A SEISMIC CLASSIFICATION MODEL

Abstract

The report is intended as an introduction to one possible approach to the seismic classification problem. It develops a very general classification model using automatic non-parametric learning based on limited data of known classification. The model accepts discriminants extracted from the seismogram and yields the probability that the input was due to an earthquake or an explosion. Thus, the discriminants are assumed to be available as inputs. Pattern recognition as used here is defined, the classification procedure is outlined, the adaptive estimation of joint probability-densities from a finite number of multi-dimensional vectors of known classification (the learning model) is discussed, a simplified flow diagram of the learning model is presented, and the selection of necessary control parameters is investigated.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1967
Accession Number
AD0659161

Entities

People

  • J. W. Clark

Organizations

  • MITRE Corporation

Tags

Communities of Interest

  • C4I
  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Cell Size
  • Data Science
  • Decision Theory
  • Distribution Functions
  • Ellipsoids
  • Equations
  • Information Science
  • Measurement
  • Nuclear Explosions
  • Pattern Recognition
  • Probabilistic Models
  • Probability Density Functions
  • Probability Distributions
  • Random Variables
  • Standards
  • Statistics

Readers

  • Computational Modeling and Simulation
  • Computer Vision.
  • Seismology

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms