def get_peak_spectrals(data, samp_rate,periods):
"""Calculate peak pseudo-spectral parameters.
Compute 5% damped PSA at input periods seconds.
Data must be an acceleration Trace.
:param data:
Obspy Trace object, containing acceleration data to convolve with pendulum at freq.
:param delta:
sample rate (samples per sec).
:returns:
Dictionary containing keys of input periods, and values of corresponding spectral accelerations.
"""
D = 0.05 # 5% damping
psadict = {}
for T in periods:
freq = 1.0 / T
omega = (2 * 3.14159 * freq) ** 2
paz_sa = corn_freq_2_paz(freq, damp=D)
paz_sa['sensitivity'] = omega
paz_sa['zeros'] = []
dd = simulate_seismometer(data.data, samp_rate, paz_remove=None, paz_simulate=paz_sa,
taper=True, simulate_sensitivity=True, taper_fraction=0.05)
if abs(max(dd)) >= abs(min(dd)):
psa = abs(max(dd))
else:
psa = abs(min(dd))
psadict[T] = psa
return psadict
def calculate_damped_spectral_acc(data, delta, freq, damp, ty):
samp_rate = 1.0 / delta
t = freq * 1.0
d = damp
omega = (2 * math.pi * t)**2
paz_sa = corn_freq_2_paz(t, damp=d)
paz_sa['sensitivity'] = omega
paz_sa['zeros'] = []
if ty == 'displacement':
data = np.gradient(data, delta)
data = np.gradient(data, delta)
elif ty == 'velocity':
data = np.gradient(data, delta)
data = simulate_seismometer(data,
samp_rate,
paz_remove=None,
paz_simulate=paz_sa,
taper=True,
simulate_sensitivity=True,
taper_fraction=0.05)
return data
def geophone_response(resonance_frequency,
gain,
damping=0.707,
output_resistance=np.inf,
cable_length=np.inf,
cable_capacitance=np.inf,
sensitivity=1,
stage_sequence_number=1):
paz = corn_freq_2_paz(resonance_frequency, damp=damping)
l = cable_length
R = output_resistance
C = cable_capacitance
if ((R * l * C) != np.inf) and ((R * l * C) != 0):
pole_cable = -1 / (R * l * C)
paz['poles'].append(pole_cable)
i_s = InstrumentSensitivity(sensitivity,
resonance_frequency,
input_units='M/S',
output_units='M/S',
input_units_description='velocity',
output_units_description='velocity')
pzr = PolesZerosResponseStage(stage_sequence_number, gain,
resonance_frequency, 'M/S', 'M/S',
'LAPLACE (RADIANT/SECOND)',
resonance_frequency, paz['zeros'],
paz['poles'])
return Response(instrument_sensitivity=i_s, response_stages=[pzr])
def _remove_node_response(st) -> obspy.Stream:
"""using the fairfield files, remove the response through deconvolution"""
stt = st.copy()
paz_5hz = corn_freq_2_paz(5.0, damp=0.707)
paz_5hz["sensitivity"] = 76700
pre_filt = (0.25, 0.5, 180.0, 200.0)
stt.simulate(paz_remove=paz_5hz, pre_filt=pre_filt)
return stt
def _get_beamforming_example_stream():
# Load data
from obspy import read
from obspy.core.util import AttribDict
from obspy.signal.invsim import corn_freq_2_paz
st = read("https://examples.obspy.org/agfa.mseed")
# Set PAZ and coordinates for all 5 channels
st[0].stats.paz = AttribDict({
'poles': [(-0.03736 - 0.03617j), (-0.03736 + 0.03617j)],
'zeros': [0j, 0j],
'sensitivity': 205479446.68601453,
'gain': 1.0})
st[0].stats.coordinates = AttribDict({
'latitude': 48.108589,
'elevation': 0.450000,
'longitude': 11.582967})
st[1].stats.paz = AttribDict({
'poles': [(-0.03736 - 0.03617j), (-0.03736 + 0.03617j)],
'zeros': [0j, 0j],
'sensitivity': 205479446.68601453,
'gain': 1.0})
st[1].stats.coordinates = AttribDict({
'latitude': 48.108192,
'elevation': 0.450000,
'longitude': 11.583120})
st[2].stats.paz = AttribDict({
'poles': [(-0.03736 - 0.03617j), (-0.03736 + 0.03617j)],
'zeros': [0j, 0j],
