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KTPicker
phasepapy.phasepicker.ktpicker
- obspy.core.ascii
- obspy.core.compatibility
- copy
- obspy.core.event
- obspy.core.event_header
- obspy.core.json
- matplotlib
- numpy
- matplotlib.pyplot
- obspy.core.quakeml
- obspy.core.scripts
- obspy.core.stream
- obspy.core.trace
- obspy.core.utcdatetime
- obspy.core.util
- KTPicker
- KTSummary
KTPicker is designed based on kurtosis.
Methods defined here:
init(self, t_win=1, t_ma=10, nsigma=6, t_up=0.2, nr_len=2, nr_coeff=2, pol_len=10, pol_coeff=10, uncert_coeff=3)
Parameter description:
- t_win : the time in seconds of moving window to calculate kurtosis
- t_ma : the time in seconds of the moving average window for dynamic threshold
- n_sigma : controls the level of threshold to trigger potential picks
- t_up : the time in seconds not allowed consecutive pick in this duration
- nr_len : noise ratio filter window length before and after potential picks used to calculate standard deviation
- nr_coeff : control threshold level to determine if remove the pick by comparing std or rms on both sides of each potential pick
- pol_len : window length in samples to calculate the standard deviation of waveform before the picks
- pol_coeff : determine if declare first motion as 'Compression' or 'Dilation' by comparing the first local extreme value after pick and standard deviation in previous window
- uncert_len : window length in time to calculate the rms of the CF before the picks, we make it as long as t_ma
- uncert_coeff : control the floating level based on the noise of CF
picks(self, tr)
Make picks, polarity, snr, and uncertainty.
The class calculate CF, threshold level, cleans the false picks, determines uncertainty, polarity and plot CF.
init(self, picker, trace)
pick_ident(self)
Clean false picks and Make picks.
plot_picks(self)
Plot picks and waveform.
plot_summary(self)
Plot CF.
polarity(self)
Determine polarity for declared picks.
threshold(self)
Control the threshold level with nsigma.
uncertainty(self)
Uncertainty is determined based on the noise level of CF.
winlen(self, index, trigger_ptnl, filter_length, t, dt)
Determine the filter window length. If the time difference between two picks is less than window length, use the picks interval as window.