Optimized automatic pickers: application to the ANCORP data set

Abstract

SUMMARY It has become increasingly important to develop fast and accurate automatic procedures to process and fully exploit increasing large seismic data sets. Traditionally these data sets are processed manually, which requires significant amounts of both manpower and time with sometimes-variable results. We have developed a cost minimization approach to train three automatic pickers: an Sta/Lta, Tpd and the PAI-K picker at each station within a dense temporary network located in northern Chile and southern Bolivia. The optimum picking parameters for each station show regional variability and need to be adjusted individually to achieve the best performance. We developed a weighting scheme that uses four independent predictors of weight calibrated using a handpicked data subset, which mimics the picking by an expert seismologist. We use the fact that each of the three pickers highlights different properties of the observed seismic trace to combine two pickers that work in tandem. The first makes an initial pick before the second picker refines and improves the accuracy of the automatic pick. We find the tandem pickers improve the accuracy of the automatic picks when compared to the single automatic pickers. We demonstrate that following the cost minimization procedure described here the automatic picks have sufficient accuracy that they would be suitable for high-precision earthquake location, focal mechanism determination or high-resolution seismic tomography.