Automatic Picker Developments and Optimization: A Strategy for Improving the Performances of Automatic Phase Pickers

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, particle 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. The family of the energy methods is probably the largest, and includes the algorithms of Allen (1978) and Baer and Kradolfer (1987). In this class of algorithms a possible pick is declared when the ratio between a short-term average (STA) of the signal (or of a characteristic function of the signal) and its long-term average (LTA) exceeds a certain threshold parameter (for this reason they are often also called “STA/LTA” algorithms). The algorithms of the second class, the autoregressive methods, determine an optimal pick time after an arrival has been already detected ( e.g. , by an energy method). These algorithms study the variation of the statistical properties of the signal, trying to find the point in time that best separates the signal from the noise (Sleeman and Van …