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A Review of Automated First-Break Picking and Seismic Trace Editing Techniques

  • Michael D. McCormack
Part of the Modern Approaches in Geophysics book series (MAGE, volume 21)

Abstract

This chapter describes two software systems, based on artificial neural networks (ANNs), which have largely automated the highly labor-intensive seismic processes of first-break refraction picking and trace editing. The underlying mechanism of these two processes relies heavily on pattern recognition techniques. In contrast, most other seismic processing algorithms depend on more traditional signal processing theory. This explains, at least in part, why first-break picking and trace editing have remained in the domain of human processors, until recently. With the development of ANN theory, powerful pattern recognition algorithms have now become available to address these two problem areas of seismic processing. This chapter describes the various approaches that researchers have employed in automating first-break picking and seismic trace editing, in particular those based on ANNs. These ANN-based systems can achieve accuracies ranging from 94% to 99%, with a fraction of the human effort required for a manual analysis.

Keywords

Seismic Data Seismic Attribute Expand Abstract Seismic Trace Seismic Data Processing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 2003

Authors and Affiliations

  • Michael D. McCormack
    • 1
  1. 1.Optimization Associates, Inc.PlanoUSA

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