Development and Tuning of a 3-D Stochastic Inversion Methodology for the European Arctic

High-resolution seismic models are a critical component of calibrating earth structure for improved seismic monitoring. We will in this study develop the Markov Chain Monte Carlo (MCMC) inversion method into an even stronger tool for deriving reliable three-dimensional seismic models of the crust an...

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Bibliographic Details
Main Authors: Bungum, Hilmar, Pasyanos, Michael E., Faleide, Jan I., Clark, Stephen A.
Other Authors: NORWEGIAN SEISMIC ARRAY (NORSAR) KJELLER
Format: Text
Language:English
Published: 2008
Subjects:
Seismology
Statistics and Probability
*ARCTIC REGIONS
*HIGH RESOLUTION
*STOCHASTIC PROCESSES
*SEISMIC DATA
MONITORING
EARTH MANTLE
MONTE CARLO METHOD
CALIBRATION
TRADE OFF ANALYSIS
MODELS
SYMPOSIA
UNCERTAINTY
DATA BASES
PROBABILITY
GEOPHYSICAL MODELS
Arctic
Online Access:http://www.dtic.mil/docs/citations/ADA516012
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA516012
Description
Summary:High-resolution seismic models are a critical component of calibrating earth structure for improved seismic monitoring. We will in this study develop the Markov Chain Monte Carlo (MCMC) inversion method into an even stronger tool for deriving reliable three-dimensional seismic models of the crust and upper mantle, based on multiple types of geophysical data sets. This will be done by tuning the method to the European Arctic through development of a probabilistic geophysical model. While a new and much improved model (BARENTS3D) recently has been developed for this region (Ritzmann et al., 2007), stochastic models have a potential to better represent our state of knowledge (and uncertainty) about geophysical structure because deterministic models do not express well the tradeoffs inherent in the data. Stochastic inverse methods also allow a more systematic exploration of the model space to help avoid the trap of falling into local minima. Finally, stochastic models allow prediction of observable distributions (and through them observable uncertainties). Published in the Proceedings of the 30th Monitoring Research Review: Ground-Based Nuclear Explosion Monitoring Technologies, held in Portsmouth, VA on 23-25 Sep 2008. The original document contains color images. BAA08-38.

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