{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
\n", "
\n", "
Rotational Seismology
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Data Download + Pre-Processing
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\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Seismo-Live: http://seismo-live.org\n", "\n", "##### Authors:\n", "* Johannes Salvermoser ([@salve-](https://github.com/salve-))\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Rotational Seismology Tutorial: Data Download + Pre-Processing

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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](images/obspy_logo_full_524x179px.png)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "plt.style.use('ggplot')\n", "plt.rcParams['figure.figsize'] = 12, 8" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pick events from catalog to define start- and endtimes of traces\n", "The first step to compare waveform data of a ring laser and a collocated seismometer is to choose which time period we want to look at.
\n", "Usually, we want to observe Love waves generated by earthquake events, so we need to pick an event from a earthquake catalog
\n", "which is done using the FDSN client.
\n", "
\n", "The FDSN client fetches an event catalog from a data center - in this case IRIS - which can be filtered by different criteria, such as:\n", "\n", "\n", "The catalog we use here is the Global CMT catalog which contains moment magnitude picked events." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 Event(s) in Catalog:\n", "2011-03-11T05:46:23.200000Z | +38.296, +142.498 | 9.1 MW\n" ] } ], "source": [ "from obspy.clients.fdsn import Client as fdsnClient\n", "from obspy.core import UTCDateTime\n", "\n", "c_fdsn = fdsnClient('IRIS')\n", "cat = c_fdsn.get_events(minmagnitude=9.0, starttime=UTCDateTime(2011,1,1), endtime=UTCDateTime(2012,1,1))\n", "event = cat[0]\n", "print(cat)\n", "start = event.origins[0].time\n", "end = start + 3600" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Download waveform traces\n", "Once we selected an event and obtained the earthquake's origin time, we can download the waveform data using the Arclink client, which is a distributed data request protocol that can be used to access archived waveform data (for more information see Obspy's Arclink documentation)
\n", "
\n", "For each requested waveform component we need to specify its unique identifier, consisting of:\n", "\n", "
\n", "In order to simplify the subsequent handling, we unite the three seismometer components [N,E,Z] (= traces) to a single **stream object**." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 Trace(s) in Stream:\n", "BW.RLAS..BJZ | 2011-03-11T05:46:00.300899Z - 2011-03-11T06:46:30.450899Z | 20.0 Hz, 72604 samples\n", "3 Trace(s) in Stream:\n", "GR.WET..BHE | 2011-03-11T05:46:13.974999Z - 2011-03-11T06:46:25.574999Z | 20.0 Hz, 72233 samples\n", "GR.WET..BHN | 2011-03-11T05:46:22.574999Z - 2011-03-11T06:46:31.024999Z | 20.0 Hz, 72170 samples\n", "GR.WET..BHZ | 2011-03-11T05:46:06.324999Z - 2011-03-11T06:46:30.274999Z | 20.0 Hz, 72480 samples\n" ] } ], "source": [ "from obspy.core.stream import Stream\n", "\n", "c_rot = fdsnClient('http://erde.geophysik.uni-muenchen.de') # rotational data source\n", "c_bb = fdsnClient('BGR') # broadband data source\n", "\n", "RLAS = c_rot.get_waveforms(network='BW', station='RLAS', location='', channel='BJZ', starttime=start, endtime=end)\n", "BHE = c_bb.get_waveforms(network='GR', station='WET', location='', channel='BHE', starttime=start, endtime=end)\n", "BHN = c_bb.get_waveforms(network='GR', station='WET', location='', channel='BHN', starttime=start, endtime=end)\n", "BHZ = c_bb.get_waveforms(network='GR', station='WET', location='', channel='BHZ', starttime=start, endtime=end)\n", "AC = Stream(traces=[BHE[0],BHN[0],BHZ[0]])\n", "\n", "print(RLAS)\n", "print(AC)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Remove the instrument responses from the recordings + convert units\n", "\n", "We want to deal with meaningful SI-units, so we need to correct the waveforms.
\n", "To convert ring laser's vertical rotation rate to [nrad/s] units, we can simply us a conversion factor:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "RLAS.detrend(type='linear')\n", "RLAS[0].data = RLAS[0].data * 1/6.3191 * 1e-3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to remove the seismometer's response using and convert the velocity recordings to acceleration [nm/s²],
\n", "Obspy's simulate function is used.
