Bayesian Differential Moment Tensor Inversion of Clustered Nuclear Tests

Abstract

Accurate seismic moment tensor measurement plays an important role in nuclear test monitoring and yield estimation. However, conventional source moment tensor inversions require accurate Greens functions between source and receivers, and rely mostly on regional data coverage which could be sparse or incomplete in azimuthal coverage. In this study, we propose a new method, differential moment tensor inversion (diffMT), which adopts relative measurements to remove the path effects shared by different events at the same station, thereby improving the accuracy of source parameter determination. We apply diffMT to the four nuclear tests conducted by North Korea from 2009 to 2016. We found that, compared with the traditional method of waveform-based moment tensor inversion, diffMT more tightly constrains the moment tensor components of these nuclear tests, measures the energy release of these events more accurately, and better distinguishes their isotropic and tectonic release components. Our results provide potential insights on the explosion mechanisms and physical processes of North Korea's nuclear tests.

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Document Details

Document Type
Technical Report
Publication Date
Dec 30, 2020
Accession Number
AD1124660

Entities

People

  • Donald V. Helmberger
  • Zhe Jia
  • Zhongwen Zhan

Organizations

  • California Institute of Technology

Tags

Communities of Interest

  • Counter IED
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Bayesian Networks
  • California
  • Computational Science
  • Frequency
  • Frequency Bands
  • Governments
  • Greens Functions
  • Information Processing
  • Information Science
  • Love Waves
  • Measurement
  • Military Research
  • Monte Carlo Method
  • North Korea
  • Nuclear Explosions
  • Societies
  • Standards
  • Surface Waves
  • Three Dimensional

Readers

  • Seismology

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference