The Dependence of Magnitude Uncertainty on Station Coverage.

Abstract

A useful event characterization parameter for relatively large (e.g., mb > 4) teleseismic events is mb-Ms, provided the events are not too deep. To use mb-Ms as a parameter with which to screen events of natural seismicity, at a given confidence level, the uncertainty (standard deviation) in mb-Ms must be quantified. This report presents a generalization to the current method of screening events using mb-Ms by including correlation between station observations due to station coverage. After a description of the current method used to estimate the uncertainty in mb-Ms, an analysis is presented, using all events currently available in the Reviewed Event Bulletin (REB) for which Ms was measured at six or more stations, to determine the dependence on station coverage. Generally, Ms measurements at stations which are located along the same direction from the event are positively correlated, while measurements at stations which are located at near right angles from the event are negatively correlated. Using the data to estimate the correlation between stations as a function of angle relative to the event location, a multivariate statistical model for the uncertainty in mb-Ms is given and applied to each of the 400 relevant events in the REB. An interpretation of the results and recommendations for treatment of station coverage is provided.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Aug 31, 1996
Accession Number
ADA321962

Entities

People

  • Gary D. Mccartor
  • Henry L. Gray
  • Mark D. Fisk
  • Steven Bottone

Tags

DTIC Thesaurus Topics

  • Atmospheric Sciences
  • Data Analysis
  • Data Science
  • Databases
  • Earth Sciences
  • Geography
  • Geophysics
  • Information Science
  • Measurement
  • Oceanography
  • Planetary Sciences
  • Radiation Patterns
  • Square Roots
  • Standards
  • Statistical Analysis
  • Statistics
  • Uncertainty

Readers

  • Seismology
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.