An Artificial Intelligence Technique for Automating Seismic Stratigraphic Interpretation,

Abstract

Studying the character of reflected seismic wavelets may reveal facts about the stratigraphy of the reflector. Computers can aid humans in this task by revealing structural similarities between various wavelets. The relational tree is a good way to represent a waveform's global structure. By representing a waveform as a relational tree, processing it symbolically, and clustering the processed trees, a seismic wave form recognition system can be constructed. The symbolic processing is based on a tree transformation. An objective function, which measures the effectiveness of such a transformation, utilizes the ratio of between-cluster to within-cluster scatter. The action of a tree transformation applied to tree spaces is the same as linear discriminants applied to feature spaces. When tested on simulated seismic data, the relational tree waveform recognition system performs well at high signal-to-noise ratios.

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Document Details

Document Type
Technical Report
Publication Date
Nov 25, 1986
Accession Number
ADA181094

Entities

People

  • Rui J. Defigueiredo
  • Scott W. Shaw

Organizations

  • Rice University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Case Studies
  • Computers
  • Electrocardiography
  • Engineering
  • Geology
  • Geophysics
  • Matched Filters
  • Pattern Recognition
  • Recognition
  • Reflectors
  • Seismic Reflection
  • Signal Processing
  • Universities
  • Visual Inspection
  • Waveforms

Readers

  • Coastal Oceanography
  • Graph Algorithms and Convex Optimization.
  • Radar Systems Engineering.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • Space
  • Space - Space Objects