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. 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. 21 figures.
- Inventors:
- Issue Date:
- OSTI Identifier:
- 6640467
- Patent Number(s):
- 5373486
- Application Number:
- PPN: US 8-013268
- Assignee:
- Dept. of Energy, Washington, DC (United States)
- DOE Contract Number:
- W-7405-ENG-48
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 3 Feb 1993
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES; 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; SEISMIC EVENTS; CLASSIFICATION; NEURAL NETWORKS; DATA ANALYSIS; SEISMIC WAVES; 580000* - Geosciences; 990200 - Mathematics & Computers
Citation Formats
Dowla, F U, Jarpe, S P, and Maurer, W. Seismic event classification system. United States: N. p., 1994.
Web.
Dowla, F U, Jarpe, S P, & Maurer, W. Seismic event classification system. United States.
Dowla, F U, Jarpe, S P, and Maurer, W. Tue .
"Seismic event classification system". United States.
@article{osti_6640467,
title = {Seismic event classification system},
author = {Dowla, F U and Jarpe, S P and Maurer, W},
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. 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. 21 figures.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1994},
month = {12}
}