'sensitivity': 250000000.0,
'gain': 1.0})
st[2].stats.coordinates = AttribDict({
'latitude': 48.108692,
'elevation': 0.450000,
'longitude': 11.583414})
st[3].stats.paz = AttribDict({
'poles': [(-4.39823 + 4.48709j), (-4.39823 - 4.48709j)],
'zeros': [0j, 0j],
'sensitivity': 222222228.10910088,
'gain': 1.0})
st[3].stats.coordinates = AttribDict({
'latitude': 48.108456,
'elevation': 0.450000,
'longitude': 11.583049})
st[4].stats.paz = AttribDict({
'poles': [(-4.39823 + 4.48709j), (-4.39823 - 4.48709j), (-2.105 + 0j)],
'zeros': [0j, 0j, 0j],
'sensitivity': 222222228.10910088,
'gain': 1.0})
st[4].stats.coordinates = AttribDict({
'latitude': 48.108730,
'elevation': 0.450000,
'longitude': 11.583157})
# Instrument correction to 1Hz corner frequency
paz1hz = corn_freq_2_paz(1.0, damp=0.707)
st.simulate(paz_remove='self', paz_simulate=paz1hz)
return st
def resi(x, debug = True):
h=x[1]
if debug:
print('Here is h: ' + str(h))
f=x[0]
if debug:
print('Here is f: ' + str(f))
pazTEMP = corn_freq_2_paz(f,h)
stTemp = st.copy()
pazTEMP['sensitivity'] = x[2]
stTemp[0].simulate(paz_remove=pazTEMP)
stTemp[1].simulate(paz_remove=paz)
stTemp.filter('highpass',freq=1./30.)
comp = sum(np.abs(stTemp[0].data - stTemp[1].data)**2)
if debug:
print('Here is the residual: ' + str(comp))
return comp
def get_params():
# Get Configuration
params = {}
######################
# params for BINFILE #
######################
params['stations'] = model.load_stations('stationsBO.txt')
# params['receivers'] = model.load_stations('receivers.txt')
params['WORKDIR'] = os.getcwd()
params['downsampling'] = False#True
params['df'] = 20.
params['dt'] = 1./params['df']
params['bin_duration'] = (3600.*24)#/4
params['percentfill'] = 10
params['freqmax'] = 9.8
params['freqmin'] = 0.01
params['stop'] = 0 # important param but default = 0
params['sizeout'] = 415 #size of the output matrix (sizein - stop)
###################
# params for CORR #
###################
params['rotation'] = False
params['corrType'] = 'crosscoherence'
params['analysis_duration'] = (3600.*24)#/24 # how much time to process as bunch. must stay like that not implemented to change now
params['temp_norm'] = 0 # 0: removing eq with param 'clipping'; -1: 1-bit normalization; 1: Windsorinzing with param 'clipping'
params['clipping'] = 3. # clipping eq or windsorizing at 3 * std(trace)
params['overlap'] = 0.4
params['corr_duration'] = 250. # slicing the 86400 in small windows
params['npts'] = params['corr_duration'] * params['df']
params['nthreads'] = 12
params['outname'] = 'BO.BO' #net1.net2
##################
# params for PAZ #
##################
#"""/!\ hard coded in CorrelBinSingle.py. Sta a or sta b"""
paz =corn_freq_2_paz(15.0, damp=0.57)
#paz['sensitivity']=(76*16/16)/5.9605e-7
params['paz'] = paz
params['pre_filt'] = (0.05, 0.055, 9.0, 9.5)
return params
def accelerometer_response(resonance_frequency,
gain,
sensitivity=1,
stage_sequence_number=1,
damping=0.707):
i_s = InstrumentSensitivity(sensitivity,
resonance_frequency,
input_units='M/S/S',
output_units='M/S/S',
input_units_description='acceleration',
output_units_description='acceleration')
paz = corn_freq_2_paz(resonance_frequency, damp=damping)
paz['zeros'] = []
pzr = PolesZerosResponseStage(1, 1, 14, 'M/S/S', 'M/S',
'LAPLACE (RADIANT/SECOND)', 1, [],
paz['poles'])
return Response(instrument_sensitivity=i_s, response_stages=[pzr])
def get_peak_spectrals(data, samp_rate, periods):
"""Calculate peak pseudo-spectral parameters.
