ObsPy Tutorial
Downloading/Processing Exercise
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image: User:Abbaszade656 / Wikimedia Commons / CC-BY-SA-4.0
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.
In [1]:
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = 12, 8
The first step is to download all the necessary information using the ObsPy FDSN client. Learn to read the documentation!
We need the following things:
- Event information about the 2014 South Napa earthquake. Use the
get_events()method of the client. A good provider of event data is the USGS. - Waveform information for a certain station. Choose your favorite one! If you have no preference, use
II.PFOwhich is available for example from IRIS. Use theget_waveforms()method. - Download the associated station/instrument information with the
get_stations()method.
In [2]:
import obspy
from obspy.clients.fdsn import Client
c_event = Client("USGS")
# Event time.
event_time = obspy.UTCDateTime("2014-08-24T10:20:44.0")
# Get the event information. The temporal and magnitude constraints make it unique
cat = c_event.get_events(starttime=event_time - 10, endtime=event_time + 10,
minmagnitude=6)
print(cat)
c = Client("IRIS")
# Download station information at the response level!
inv = c.get_stations(network="II", station="PFO", location="00", channel="BHZ",
starttime=event_time - 10 * 60, endtime=event_time + 30 * 60,
level="response")
print(inv)
# Download 3 component waveforms.
st = c.get_waveforms(network="II", station="PFO", location="00",
channel="BHZ", starttime=event_time - 10 * 60,
endtime=event_time + 30 * 60)
print(st)
Have a look at the just downloaded data.
In [3]:
inv.plot()
inv.plot_response(0.001)
cat.plot()
st.plot()
Out[3]:
In [4]:
coords = inv.get_coordinates("II.PFO.00.BHZ")
coords
Out[4]:
Step 2: Determine Coordinates of Event¶
In [5]:
origin = cat[0].origins[0]
print(origin)
Step 3: Calculate distance of event and station.¶
Use obspy.geodetics.locations2degree.
In [6]:
from obspy.geodetics import locations2degrees
distance = locations2degrees(origin.latitude, origin.longitude,
coords["latitude"], coords["longitude"])
print(distance)
Step 4: Calculate Theoretical Arrivals¶
from obspy.taup import TauPyModel
m = TauPyModel(model="ak135")
arrivals = m.get_ray_paths(...)
arrivals.plot()
In [7]:
from obspy.taup import TauPyModel
m = TauPyModel(model="ak135")
arrivals = m.get_ray_paths(
distance_in_degree=distance,
source_depth_in_km=origin.depth / 1000.0)
arrivals.plot();
Step 5: Calculate absolute time of the first arrivals at the station¶
In [8]:
first_arrival = origin.time + arrivals[0].time
print(first_arrival)
Step 6: Cut to 1 minute before and 5 minutes after the first arrival¶
In [9]:
st.trim(first_arrival - 60, first_arrival + 300)
st.plot();
In [10]:
st.remove_response(inventory=inv,
pre_filt=(1.0 / 100.0, 1.0 / 50.0, 10.0, 20.0),
output="VEL")
st.plot()
Out[10]:
Bonus: Interactive IPython widgets¶
In [11]:
from ipywidgets import interact
from obspy.taup import TauPyModel
m = TauPyModel("ak135")
def plot_raypaths(distance, depth, wavetype):
try:
plt.close()
except:
pass
if wavetype == "ttall":
phases = ["ttall"]
elif wavetype == "diff":
phases = ["Pdiff", "pPdiff"]
m.get_ray_paths(distance_in_degree=distance,
source_depth_in_km=depth,
phase_list=phases).plot();
interact(plot_raypaths, distance=(1, 180),
depth=(0, 700), wavetype=["ttall", "diff"]);
