Highlights

Automatic picker developments and optimization: An optimization strategy for improving the performance of automatic phase pickers

Seismological Research Letters , 83, 3, 541-554, 2012, doi: 10.1785/gssrl.83.3.541
M. Vassallo, C. Satriano and A. Lomax

Abstract

Modern seismic networks, either permanent or temporary, can nowadays easily produce such large volumes of data that manual analysis is not possible. Effective and consistent automatic procedures for the detection and processing of seismic events are required to homogeneously process large datasets and to provide rapid responses in near real time. One of the first modular components of the automatic analysis chain is generally a tool for the identification of seismic phases on the recorded seismic waveforms and the determination of their onset time, a process known as phase arrival picking. A variety of procedures for the automatic picking of phase arrivals have been proposed and successfully implemented during the last decades; almost all of these methodologies are based on the analysis of variations in amplitude, frequency, article motion, or a combination of these. They typically deal with the first arriving P phase; less frequently they are able to detect secondary arrivals. Most of the picking algorithms can be classified into three main families: energy methods, autoregressive methods, and neural network approaches.

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