William M Maurer

Author:

William C Maurer

ISBN #:

0878141170

US Patent:

53734866, Dec 13, 1994

Filed:

Feb 3, 1993

Appl. No.:

8/013268

Inventors:

Farid U. Dowla - Castro Valley CA
Stephen P. Jarpe - Brentwood CA
William Maurer - Livermore CA

Assignee:

The United States Department of Energy - Washington DC

International Classification:

H04B 106

US Classification:

367135

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.