obspy.signal.trigger.ar_pick

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)[source]

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.

Parameters:

  • a (numpy.ndarray) – Z signal the data.

  • b (numpy.ndarray) – N signal of the data.

  • c (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:

tuple

Returns:

A tuple with the P and the S arrival.