Open AccessCharacterization of seismic phases—an automatic analyser for seismograms
Author(s) -
Tong Cheng
Publication year - 1995
Publication title -
geophysical journal international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.302
H-Index - 168
eISSN - 1365-246X
pISSN - 0956-540X
DOI - 10.1111/j.1365-246x.1995.tb06900.x
Subject(s) - seismogram , analyser , phase (matter) , wavelet , waveform , computer science , characterization (materials science) , set (abstract data type) , synthetic seismogram , pattern recognition (psychology) , algorithm , geology , seismology , artificial intelligence , optics , physics , telecommunications , radar , quantum mechanics , programming language
SUMMARY An automatic analyser which characterizes, as well as detects, seismic phases can be a significant help in the interpretation of seismograms. Such an analyser has been constructed using concepts from work in artificial intelligence. A seismogram is recognized as a hierarchical structure of subunits and the features characterizing a phase are extracted via a structural analysis. The automatic analyser detects the onsets of phases, separates the relevant waveform segments, fits phase structures to standard wavelets, and generates a set of five parameters (‘a quintuple’) to characterize a detected phase. The whole analysis system requires just a few input parameters and can be accomplished swiftly enough to allow continuous real‐time operation. The analysis procedure is adaptive, including updating of the estimate of the local frequency so that it can be applied to broad‐band records with a wide range of frequency content. By coupling the analysis procedure with real‐time filtering, very good results for phase detection and characterization can be achieved for weak distant events. The characterization of phase segments also provides useful information for the comparison of seismograms.