2016 release analysis of APASVO-P wave phase automatic pickup

• Why automatic attractive?

1. Large amount of seismic data;
2. If manually,it depends Om experience of analyst;
3. Quliaty can obscured by several factors such as background and non-stationary noise from diverse sources;

• The method used by the software is

Sta-lta (very effective for amplitude mutation recognition);

AMPA;

An autogressive aic-based picking method;

• Software Benefits

1. Applicable to multi-platform;
2. Can handle large quantities of data in different file formats;
3. Can be used for microseismicity of the earthquake phase pickup;

• Software dependent dependencies

Scientific computing packages such as Obspy,scipy,numpy,and matplotlib

• Effect of pickup accuracy on subsequent work

1. tomographic inversion of Active-source data (seismic phase imaging analysis)
2. Hypocenter location

• Author's idea: The previous pickup method is divided into two kinds of coarse pickup (relatively imprecise) and precise pickup (precise).

2. Sta/lta:naive version of the method has too broad range in parameter configuration; (a large number of statistical experiments may be required for earthquakes of different distances)
4. Direct use of the envelope function with a dynamic trigger level;(Baer and kradolfer,1987)
6. Higher-order statistic; (Saragiotis et al.,2002; Kuperkoch et al,.2010; Nippress et al.,2008)
8. polarization approaches; (Montalbetti and kanasewich,1970; Kurzon et al.,2014)
10. Local-maxima distribution (Panagiotakis et al.,2008)
12. Wavelet-based methods (Zhang et al.,2003)
14. Even pattern recognition methods (tong,1995)
16. Neural networks (Dai andmacbeth,1995; Gentili and michelini,2006)
18. Computer vision inspired methods (joswig,1990)
20. Autogressive models:the Standard one named Ar-aic (establishment of a autoregressive model of background noise and arrival phase): Find the phase coming point that the locally statio Nary AR model attains the minimum value of the Akaike information criterion. (When the minimum value of a standard is reached);: This method is more accurate to pick up, but it is strongly recommended for initial pickup work which has already had preliminary estimate.

• Solutions for low signal-to-noise ratios

AMPA algorithm (Adaptive muti-band picking algorithm): Related Article-alvarez et al.,2013

It applied a serises of filtering stages to the input signal in order to weaken ambient noise and strengthen the arrival of the earthquake phase.

1. Step1:using Filter Bank to eatimate the signal envelope and equalize (compensation) noise for each subband.filter band
2. The resultant envelope is filterd by a use of set of Filters,each one designed different length in order to enhance the P Hase arrival and Lower emergent noised.

• Software method Support

Sta/lta and Ampa picking algorithm, AIC supplemented;

• Software framework

1. Apasvo-gui: Interactive interface, allowing users to manually change the value;
2. Apasvo-detector: Allows the use of command line to calculate, there are batch processing and supervision mode of the two modes of selection, supervision is used to interactively determine whether a satisfactory pickup, whether accepted. Batches are not addressed during the process.
3. Apasvo-generator: Allows the use of command lines to generate seismic wave trajectories and allows for background noise;

2016 release analysis of APASVO-P wave phase automatic pickup