Composition of Short-Period Regional Phases Inferred From Fennoscandian Array Data

Abstract

Short-period seismograms from local and regional events recorded at the NORESS and ARCESS arrays are being studied in order to understand the composition of local and regional phases and to separate the effects of structure and source parameters on the seismic signal. This is a precondition for accurate phase identification and accordingly, for reliable location of events within regional distances. F-K analysis is used to identify and locate arrivals in the seismograms and a composite of array beams is used as an approximation to each event. Interferences about structure differences at ARCESS and NORESS are made by comparing composite-seismogram record sections from the two sites. Synthetic P-wave seismograms are calculated for velocity models representing the average structure of the propagation paths. The effects of near-receiver and source structure on regional phases need to be understood and separated from the effects of propagation path. For this purpose receiver functions obtained from teleseismic events, recorded at the three-component, intermediate-period channels, EO at NORESS and ARCESS are being analyzed. Average Moho depth of 35 km is obtained under NORESS by stacking receiver functions from all events. The effects of a surface scatterer at Lake Mjosa are seen in one event.

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Document Details

Document Type
Technical Report
Publication Date
Dec 31, 1991
Accession Number
ADA253289

Entities

People

  • Ben Yan
  • Charles A. Langston
  • Kristin Vogfjord

Organizations

  • Pennsylvania State University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Atmospheric Sciences
  • Contracts
  • Earth Sciences
  • Geography
  • Geological Surveys
  • Geology
  • Geophysics
  • Monitoring
  • Pennsylvania
  • Phase Velocity
  • Planetary Sciences
  • Technical Information Centers
  • Travel Time
  • Two Dimensional
  • Wave Propagation
  • Waveforms

Fields of Study

  • Environmental science

Readers

  • Seismology

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference