |
| " <td>5.4949</td>\n", | |
| " <td>124.9006</td>\n", | |
| " <td>30.80</td>\n", | |
| " <td>5.1</td>\n", | |
| " <td>mww</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>60.0</td>\n", | |
| " <td>1.703</td>\n", | |
| " <td>1.01</td>\n", | |
| " <td>...</td>\n", | |
| " <td>2018-03-17T01:54:40.040Z</td>\n", | |
| " <td>40km S of Daliao, Philippines</td>\n", | |
| " <td>earthquake</td>\n", | |
| " <td>6.7</td>\n", | |
| " <td>4.0</td>\n", | |
| " <td>0.073</td>\n", | |
| " <td>18.0</td>\n", | |
| " <td>reviewed</td>\n", | |
| " <td>us</td>\n", | |
| " <td>us</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>2017-12-31T14:51:58.200Z</td>\n", | |
| " <td>-11.8634</td>\n", | |
| " <td>165.4973</td>\n", | |
| " <td>9.55</td>\n", | |
| " <td>5.1</td>\n", | |
| " <td>mb</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>74.0</td>\n", | |
| " <td>5.963</td>\n", | |
| " <td>1.03</td>\n", | |
| " <td>...</td>\n", | |
| " <td>2018-03-17T01:54:40.040Z</td>\n", | |
| " <td>132km SSW of Lata, Solomon Islands</td>\n", | |
| " <td>earthquake</td>\n", | |
| " <td>9.1</td>\n", | |
| " <td>4.1</td>\n", | |
| " <td>0.059</td>\n", | |
| " <td>92.0</td>\n", | |
| " <td>reviewed</td>\n", | |
| " <td>us</td>\n", | |
| " <td>us</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>2017-12-31T03:48:57.420Z</td>\n", | |
| " <td>29.6759</td>\n", | |
| " <td>129.3045</td>\n", | |
| " <td>162.80</td>\n", | |
| " <td>5.0</td>\n", | |
| " <td>mww</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>89.0</td>\n", | |
| " <td>2.972</td>\n", | |
| " <td>0.77</td>\n", | |
| " <td>...</td>\n", | |
| " <td>2018-03-17T01:54:40.040Z</td>\n", | |
| " <td>146km N of Naze, Japan</td>\n", | |
| " <td>earthquake</td>\n", | |
| " <td>7.6</td>\n", | |
| " <td>4.2</td>\n", | |
| " <td>0.065</td>\n", | |
| " <td>23.0</td>\n", | |
| " <td>reviewed</td>\n", | |
| " <td>us</td>\n", | |
| " <td>us</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>5 rows × 22 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " time latitude longitude depth mag magType nst \\\n", | |
| "0 2017-12-31T20:59:02.500Z -53.0266 -118.3468 10.00 5.1 mb NaN \n", | |
| "1 2017-12-31T20:27:49.390Z -8.1161 68.0644 10.00 5.1 mww NaN \n", | |
| "2 2017-12-31T14:53:31.580Z 5.4949 124.9006 30.80 5.1 mww NaN \n", | |
| "3 2017-12-31T14:51:58.200Z -11.8634 165.4973 9.55 5.1 mb NaN \n", | |
| "4 2017-12-31T03:48:57.420Z 29.6759 129.3045 162.80 5.0 mww NaN \n", | |
| "\n", | |
| " gap dmin rms ... updated \\\n", | |
| "0 37.0 30.620 0.85 ... 2018-03-17T01:54:41.040Z \n", | |
| "1 59.0 12.965 0.72 ... 2018-03-17T01:54:41.040Z \n", | |
| "2 60.0 1.703 1.01 ... 2018-03-17T01:54:40.040Z \n", | |
| "3 74.0 5.963 1.03 ... 2018-03-17T01:54:40.040Z \n", | |
| "4 89.0 2.972 0.77 ... 2018-03-17T01:54:40.040Z \n", | |
| "\n", | |
| " place type horizontalError depthError \\\n", | |
| "0 Southern East Pacific Rise earthquake 13.7 1.8 \n", | |
| "1 Chagos Archipelago region earthquake 6.5 1.8 \n", | |
| "2 40km S of Daliao, Philippines earthquake 6.7 4.0 \n", | |
| "3 132km SSW of Lata, Solomon Islands earthquake 9.1 4.1 \n", | |
| "4 146km N of Naze, Japan earthquake 7.6 4.2 \n", | |
| "\n", | |
| " magError magNst status locationSource magSource \n", | |
| "0 0.053 117.0 reviewed us us \n", | |
| "1 0.062 25.0 reviewed us us \n", | |
| "2 0.073 18.0 reviewed us us \n", | |
| "3 0.059 92.0 reviewed us us \n", | |
| "4 0.065 23.0 reviewed us us \n", | |
| "\n", | |
| "[5 rows x 22 columns]" | |
| ] | |
| }, | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "data = pandas.read_csv(\"https://risk-engineering.org/static/data/earthquakes-2017.csv\")\n", | |
| "data.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "1559" | |
| ] | |
| }, | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "len(data)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Let's clean up the data a little." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "data.time = pandas.to_datetime(data.time)\n", | |
| "data.sort_values(\"time\", inplace=True)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Plot the data to see whether we can identify any visible patterns. " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 1440x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "dark" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "fig = plt.figure()\n", | |
| "fig.set_size_inches(20, 4)\n", | |
| "plt.plot(data.time, data.mag, \".\");\n", | |
| "plt.ylabel(\"Magnitude\");" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "There's no visible trend in this time series. \n", | |
| "\n", | |
| "Let's examine the distribution of earthquake magnitudes (recall that we only have data for earthquakes of magnitude larger than 5). " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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