- ar_pick(a, b, c, samp_rate, f1, f2, lta_p, sta_p, lta_s, sta_s, m_p, m_s, l_p, l_s, s_pick=True)
Pick P and S arrivals with an AR-AIC + STA/LTA algorithm.
The algorithm picks onset times using an Auto Regression - Akaike Information Criterion (AR-AIC) method. The detection intervals are successively narrowed down with the help of STA/LTA ratios as well as STA-LTA difference calculations. For details, please see [Akazawa2004].
An important feature of this algorithm is that it requires comparatively little tweaking and site-specific settings and is thus applicable to large, diverse data sets.
numpy.ndarray) – Z signal the data.
numpy.ndarray) – N signal of the data.
numpy.ndarray) – E signal of the data.
samp_rate (float) – Number of samples per second.
f1 (float) – Frequency of the lower bandpass window.
f2 (float) – Frequency of the upper .andpass window.
lta_p (float) – Length of LTA for the P arrival in seconds.
sta_p (float) – Length of STA for the P arrival in seconds.
lta_s (float) – Length of LTA for the S arrival in seconds.
sta_s (float) – Length of STA for the S arrival in seconds.
m_p (int) – Number of AR coefficients for the P arrival.
m_s (int) – Number of AR coefficients for the S arrival.
l_p (float) – Length of variance window for the P arrival in seconds.
l_s (float) – Length of variance window for the S arrival in seconds.
s_pick (bool) – If
True, also pick the S phase, otherwise only the P phase.
- Return type
A tuple with the P and the S arrival.