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The PhasePApy (Chen and Holland, 2016) has two sub-packages: the PhasePicker and Associator, aiming to identify phase arrival onsets and associate them to phase types respectively. The PhasePicker and Associator can work jointly or separately. Three autopickers are implemented in the PhasePicker sub-package: the frequency band picker (FBPicker), the Akaike Information Criteria (AIC) function derivative picker (AICDPicker), and the kurtosis picker (KTPicker). The FBpicker is a modified method from Lomax et al. (2012). The FBpicker automatically adapts to instrumentation sampling rate and no need to specify triggering threshold for P- or S-waves. The AICDpicker uses the derivation of the AIC function to determine the characteristic function (Sleeman and van Eck, 1999; Maeda, 1985). The KTpicker in the PhasePicker is a kurtosis-based picker (Saragiotis et al., 2002; Panagiotakis et al., 2008). There are two associators included in the Associator sub-package: the 1D and 3D Associator, which determine phase types for picks can best fit potential earthquakes by minimizing root mean square (RMS) residuals of the misfits. Both two associators use travel-time look up tables to determine the best estimation of the earthquake location and evaluate the phase type for picks.
This program is developed to leverage the growing number of scientific libraries being written in Python mostly notably Obspy (Beyreuther et al., 2010). The choice of making our algorithms compatible with Obspy means that all data formats and ac- cess methods supported by Obspy are naturally supported, such as SEED, MiniSEED, SAC, SEGY, and etc. The other libraries on which Obspy and our algorithms rely are invaluable Numpy (Oliphant, 2007), Scipy (Jones et al., 2001), and Matplotlib (Hunter, 2007).
The PhasePApy uses those open libraries to implement the picker routines. The PhasePicker requires the Python libraries Obspy, Numpy, and Scipy, maybe Mat- plotlib if the user need visualize the performance. The Associator requires several open libraries and modules for the processing such as Numpy, Sqlalchemy, datetime, operator, itertools, and search, maybe more modules for plotting (check plot modules in Associator sub-package if necessary). This documentation provides two examples to demonstrate how to tune the parameters in these two packages. The code is colored in green, output in blue, and parameter description in red.
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