Compute 5% damped PSA at input periods seconds.
Data must be an acceleration Trace.
:param data:
Obspy Trace object, containing acceleration data to convolve with pendulum at freq.
:param delta:
sample rate (samples per sec).
:returns:
Dictionary containing keys of input periods, and values of corresponding spectral accelerations.
"""
D = 0.05 # 5% damping
psadict = {}
for T in periods:
freq = 1.0 / T
omega = (2 * 3.14159 * freq)**2
paz_sa = corn_freq_2_paz(freq, damp=D)
paz_sa['sensitivity'] = omega
paz_sa['zeros'] = []
dd = simulate_seismometer(data.data,
samp_rate,
paz_remove=None,
paz_simulate=paz_sa,
taper=True,
simulate_sensitivity=True,
taper_fraction=0.05)
if abs(max(dd)) >= abs(min(dd)):
psa = abs(max(dd))
else:
psa = abs(min(dd))
psadict[T] = psa
return psadict
import sys, glob
import numpy as np
from obspy import Trace, read, read_inventory
# from pylab import *
#import matplotlib.pyplot as plt
import math, os
import nnobspy
from pyrocko import model
from obspy.signal.invsim import corn_freq_2_paz
oridir = '/data/beroza/liuxin/NLphase2/'
bindir = 'bins/full/'
f_prefilt=(0.02, 0.025, 9.5, 10.0)
resamp=20
nt = int(86400*resamp)
paz = corn_freq_2_paz(5.0, damp=0.67)
paz['sensitivity'] = 89.4
#respdir = '../responses'
#inv = read_inventory('inv.xml')
#kjd
# ------------------------------------------------------------------------------
# Main
def Process(arg_list):
# ------------------------------------------------------------
# parameters
stations = model.load_stations('stationsBO.txt')
sday = int(arg_list[0])
eday = int(arg_list[1])
comp = arg_list[2]
iyear = int(arg_list[3])
import matplotlib.pyplot as plt
import obspy
from obspy.clients.arclink import Client
from obspy.signal.invsim import corn_freq_2_paz, simulate_seismometer
# Retrieve data via ArcLink
# please provide a valid email address for the keyword user
client = Client(user="******")
t = obspy.UTCDateTime("2009-08-24 00:20:03")
st = client.get_waveforms('BW', 'RJOB', '', 'EHZ', t, t + 30)
paz = client.get_paz('BW', 'RJOB', '', 'EHZ', t)
# 1Hz instrument
one_hertz = corn_freq_2_paz(1.0)
# Correct for frequency response of the instrument
res = simulate_seismometer(st[0].data.astype('float32'),
st[0].stats.sampling_rate, paz, inst_sim=one_hertz)
# Correct for overall sensitivity
res = res / paz['sensitivity']
# Plot the seismograms
sec = np.arange(len(res)) / st[0].stats.sampling_rate
plt.subplot(211)
plt.plot(sec, st[0].data, 'k')
plt.title("%s %s" % (st[0].stats.station, t))
plt.ylabel('STS-2')
plt.subplot(212)
plt.plot(sec, res, 'k')
plt.xlabel('Time [s]')
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
st_orig.plot()
st.plot()
hs = []
sens = []
for sta in stas:
f = open('Results_Swept_Sine' + sta, 'r')
for line in f:
line = line.split(',')
fs.append(float(line[4]))
hs.append(float(line[5]))
sens.append(float(line[6]))
chans.append(line[0])
f.close()
#####################################################Make mean
for triple in zip(fs, hs, sens):
paz = corn_freq_2_paz(triple[0], triple[1])
h, f = paz_to_freq_resp(paz['poles'], paz['zeros'], 1., 1. / 1000., 2**14,
True)
h *= triple[2]
if 'hmean' not in vars():
hmean = h
else:
hmean += h
hmean *= 1. / 9.
