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• W2278940410 abstract "Characterization of seismic sources is an important aspect of seismology. Parameter uncertainties in such inversions are essential for estimating solution robustness, but are rarely available. We have developed a non-linear moment tensor inversion method in a probabilistic Bayesian framework that also accounts for noise in the data. The method is designed for point source inversion using waveform data of moderate-size earthquakes and explosions at regional distances. This probabilistic approach results in an ensemble of models, whose density is proportional to parameter probability distribution and quantifies parameter uncertainties. Furthermore, we invert for noise in the data, allowing it to determine the model complexity. We implement an empirical noise covariance matrix that accounts for interdependence of observational errors present in waveform data. After we demonstrate the feasibility of the approach on synthetic data, we apply it to a Long Valley Caldera, CA, earthquake with a well-documented anomalous (non-double-couple) radiation from previous studies. We confirm a statistically significant isotropic component in the source without a trade-off with the compensated linear vector dipoles component." @default.
• W2278940410 created "2016-06-24" @default.
• W2278940410 creator A5049393692 @default.
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• W2278940410 date "2015-11-23" @default.
• W2278940410 modified "2023-10-01" @default.
• W2278940410 title "Point source moment tensor inversion through a Bayesian hierarchical model" @default.
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• W2278940410 doi "https://doi.org/10.1093/gji/ggv458" @default.
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