Seismic event classification system
Abstract
In the computer interpretation of seismic data, the critical first step is to identify the general class of an unknown event. For example, the classification might be: teleseismic, regional, local, vehicular, or noise. Selforganizing neural networks (SONNs) can be used for classifying such events. Both Kohonen and Adaptive Resonance Theory (ART) SONNs are useful for this purpose. Given the detection of a seismic event and the corresponding signal, computation is made of: the timefrequency distribution, its binary representation, and finally a shiftinvariant representation, which is the magnitude of the twodimensional Fourier transform (2D FFT) of the binary timefrequency distribution. This preprocessed input is fed into the SONNs. These neural networks are able to group events that look similar. The ART SONN has an advantage in classifying the event because the types of cluster groups do not need to be predefined. The results from the SONNs together with an expert seismologist's classification are then used to derive event classification probabilities.
 Inventors:
 (Castro Valley, CA)
 (Brentwood, CA)
 (Livermore, CA)
 Publication Date:
 Research Org.:
 Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
 OSTI Identifier:
 869651
 Patent Number(s):
 US 5373486
 Assignee:
 United States Department of Energy (Washington, DC) LLNL
 DOE Contract Number:
 W7405ENG48
 Resource Type:
 Patent
 Country of Publication:
 United States
 Language:
 English
 Subject:
 seismic; event; classification; computer; interpretation; data; critical; step; identify; example; teleseismic; regional; local; vehicular; noise; selforganizing; neural; networks; sonns; classifying; events; kohonen; adaptive; resonance; theory; useful; purpose; detection; corresponding; signal; computation; timefrequency; distribution; binary; representation; finally; shiftinvariant; magnitude; twodimensional; fourier; transform; 2d; fft; preprocessed; input; fed; look; similar; sonn; advantage; types; cluster; predefined; results; expert; seismologist; derive; probabilities; seismic data; seismic event; neural network; fourier transform; neural networks; frequency distribution; neural net; /367/181/702/
Citation Formats
Dowla, Farid U., Jarpe, Stephen P., and Maurer, William. Seismic event classification system. United States: N. p., 1994.
Web.
Dowla, Farid U., Jarpe, Stephen P., & Maurer, William. Seismic event classification system. United States.
Dowla, Farid U., Jarpe, Stephen P., and Maurer, William. Sat .
"Seismic event classification system". United States.
doi:. https://www.osti.gov/servlets/purl/869651.
@article{osti_869651,
title = {Seismic event classification system},
author = {Dowla, Farid U. and Jarpe, Stephen P. and Maurer, William},
abstractNote = {In the computer interpretation of seismic data, the critical first step is to identify the general class of an unknown event. For example, the classification might be: teleseismic, regional, local, vehicular, or noise. Selforganizing neural networks (SONNs) can be used for classifying such events. Both Kohonen and Adaptive Resonance Theory (ART) SONNs are useful for this purpose. Given the detection of a seismic event and the corresponding signal, computation is made of: the timefrequency distribution, its binary representation, and finally a shiftinvariant representation, which is the magnitude of the twodimensional Fourier transform (2D FFT) of the binary timefrequency distribution. This preprocessed input is fed into the SONNs. These neural networks are able to group events that look similar. The ART SONN has an advantage in classifying the event because the types of cluster groups do not need to be predefined. The results from the SONNs together with an expert seismologist's classification are then used to derive event classification probabilities.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sat Jan 01 00:00:00 EST 1994},
month = {Sat Jan 01 00:00:00 EST 1994}
}

In the computer interpretation of seismic data, the critical first step is to identify the general class of an unknown event. For example, the classification might be: teleseismic, regional, local, vehicular, or noise. Selforganizing neural networks (SONNs) can be used for classifying such events. Both Kohonen and Adaptive Resonance Theory (ART) SONNs are useful for this purpose. Given the detection of a seismic event and the corresponding signal, computation is made of: the timefrequency distribution, its binary representation, and finally a shiftinvariant representation, which is the magnitude of the twodimensional Fourier transform (2D FFT) of the binary timefrequency distribution. Thismore »

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