fig = plt.figure(1, figsize=(12, 12))
ax = fig.add_subplot(111)
plt.subplots_adjust(hspace=0.001)
for idx, quad in enumerate(zip(chans, fs, hs, sens)):
if quad[0] == 'HHZ':
return comp
f = 1. /(2.*np.pi / abs(pazTest['poles'][0]))
h = abs(pazTest['poles'][0].real)/abs(pazTest['poles'][0])
sen = 75900.
pazTest['sensitivity'] = sen
x = np.array([f, h, sen])
bf = fmin_bfgs(resi,x)
#else:
#bf= x
pazNEW = corn_freq_2_paz(bf[0],bf[1])
pazNEW['sensitivity'] = bf[2]
stTemp = st.copy()
st[0].simulate(paz_remove=pazNEW)
st[1].simulate(paz_remove=paz)
stTemp[0].simulate(paz_remove=pazTest)
st.filter('highpass',freq=1./30.)
stTemp.filter('highpass',freq=1./30.)
rat = st[0].std()/st[1].std()
print('Here is the ratio of ' + sta + ' ' + chan + ' : ' + str(rat))
ffile.write(chan + ', ' + str(f) + ', ' + str(h) + ', ' + str(sen) + ', ' + str(bf[0]) + ', ' + str(bf[1]) + ', ' + str(bf[2]) + ', ' + str(rat) + ', ' + str(resi(x)) +', ' + str(resi(bf)) + '\n')
index, value = xcorr(st[0],st[1], 300)
def peak_ground_motion(data, delta, freq, damp=0.1):
"""
Peak ground motion parameters
Compute the maximal displacement, velocity, acceleration and the peak
ground acceleration at a certain frequency (standard frequencies for
ShakeMaps are 0.3/1.0/3.0 Hz).
Data must be displacement
:type data: :class:`~numpy.ndarray`
:param data: Data in displacement to convolve with pendulum at freq.
:type delta: float
:param delta: Sampling interval
:type freq: float
:param freq: Frequency in Hz.
:type damp: float
:param damp: damping factor. Default is set to 0.1
:rtype: (float, float, float, float)
:return: Peak Ground Acceleration, maximal displacement, velocity,
acceleration
"""
data = data.copy()
# Displacement
if abs(max(data)) >= abs(min(data)):
m_dis = abs(max(data))
else:
m_dis = abs(min(data))
# Velocity
data = np.gradient(data, delta)
if abs(max(data)) >= abs(min(data)):
m_vel = abs(max(data))
else:
m_vel = abs(min(data))
# Acceleration
data = np.gradient(data, delta)
if abs(max(data)) >= abs(min(data)):
m_acc = abs(max(data))
else:
m_acc = abs(min(data))
samp_rate = 1.0 / delta
t = freq * 1.0
d = damp
omega = (2 * 3.14159 * t)**2
paz_sa = corn_freq_2_paz(t, damp=d)
paz_sa['sensitivity'] = omega
paz_sa['zeros'] = []
data = simulate_seismometer(data,
samp_rate,
paz_remove=None,
paz_simulate=paz_sa,
taper=True,
simulate_sensitivity=True,
taper_fraction=0.05)
if abs(max(data)) >= abs(min(data)):
pga = abs(max(data))
else:
pga = abs(min(data))
return (pga, m_dis, m_vel, m_acc)
sens.append(float(line[6]))
chans.append(line[0])
f.close()
fig = plt.figure(1, figsize=(12, 12))
plt.subplots_adjust(hspace=0.001)
for idx, quad in enumerate(zip(chans, fs, hs, sens)):
if quad[0] == 'HHZ':
color = 'C0'
elif quad[0] == 'HHN':
color = 'C1'
else:
color = 'C2'
print(quad[1])
print(quad[2])
paz = corn_freq_2_paz(quad[1], quad[2])
h, f = paz_to_freq_resp(paz['poles'], paz['zeros'], 1., 1. / 1000., 2**14,
True)
h *= quad[3]
plt.subplot(2, 1, 1)
if idx < 3:
plt.loglog(f, np.abs(h), c=color, label=quad[0])
else:
plt.loglog(f, np.abs(h), c=color)
plt.text(.11, .5 * 10**5, 'A)', fontsize=24)
plt.xlim((.1, 100.))
