{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
" 2010 Port-au-Prince, Haiti Earthquake \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The purpose of this Jupyter notebook is to investigate the events surrounding the M 7.0 earthquake that took place near Port-au-Prince, Haiti, on January 12, 2010 and to see why this earthquake was especially devastating."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One potential reason for the massive destruction and loss of life could have been the lack of warning and preparedness. It was known that the increasing build-up of stress in the Caribbean region -- specifically the fault zone on the south side of Hispaniola -- posed \"a major seismic hazard,\" and could generate an earthquake as large as M 7.2, as described at the 18th Caribbean Geological Conference in March 2008 in Santo Domingo, Dominican Republic. However, were there any more immediate signs that could have prepared the country? Were there any indications of an imminent event (e.g. increases in seismicity) directly before the M 7.0 mainshock, which could have prepared better prepared Haitian citizens? These are questions we will investigate with this Jupyter notebook."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another reason for the profound devastation could have been the particular frequency signature of the earthquake. We will use this Jupyter notebook to analyze the frequency content of the mainshock using spectrograms to see if its frequency signature (in addition to the lack of warning and preparedness) also contributed to the widespread devastation that Haiti experienced."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" The purpose of the first section of this notebook will be to see whether there was an unusual pattern of seismicity in the days leading up to the mainshock, to see if there were any seismic signs of a large imminent earthquake. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import matplotlib.dates as mdates\n",
"from obspy import UTCDateTime\n",
"from obspy.clients.fdsn import Client\n",
"client = Client(\"IRIS\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" SET SEARCH PARAMETERS HERE :\n",
"The next set of code will set the variables for the earthquake catalog search. The start time will be 5 years prior to the 2010 earthquake."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"startt = UTCDateTime(\"2005-01-13\")\n",
"endt = UTCDateTime(\"2010-01-13\")\n",
"lon = -72.5330\n",
"lat = 18.4570\n",
"maxrad = 1.0\n",
"minmag = 1.0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following catalog request may take a few minutes to complete if the number of events being requested is large:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"cat = client.get_events(starttime=startt, endtime=endt, latitude=lat, longitude=lon, maxradius=maxrad, minmagnitude=minmag, catalog=\"ISC\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"41 Event(s) in Catalog:\n",
"2010-01-12T23:55:27.420000Z | +18.425, -72.771 | 3.6 mb\n",
"2010-01-12T23:54:59.450000Z | +18.462, -72.775 | 3.6 mb\n",
"...\n",
"2005-05-12T00:57:59.120000Z | +18.430, -72.354 | 4.3 mb\n",
"2005-04-05T01:22:55.310000Z | +18.231, -73.464 | 3.4 mb\n",
"To see all events call 'print(CatalogObject.__str__(print_all=True))'\n"
]
}
],
"source": [
"print(cat)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In essence, this loop is taking the times and magnitudes from the catalog object and storing them in separate lists called times[] and mags[]. The list structure will allow them to be modified (necessary for the times; mtimes[]) and sent to matplotlib for plotting."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2010-01-12 23:38:53.600000 3.7\n",
"733784.985343 3.7\n",
"[3.6, 3.6, 4.5, 3.1, 3.7, 4.6, 4.7, 4.3, 3.7, 5.3, 3.8, 4.6, 3.5, 4.7, 3.5, 4.0, 4.2, 4.3, 5.7, 4.3, 4.1, 5.0, 6.0, 7.0, 3.3, 4.3, 4.2, 2.0, 3.0, 1.1, 2.0, 1.4, 1.6, 2.9, 3.3, 2.4, 1.1, 3.5, 4.0, 4.3, 3.4]\n"
]
}
],
"source": [
"times = []\n",
"mags = []\n",
"for event in cat:\n",
" times.append(event.origins[0].time.datetime)\n",
" mags.append(event.magnitudes[0].mag)\n",
" \n",
"mtimes = mdates.date2num(times)\n",
"print(times[4],mags[4])\n",
"print(mtimes[4],mags[4])\n",
"print(mags)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The next section of code will create a plot that shows the magnitude of all earthquakes in the catalog over time."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig1 = plt.subplots(1,1)\n",
"plt.plot_date(mtimes,mags,marker='.')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The magnitudes of earthquakes before the 2010 M 7.0 earthquake were low -- with no quakes above M 1.0 occurring. On the yearly scale, this does not appear to be particularly unusual for this region and it shows that there weren't any earthquakes leading up to the M 7.0 mainshock. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following code will investigate whether the seismicity rate was different leading up to the 2010 earthquake than it had been in previous years. This will indicate whether or not the earthquake could have been better anticipated or if any warning at all could have been issued."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig2, axes = plt.subplots(1,1)\n",
"axes.hist(mtimes, bins=60)\n",
"axes.xaxis.set_major_locator(mdates.YearLocator())\n",
"axes.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The seismicity rate was 0 for about a year before the 2010 earthquake, which was not very unusual for this region. Therefore, there was no reason to think that such a large earthquake would take place at that particular time, making it hard to prepare in the short-term for such a destructive event."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" The next section of code will analyze the earthquake's frequency using spectrograms. The frequency content may tell us more about why this event was so damaging for a M 7.0 earthquake. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import libraries:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from obspy.clients.fdsn import Client\n",
"import matplotlib.pyplot as plt\n",
"from obspy import UTCDateTime as UTCDateTime"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following code will set the parameters and request the seismogram."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"network = \"IU\"\n",
"station = \"TEIG\"\n",
"location = \"00\"\n",
"channel = \"BHZ\"\n",
"client = Client(\"IRIS\")\n",
"startt = UTCDateTime(\"2010-01-12T21:56:00\")\n",
"endt = UTCDateTime(\"2010-01-12T22:06:30\")\n",
"\n",
"st = client.get_waveforms(network, station, location, channel, startt, endt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we will extract a seismic trace from the stream that was just downloaded. Then we will plot the trace."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"tr = st[0]\n",
"fig = tr.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The next code cell will create a spectrogram from the above seismogram. Wave amplitude is represented by the color of the graph, frequency is measured on the y-axis, and time is measured on the x-axis."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(5.9918224530375696e-15, 201087.8127976626)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.rcParams['figure.figsize'] = (12,4)\n",
"fig = tr.spectrogram(show=False, per_lap=.5) \n",
"ax = fig.axes[0] \n",
"im = ax.images[0] \n",
"print(im.get_clim())\n",
"im.set_clim(vmax=100000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The amplitude of the waves increases greatly when the S waves and the surface waves arrive (demonstrated by the lighter colors then). These bursts of energy, which correspond with the arrival times of each phase, are seen across almost all observed frequencies (1-9 Hz)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The frequency signal of this earthquake is relatively high, as the wave frequencies continually extend above frequencies of 1 Hz and reach closer to 8 and 10 Hz. This high-frequency shaking makes sense, as no major earthquake had occurred on this fault for many years, suggesting that more rapid vibrations would be released during this large earthquake (McLaskey et al., 2012). (https://courses.cit.cornell.edu/mclaskey/pubs/McLaskey_acceptedNaturePaper.pdf)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The high frequency waves generated by the 2010 Haiti earthquake also help to explain why the damage from the M 7.0 earthquake was so severe. The resonance frequency of many human-made structures (e.g. homes, bridges, roads, low-rise buildings) typically falls within the earthquake's higher frequency range. Collapse of buildings (both residences and commercial buildings) help account for the devastating loss of human life caused by this earthquake."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}