Investigations of Tectonic Stress

Abstract

Results relating to the analytical modeling of earthquakes by relaxation-source theory models are summarized. A general theory of failure using concepts of a generalized phase change is developed. Applications to explosion-earthquake discrimination and earthquake source parameter estimation, particularly tectonic stress determinations, are discussed and the theoretical basis for M sub b - M sub s discrimination and discrimination using spectra magnitude parameters is emphasized. Computer based signal analysis methods are described and applications to the estimation of discriminatory parameters are illustrated, using a large data set Eurasia. Observational methods and results for p wave spectral discrimination of earthquakes and explosions are described, and it is concluded that the observed behavior of these spectral magnitudes is in agreement with the theoretical predictions and can serve as a very powerful discrimination procedure. Stress field estimates are inferred from the set of world-wide M sub b - M sub s data for the period 1968-1974, for the North Pacific region. The results show zones of high stress, the maximum being near 1 kbar, and that these zones are also seismic gaps. Small earthquakes occurring in these high stress regions are more explosion-like than normal due to the high stress drops associated with them.

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Document Details

Document Type
Technical Report
Publication Date
Apr 26, 1977
Accession Number
ADA047610

Entities

People

  • Charles B. Archambeau

Organizations

  • University of Colorado Boulder

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Computational Science
  • Computer Programs
  • Data Sets
  • Doppler Effect
  • Elastic Properties
  • Elastic Waves
  • Equations
  • Failure Mode And Effect Analysis
  • Materials
  • Measurement
  • Mechanics
  • Surface Waves
  • Three Dimensional
  • United States
  • Wave Propagation
  • Waveforms

Readers

  • Seismology
  • Statistical inference.
  • Systems Analysis and Design

Technology Areas

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