plt.xticks([])
plt.legend(loc=4)
plt.ylabel('Amplitude (Counts/m/s)')
plt.subplot(2, 1, 2)
from obspy.signal.cross_correlation import templates_max_similarity
from pprint import pprint
import matplotlib.pyplot as plt
from obspy.signal.invsim import corn_freq_2_paz
from eqcorrscan.core.template_gen import multi_template_gen
from obspy.core.event import read_events
from eqcorrscan.utils import pre_processing
from obspy.signal.invsim import corn_freq_2_paz
paz = {
'sensitivity': 1258650000.0,
'poles': [-0.074 + 0.074j, -0.074 - 0.074j, -222 + 222j, -222 - 222j],
'gain': 98570.0,
'zeros': [0j, 0j]
}
paz_1hz = corn_freq_2_paz(1.0, damp=0.707) # 1Hz instrument
paz_1hz['sensitivity'] = 1.0
catalog = read_events('ludian.xml')
for e in catalog:
picks = e.picks
for p in picks:
print(p)
ynst = read("2014080316.YN.mseed").sort(['starttime']).trim()
st = ynst.select(station='ZAT')
# st += ynst.select(station='QIJ')
# st += ynst.select(station='PGE')
# st += ynst.select(station='DOC')
# st += ynst.select(station='XUW')
def peak_ground_motion(data, delta, freq, damp=0.1):
"""
Peak ground motion parameters
Compute the maximal displacement, velocity, acceleration and the peak
ground acceleration at a certain frequency (standard frequencies for
ShakeMaps are 0.3/1.0/3.0 Hz).
Data must be displacement
:type data: :class:`~numpy.ndarray`
:param data: Data in displacement to convolve with pendulum at freq.
:type delta: float
:param delta: Sampling interval
:type freq: float
:param freq: Frequency in Hz.
:type damp: float
:param damp: damping factor. Default is set to 0.1
:rtype: (float, float, float, float)
:return: Peak Ground Acceleration, maximal displacement, velocity,
acceleration
"""
data = data.copy()
# Displacement
if abs(max(data)) >= abs(min(data)):
m_dis = abs(max(data))
else:
m_dis = abs(min(data))
# Velocity
data = np.gradient(data, delta)
if abs(max(data)) >= abs(min(data)):
m_vel = abs(max(data))
else:
m_vel = abs(min(data))
# Acceleration
data = np.gradient(data, delta)
if abs(max(data)) >= abs(min(data)):
m_acc = abs(max(data))
else:
m_acc = abs(min(data))
samp_rate = 1.0 / delta
T = freq * 1.0
D = damp
omega = (2 * 3.14159 * T) ** 2
paz_sa = corn_freq_2_paz(T, damp=D)
paz_sa['sensitivity'] = omega
paz_sa['zeros'] = []
data = simulate_seismometer(data, samp_rate, paz_remove=None,
paz_simulate=paz_sa, taper=True,
simulate_sensitivity=True, taper_fraction=0.05)
if abs(max(data)) >= abs(min(data)):
pga = abs(max(data))
else:
pga = abs(min(data))
return (pga, m_dis, m_vel, m_acc)
st[4].stats.paz = AttribDict({
'poles': [(-4.39823 + 4.48709j), (-4.39823 - 4.48709j), (-2.105 + 0j)],
'zeros': [0j, 0j, 0j],
'sensitivity':
222222228.10910088,
'gain':
1.0
})
st[4].stats.coordinates = AttribDict({
'latitude': 48.108730,
'elevation': 0.450000,
'longitude': 11.583157
})
# Instrument correction to 1Hz corner frequency
paz1hz = corn_freq_2_paz(1.0, damp=0.707)
st.simulate(paz_remove='self', paz_simulate=paz1hz)
# Execute array_processing
kwargs = dict(
# slowness grid: X min, X max, Y min, Y max, Slow Step
sll_x=-3.0,
slm_x=3.0,
sll_y=-3.0,
slm_y=3.0,
sl_s=0.03,
# sliding window properties
win_len=1.0,
win_frac=0.05,
# frequency properties
frqlow=1.0,
#files = glob.glob('okdata/' + sta +'/*/*/*' + chan + '*.seed')
#st= Stream()
#for idx, curfile in enumerate(files):
#print('On ' + str(idx+1) + ' of ' + str(len(files)))
#st= read(curfile)
#if idx == 0:
#ppsd = PPSD(st[0].stats, paz)
#ppsd.add(st)
#ppsd.save_npz(st[0].id + 'PDF.npz')
debug = True
string = 'newdata/output_raw/2017/XX/FF'
pazNEW = corn_freq_2_paz(4.9, .96)
pazNEW['sensitivity'] = 75900.
