HR: 0800h
AN: S21A-0266    [Abstracts]
TI: Automatic Phase Picker for Local and Teleseismic Events Using Wavelet Transform and Simulated Annealing
AU: * Gaillot, P
EM: philippe.gaillot@univ-pau.fr
AF: Imagerie G‚ophysique FRE 2639, UPPA, Avenue de l'Universit‚, BP 1155, Pau, 64013 France
AU: Bardaine, T
EM: Thomas.bardainne@univ-pau.fr
AF: Imagerie G‚ophysique FRE 2639, UPPA, Avenue de l'Universit‚, BP 1155, Pau, 64013 France
AU: Lyon-Caen, H
EM: Helene.Lyon-Caen@ens.fr
AF: Laboratoire de Geologie, Ecole Normale Superieure, 24 rue Lhomond, Cedex 05, Paris, 75231 France
AB: Since recent years, various automatic phase pickers based on the wavelet transform have been developed. The main motivation for using wavelet transform is that they are excellent at finding the characteristics of transient signals, they have good time resolution at all periods, and they are easy to program for fast execution. Thus, the time-scale properties and flexibility of the wavelets allow detection of P and S phases in a broad frequency range making their utilization possible in various context. However, the direct application of an automatic picking program in a different context/network than the one for which it has been initially developed is quickly tedious. In fact, independently of the strategy involved in automatic picking algorithms (window average, autoregressive, beamforming, optimization filtering, neuronal network), all developed algorithms use different parameters that depend on the objective of the seismological study, the region and the seismological network. Classically, these parameters are manually defined by trial-error or calibrated learning stage. In order to facilitate this laborious process, we have developed an automated method that provide optimal parameters for the picking programs. The set of parameters can be explored using simulated annealing which is a generic name for a family of optimization algorithms based on the principle of stochastic relaxation. The optimization process amounts to systematically modifying an initial realization so as to decrease the value of the objective function, getting the realization acceptably close to the target statistics. Different formulations of the optimization problem (objective function) are discussed using (1) world seismicity data recorded by the French national seismic monitoring network (ReNass), (2) regional seismicity data recorded in the framework of the Corinth Rift Laboratory (CRL) experiment, (3) induced seismicity data from the gas field of Lacq (Western Pyrenees), and (4) micro-seismicity data from glacier monitoring. The developed method is discussed and tested using our wavelet version of the standard STA-LTA algorithm.
DE: 7299 General or miscellaneous
SC: Seismology [S]
MN: 2004 AGU Fall Meeting