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pure and applied geophysics

, Volume 135, Issue 1, pp 61–75 | Cite as

Focus-of-attention techniques in the automatic interpretation of seismograms

  • Claudio Chiaruttini
Article

Abstract

The focus-of-attention techniques implemented in SNA2, a knowledge-based system for seismogram interpretation, are presented. They consist of data compression of the input digital records, scanning of the compressed traces to detect candidate seismograms and extraction of seismogram features. A criterion is given to rate the clarity of seismograms; the clarity defines the order in which the system will consider them to build up the interpretation. The proposed techniques are simple and fast; they allow quick rejection of noise and focussing the attention of the system on the portions of traces containing relevant information.

Key words

Seismology seismic networks artificial intelligence expert systems automatic interpretation seismic event detection data compression 

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References

  1. Barr, A., andFeigenbaum, E. A.,The Handbook of Artificial Intelligence (Pitman, London 1982).Google Scholar
  2. Brownston, L., Farrell, R., Kant, E., andMartin, N.,Programming Expert Systems in OPSS (Addison-Wesley, New York 1985).Google Scholar
  3. Chiaruttini, C., andRoberto, V. (1988),Automation of Seismic Network Signal Interpretation: An Artificial Intelligence Approach, Il Nuovo Cimento11C, 327–338.Google Scholar
  4. Chiaruttini, C., Roberto, V., andSaitta, F. (1989),Artificial Intelligence Techniques in Seismic Signal Interpretation, Geophys. J. Int.98, 223–232.Google Scholar
  5. Dixon, W. J., andMassey, F. J. Jr.,Introduction to Statistical Analysis (McGraw-Hill, New York 1957).Google Scholar
  6. Frost, R. A.,Introduction to Knowledge Base Systems (Collins, London 1986).Google Scholar
  7. Nii, H. P. (1986),Blackboard Systems—Blackboard Application Systems, Blackboard Systems from a Knowledge Engineering Perspective, The AI Magazine, August, pp. 82–106.Google Scholar
  8. Nii, H. P., andFeigenbaum, E. A.,Rule-based understanding of signals. InPattern-directed Inference Systems (eds. Waterman, D. A., and Hayes-Roth, F.) (Academic Press, New York 1978), pp. 483–501.Google Scholar
  9. Nii, H. P., Feigenbaum, E. A., Anton, J. J., andRockmore, A. J. (1982),Signal-to-Symbol Transformation: The HASP/SIAP Case Study, The AI Magazine, Spring, pp. 23–35.Google Scholar
  10. Roberto, V., andChiaruttini, C.,Signal understanding in the seismological domain: A knowledge-based system, InProc. Tenth International Workshop on Expert Systems and their Applications (Avignon 1990) vol. 1, pp. 89–104.Google Scholar
  11. Roberto, V., Paglietti, P., andChiaruttini, C. (1990),Syntactic Filtering and Recognition of Wide-band Noise Waveforms, Signal Processing19, 43–60.Google Scholar

Copyright information

© Birkhäuser Verlag 1991

Authors and Affiliations

  • Claudio Chiaruttini
    • 1
  1. 1.Istituto di Geodesia e GeofisicaUniversità di TriesteTriesteItaly

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