chans = ['HHZ', 'HHE', 'HHN']
for idx, chan in enumerate(chans):
st = Stream()
for s in range(1, 9):
if debug:
print('On ' + str(s) + ' ' + chan)
st += read(string + str(s) + '/' + chan + '.D/XX*')
stime = UTCDateTime('2017-05-03T01:10:00.0')
etime = UTCDateTime('2017-05-04T01:10:00.0')
st.trim(starttime=stime, endtime=etime)
st.decimate(4)
'elevation': 0.450000,
'longitude': 11.583049})
st[4].stats.paz = AttribDict({
'poles': [(-4.39823 + 4.48709j), (-4.39823 - 4.48709j), (-2.105 + 0j)],
'zeros': [0j, 0j, 0j],
'sensitivity': 222222228.10910088,
'gain': 1.0})
st[4].stats.coordinates = AttribDict({
'latitude': 48.108730,
'elevation': 0.450000,
'longitude': 11.583157})
# Instrument correction to 1Hz corner frequency
paz1hz = corn_freq_2_paz(1.0, damp=0.707)
st.simulate(paz_remove='self', paz_simulate=paz1hz)
# Execute array_processing
stime = obspy.UTCDateTime("20080217110515")
etime = obspy.UTCDateTime("20080217110545")
kwargs = dict(
# slowness grid: X min, X max, Y min, Y max, Slow Step
sll_x=-3.0, slm_x=3.0, sll_y=-3.0, slm_y=3.0, sl_s=0.03,
# sliding window properties
win_len=1.0, win_frac=0.05,
# frequency properties
frqlow=1.0, frqhigh=8.0, prewhiten=0,
# restrict output
semb_thres=-1e9, vel_thres=-1e9, timestamp='mlabday',
stime=stime, etime=etime
import numpy as np
import matplotlib.pyplot as plt
import obspy
from obspy.clients.arclink import Client
from obspy.signal.invsim import corn_freq_2_paz, simulate_seismometer
# Retrieve data via ArcLink
# please provide a valid email address for the keyword user
client = Client(user="******")
t = obspy.UTCDateTime("2009-08-24 00:20:03")
st = client.get_waveforms('BW', 'RJOB', '', 'EHZ', t, t + 30)
paz = client.get_paz('BW', 'RJOB', '', 'EHZ', t)
# 1Hz instrument
one_hertz = corn_freq_2_paz(1.0)
# Correct for frequency response of the instrument
res = simulate_seismometer(st[0].data.astype('float32'),
st[0].stats.sampling_rate,
paz,
inst_sim=one_hertz)
# Correct for overall sensitivity
res = res / paz['sensitivity']
# Plot the seismograms
sec = np.arange(len(res)) / st[0].stats.sampling_rate
plt.subplot(211)
plt.plot(sec, st[0].data, 'k')
plt.title("%s %s" % (st[0].stats.station, t))
plt.ylabel('STS-2')
plt.subplot(212)
