Seismic event classification system

Patent · Sat Jan 01 00:00:00 EST 1994

OSTI ID:869651

Dowla, Farid U ^[1]; Jarpe, Stephen P ^[2]; Maurer, William ^[3]

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1. Castro Valley, CA
2. Brentwood, CA
3. Livermore, CA

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. Self-organizing 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 time-frequency distribution, its binary representation, and finally a shift-invariant representation, which is the magnitude of the two-dimensional Fourier transform (2-D FFT) of the binary time-frequency distribution. This pre-processed 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 pre-defined. The results from the SONNs together with an expert seismologist's classification are then used to derive event classification probabilities.

• Research Organization: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
• DOE Contract Number: W-7405-ENG-48
• Assignee: United States Department of Energy (Washington, DC)
• Patent Number(s): US 5373486
• OSTI ID: 869651
• Country of Publication: United States
• Language: English

Related Subjects

seismic
event
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interpretation
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example
teleseismic
regional
local
vehicular
noise
self-organizing
neural
networks
sonns
classifying
events
kohonen
adaptive
resonance
theory
useful
purpose
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time-frequency
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shift-invariant
magnitude
two-dimensional
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2-d
fft
pre-processed
input
fed
look
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sonn
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types
cluster
pre-defined
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expert
seismologist
derive
probabilities
seismic data
seismic event
neural network
fourier transform
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frequency distribution
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