\n", "It removes the sensitivity and converts to acceleration in one step employing poles & zeros in this case." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3 Trace(s) in Stream:\n", "GR.WET..BHE | 2011-03-11T05:46:13.974999Z - 2011-03-11T06:46:25.574999Z | 20.0 Hz, 72233 samples\n", "GR.WET..BHN | 2011-03-11T05:46:22.574999Z - 2011-03-11T06:46:31.024999Z | 20.0 Hz, 72170 samples\n", "GR.WET..BHZ | 2011-03-11T05:46:06.324999Z - 2011-03-11T06:46:30.274999Z | 20.0 Hz, 72480 samples" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "AC.detrend(type='linear')\n", "AC.taper(max_percentage=0.05)\n", "\n", "paz_sts2 = {'poles': [(-0.0367429 + 0.036754j), (-0.0367429 - 0.036754j)],\n", " 'sensitivity': 0.944019640,\n", " 'zeros': [0j],\n", " 'gain': 1.0}\n", "\n", "AC.simulate(paz_remove=paz_sts2, remove_sensitivity=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The resulting traces are trimmed to make sure that start- and endtimes match for all waveforms:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1 Trace(s) in Stream:\n", "BW.RLAS..BJZ | 2011-03-11T05:46:22.550899Z - 2011-03-11T06:46:25.550899Z | 20.0 Hz, 72061 samples" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "startaim = max([tr.stats.starttime for tr in (AC + RLAS)])\n", "endtaim = min([tr.stats.endtime for tr in (AC + RLAS)])\n", "\n", "AC.trim(startaim, endtaim, nearest_sample=True)\n", "RLAS.trim(startaim, endtaim, nearest_sample=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot the resulting traces\n", "\n", "The single traces/components are plotted and compared." ] }, { "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": [ "import matplotlib.pyplot as plt\n", "fig, (ax1,ax2,ax3,ax4) = plt.subplots(4, sharex=True)\n", "\n", "ax1.set_title(str(start.date) + ': ' + str(start.time) + \" - \" + str(end.time))\n", "ax1.plot(RLAS[0].times(), RLAS[0],'r',label='vertical rotation rate')\n", "ax1.set_ylabel('rot. rate [nrad/s]')\n", "ax2.plot(AC[0].times(),AC[0],'k',label='horizontal acc. E-component')\n", "ax2.set_ylabel('acc. [nm/s^2]')\n", "ax3.plot(AC[1].times(),AC[1],'k',label='horizontal acc. N-component')\n", "ax3.set_ylabel('acc. [nm/s^2]')\n", "ax4.plot(AC[2].times(),AC[2],'k',label='vertical acc.')\n", "ax4.set_ylabel('acc. [nm/s^2]')\n", "ax4.set_xlabel('time [s]')\n", "\n", "for ax in [ax1,ax2,ax3,ax4]:\n", " ax.legend(loc=2, prop={\"size\":12})\n", " ax.yaxis.major.formatter.set_powerlimits((-1,2))\n", " ax.set_xlim(0,max(AC[0].times()))\n", "\n", "fig.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Resample, Filter and Rotate\n", "Resample seismograms using **decimate** in order to reduce the size of the arrays (speeds up processing).\n", "The seismograms are high-cut and low-cut filtered, depending on the frequency range of interest and the resolution of the instruments." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3 Trace(s) in Stream:\n", "GR.WET..BHE | 2011-03-11T05:46:22.574999Z - 2011-03-11T06:46:25.574999Z | 5.0 Hz, 18016 samples\n", "GR.WET..BHN | 2011-03-11T05:46:22.574999Z - 2011-03-11T06:46:25.574999Z | 5.0 Hz, 18016 samples\n", "GR.WET..BHZ | 2011-03-11T05:46:22.574999Z - 2011-03-11T06:46:25.574999Z | 5.0 Hz, 18016 samples" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "RLAS.decimate(factor=4)\n", "AC.decimate(factor=4)\n", "high_cut = 1.0\n", "low_cut = 0.005\n", "\n", "RLAS.filter('bandpass', freqmax=high_cut, freqmin=low_cut, corners=2, zerophase=True)\n", "AC.filter('bandpass', freqmax=high_cut, freqmin=low_cut, corners=2, zerophase=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to align the seismometer recordings with the event direction, we need to rotate the horizontal components
\n", "of the acceleration to transverse and radial.
\n", "
\n", "We can determine the theoretical rotation/direction angle (= backazimuth) from station and event location using **gps2dist_azimuth**.
\n", "This function also yields the epicentral distance and the azimuth angle." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epicentral distance [m]: 9127350.828883186\n", "Theoretical azimuth [deg]: 329.40798115843046\n", "Theoretical backazimuth [deg]: 37.602787067188785\n" ] } ], "source": [ "from obspy.geodetics.base import gps2dist_azimuth\n", "\n", "# event location from event info\n", "source_latitude = event.origins[0].latitude\n", "source_longitude = event.origins[0].longitude\n", "\n", "# station location (Wettzell)\n", "station_latitude = 49.144001\n", "station_longitude = 12.8782\n", "\n", "# theoretical backazimuth and distance\n", "baz = gps2dist_azimuth(source_latitude, source_longitude, station_latitude, station_longitude)\n", "\n", "print('Epicentral distance [m]: ', baz[0])\n", "print('Theoretical azimuth [deg]: ', baz[1])\n", "print('Theoretical backazimuth [deg]: ', baz[2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now rotate the E-N component seismometer recordings to radial [0] and transverse [1] acceleration using the theoretical BAz.
\n", "\n", "In a last step the normalized **transverse acceleration** is plotted together with **vertical rotation rate**." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from obspy.signal.rotate import rotate_ne_rt\n", "import numpy as np\n", "\n", "# rotate\n", "AC.rotate(method='NE->RT',back_azimuth=baz[2])\n", "plt.figure(figsize=(15,2))\n", "\n", "ax = plt.subplot(111)\n", "ax.plot(RLAS[0].times(), RLAS[0].data/np.max(np.abs(RLAS[0].data)), 'r', label='vertical rotation rate')\n", "ax.plot(AC[0].times(), AC[0].data/np.max(np.abs(AC[0].data)), 'k', label='transverse acceleration')\n", "ax.legend(loc=2, prop={\"size\":12})\n", "ax.set_xlabel('time [s]')\n", "ax.set_ylabel('normalized amplitude')\n", "ax.set_xlim(0,max(RLAS[0].times()))\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note: the vertical rotation rate and transverse acceleration are in phase!**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### References\n", "" ] } ], "metadata": { "jupytext": { "encoding": "# -*- coding: utf-8 -*-" }, "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }