14. Seismometer Correction/Simulation

14.1. Calculating response from filter stages using evalresp..

14.1.1. ..using a StationXML file or in general an Inventory object

When using the FDSN client the response can directly be attached to the waveforms and then subsequently removed using Stream.remove_response():

from obspy import UTCDateTime
from obspy.clients.fdsn import Client

t1 = UTCDateTime("2010-09-3T16:30:00.000")
t2 = UTCDateTime("2010-09-3T17:00:00.000")
fdsn_client = Client('IRIS')
# Fetch waveform from IRIS FDSN web service into a ObsPy stream object
# and automatically attach correct response
st = fdsn_client.get_waveforms(network='NZ', station='BFZ', location='10',
                               channel='HHZ', starttime=t1, endtime=t2,
# define a filter band to prevent amplifying noise during the deconvolution
pre_filt = (0.005, 0.006, 30.0, 35.0)
st.remove_response(output='DISP', pre_filt=pre_filt)

Alternatively an Inventory object can be directly passed to the Stream.remove_response(): method:

from obspy import read, read_inventory

# simply use the included example waveform
st = read()
# the corresponding response is included in ObsPy as a StationXML file
inv = read_inventory()
# the routine automatically picks the correct response for each trace
# define a filter band to prevent amplifying noise during the deconvolution
pre_filt = (0.005, 0.006, 30.0, 35.0)
st.remove_response(inventory=inv, output='DISP', pre_filt=pre_filt)

Using the plot option it is possible to visualize the individual steps during response removal in the frequency domain to check the chosen pre_filt and water_level options to stabilize the deconvolution of the inverted instrument response spectrum:

from obspy import read, read_inventory

st = read("/path/to/IU_ULN_00_LH1_2015-07-18T02.mseed")
tr = st[0]
inv = read_inventory("/path/to/IU_ULN_00_LH1.xml")
pre_filt = [0.001, 0.005, 10, 20]
tr.remove_response(inventory=inv, pre_filt=pre_filt, output="DISP",
                   water_level=60, plot=True)

(Source code, png, hires.png)


14.1.2. ..using a RESP file

It is further possible to use evalresp to evaluate the instrument response information from a RESP file.

import matplotlib.pyplot as plt

import obspy
from obspy.core.util import NamedTemporaryFile
from obspy.clients.fdsn import Client as FDSN_Client
from obspy.clients.iris import Client as OldIris_Client

# MW 7.1 Darfield earthquake, New Zealand
t1 = obspy.UTCDateTime("2010-09-3T16:30:00.000")
t2 = obspy.UTCDateTime("2010-09-3T17:00:00.000")

# Fetch waveform from IRIS FDSN web service into a ObsPy stream object
fdsn_client = FDSN_Client("IRIS")
st = fdsn_client.get_waveforms('NZ', 'BFZ', '10', 'HHZ', t1, t2)

# Download and save instrument response file into a temporary file
with NamedTemporaryFile() as tf:
    respf = tf.name
    old_iris_client = OldIris_Client()
    # fetch RESP information from "old" IRIS web service, see obspy.fdsn
    # for accessing the new IRIS FDSN web services
    old_iris_client.resp('NZ', 'BFZ', '10', 'HHZ', t1, t2, filename=respf)

    # make a copy to keep our original data
    st_orig = st.copy()

    # define a filter band to prevent amplifying noise during the deconvolution
    pre_filt = (0.005, 0.006, 30.0, 35.0)

    # this can be the date of your raw data or any date for which the
    # SEED RESP-file is valid
    date = t1

    seedresp = {'filename': respf,  # RESP filename
                # when using Trace/Stream.simulate() the "date" parameter can
                # also be omitted, and the starttime of the trace is then used.
                'date': date,
                # Units to return response in ('DIS', 'VEL' or ACC)
                'units': 'DIS'

    # Remove instrument response using the information from the given RESP file
    st.simulate(paz_remove=None, pre_filt=pre_filt, seedresp=seedresp)

# plot original and simulated data
tr = st[0]
tr_orig = st_orig[0]
time = tr.times()

plt.plot(time, tr_orig.data, 'k')
plt.ylabel('STS-2 [counts]')
plt.plot(time, tr.data, 'k')
plt.ylabel('Displacement [m]')
plt.xlabel('Time [s]')

(Source code)

14.1.3. ..using a Dataless/Full SEED file (or XMLSEED file)

A Parser object created using a Dataless SEED file can also be used. For each trace the respective RESP response data is extracted internally then. When using Stream/Trace‘s simulate() convenience methods the “date” parameter can be omitted (each trace’s start time is used internally).

import obspy
from obspy.io.xseed import Parser

st = obspy.read("https://examples.obspy.org/BW.BGLD..EH.D.2010.037")
parser = Parser("https://examples.obspy.org/dataless.seed.BW_BGLD")
st.simulate(seedresp={'filename': parser, 'units': "DIS"})

14.2. Using a PAZ dictionary

The following script shows how to simulate a 1Hz seismometer from a STS-2 seismometer with the given poles and zeros. Poles, zeros, gain (A0 normalization factor) and sensitivity (overall sensitivity) are specified as keys of a dictionary.

import obspy
from obspy.signal.invsim import corn_freq_2_paz

paz_sts2 = {
    'poles': [-0.037004 + 0.037016j, -0.037004 - 0.037016j, -251.33 + 0j,
              - 131.04 - 467.29j, -131.04 + 467.29j],
    'zeros': [0j, 0j],
    'gain': 60077000.0,
    'sensitivity': 2516778400.0}
paz_1hz = corn_freq_2_paz(1.0, damp=0.707)  # 1Hz instrument
paz_1hz['sensitivity'] = 1.0

st = obspy.read()
# make a copy to keep our original data
st_orig = st.copy()

# Simulate instrument given poles, zeros and gain of
# the original and desired instrument
st.simulate(paz_remove=paz_sts2, paz_simulate=paz_1hz)

# plot original and simulated data

(Source code, png, hires.png)


For more customized plotting we could also work with matplotlib manually from here:

import numpy as np
import matplotlib.pyplot as plt

tr = st[0]
tr_orig = st_orig[0]

t = np.arange(tr.stats.npts) / tr.stats.sampling_rate

plt.plot(t, tr_orig.data, 'k')
plt.ylabel('STS-2 [counts]')
plt.plot(t, tr.data, 'k')
plt.ylabel('1Hz Instrument [m/s]')
plt.xlabel('Time [s]')

(Source code, png, hires.png)