# 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.

- 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:
- W-7405-ENG-48

- 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; self-organizing; neural; networks; sonns; classifying; events; kohonen; adaptive; resonance; theory; useful; purpose; detection; corresponding; signal; computation; time-frequency; distribution; binary; representation; finally; shift-invariant; magnitude; two-dimensional; fourier; transform; 2-d; fft; pre-processed; input; fed; look; similar; sonn; advantage; types; cluster; pre-defined; 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. 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. 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.},

doi = {},

journal = {},

number = ,

volume = ,

place = {United States},

year = {1994},

month = {1}

}