DataAnalysis2022/ObsPy/07_Basic_Processing_Exercise.ipynb

{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["<div style='background-image: url(\"../share/images/header.svg\") ; padding: 0px ; background-size: cover ; border-radius: 5px ; height: 250px'>\n", "    <div style=\"float: right ; margin: 50px ; padding: 20px ; background: rgba(255 , 255 , 255 , 0.7) ; width: 50% ; height: 150px\">\n", "        <div style=\"position: relative ; top: 50% ; transform: translatey(-50%)\">\n", "            <div style=\"font-size: xx-large ; font-weight: 900 ; color: rgba(0 , 0 , 0 , 0.8) ; line-height: 100%\">ObsPy Tutorial</div>\n", "            <div style=\"font-size: large ; padding-top: 20px ; color: rgba(0 , 0 , 0 , 0.5)\">Downloading/Processing Exercise</div>\n", "        </div>\n", "    </div>\n", "</div>"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Seismo-Live: http://seismo-live.org\n", "\n", "##### Authors:\n", "* Lion Krischer ([@krischer](https://github.com/krischer))\n", "* Tobias Megies ([@megies](https://github.com/megies))\n", "---"]}, {"cell_type": "markdown", "metadata": {}, "source": ["![](images/obspy_logo_full_524x179px.png)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["For the this exercise we will download some data from the Tohoku-Oki earthquake, cut out a certain time window around the first arrival and remove the instrument response from the data."]}, {"cell_type": "code", "execution_count": null, "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": ["The first step is to download all the necessary information using the ObsPy FDSN client. **Learn to read the documentation!**\n", "\n", "We need the following things:\n", "\n", "1. Event information about the Tohoku-Oki earthquake. Use the `get_events()` method of the client. A good provider of event data is the USGS.\n", "2. Waveform information for a certain station. Choose your favorite one! If you have no preference, use `II.BFO` which is available for example from IRIS. Use the `get_waveforms()` method.\n", "3. Download the associated station/instrument information with the `get_stations()` method."]}, {"cell_type": "code", "execution_count": null, "metadata": {"lines_to_next_cell": 2, "tags": ["exercise"]}, "outputs": [], "source": []}, {"cell_type": "code", "execution_count": null, "metadata": {"tags": ["solution"]}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["Have a look at the just downloaded data."]}, {"cell_type": "code", "execution_count": null, "metadata": {"lines_to_next_cell": 2, "tags": ["exercise"]}, "outputs": [], "source": []}, {"cell_type": "code", "execution_count": null, "metadata": {"tags": ["solution"]}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["## Exercise\n", "\n", "The goal of this exercise is to cut the data from 1 minute before the first arrival to 5 minutes after it, and then remove the instrument response.\n", "\n", "\n", "#### Step 1: Determine Coordinates of Station"]}, {"cell_type": "code", "execution_count": null, "metadata": {"lines_to_next_cell": 2, "tags": ["exercise"]}, "outputs": [], "source": []}, {"cell_type": "code", "execution_count": null, "metadata": {"tags": ["solution"]}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["#### Step 2: Determine Coordinates of Event"]}, {"cell_type": "code", "execution_count": null, "metadata": {"lines_to_next_cell": 2, "tags": ["exercise"]}, "outputs": [], "source": []}, {"cell_type": "code", "execution_count": null, "metadata": {"tags": ["solution"]}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["#### Step 3: Calculate distance of event and station.\n", "\n", "Use `obspy.geodetics.locations2degree`."]}, {"cell_type": "code", "execution_count": null, "metadata": {"lines_to_next_cell": 2, "tags": ["exercise"]}, "outputs": [], "source": []}, {"cell_type": "code", "execution_count": null, "metadata": {"tags": ["solution"]}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["#### Step 4: Calculate Theoretical Arrivals\n", "\n", "```python\n", "from obspy.taup import TauPyModel\n", "m = TauPyModel(model=\"ak135\")\n", "arrivals = m.get_ray_paths(...)\n", "arrivals.plot()\n", "```"]}, {"cell_type": "code", "execution_count": null, "metadata": {"lines_to_next_cell": 2, "tags": ["exercise"]}, "outputs": [], "source": []}, {"cell_type": "code", "execution_count": null, "metadata": {"tags": ["solution"]}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["#### Step 5: Calculate absolute time of the first arrivals at the station"]}, {"cell_type": "code", "execution_count": null, "metadata": {"lines_to_next_cell": 2, "tags": ["exercise"]}, "outputs": [], "source": []}, {"cell_type": "code", "execution_count": null, "metadata": {"tags": ["solution"]}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["#### Step 6:  Cut to 1 minute before and 5 minutes after the first arrival"]}, {"cell_type": "code", "execution_count": null, "metadata": {"lines_to_next_cell": 2, "tags": ["exercise"]}, "outputs": [], "source": []}, {"cell_type": "code", "execution_count": null, "metadata": {"tags": ["solution"]}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["#### Step 7: Remove the instrument response\n", "\n", "```python\n", "st.remove_response(inventory=inv, pre_filt=...)\n", "```\n", "\n", "![taper](images/cos_taper.png)"]}, {"cell_type": "code", "execution_count": null, "metadata": {"lines_to_next_cell": 2, "tags": ["exercise"]}, "outputs": [], "source": []}, {"cell_type": "code", "execution_count": null, "metadata": {"tags": ["solution"]}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["## Bonus: Interactive IPython widgets"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["from IPython.html.widgets import interact\n", "from obspy.taup import TauPyModel\n", "\n", "m = TauPyModel(\"ak135\")\n", "\n", "def plot_raypaths(distance, depth, wavetype):\n", "    try:\n", "        plt.close()\n", "    except:\n", "        pass\n", "    if wavetype == \"ttall\":\n", "        phases = [\"ttall\"]\n", "    elif wavetype == \"diff\":\n", "        phases = [\"Pdiff\", \"pPdiff\"]\n", "    m.get_ray_paths(distance_in_degree=distance,\n", "                    source_depth_in_km=depth,\n", "                    phase_list=phases).plot();\n", "    \n", "interact(plot_raypaths, distance=[0, 180],\n", "         depth=[0, 700], wavetype=[\"ttall\", \"diff\"]);"]}], "metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}}, "nbformat": 4, "nbformat_minor": 2}