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- W2152954335 abstract "Abstract. Seismic source inversion is a non-linear problem in seismology where not just the earthquake parameters themselves but also estimates of their uncertainties are of great practical importance. Probabilistic source inversion (Bayesian inference) is very adapted to this challenge, provided that the parameter space can be chosen small enough to make Bayesian sampling computationally feasible. We propose a framework for PRobabilistic Inference of Seismic source Mechanisms (PRISM) that parameterises and samples earthquake depth, moment tensor, and source time function efficiently by using information from previous non-Bayesian inversions. The source time function is expressed as a weighted sum of a small number of empirical orthogonal functions, which were derived from a catalogue of >1000 source time functions (STFs) by a principal component analysis. We use a likelihood model based on the cross-correlation misfit between observed and predicted waveforms. The resulting ensemble of solutions provides full uncertainty and covariance information for the source parameters, and permits propagating these source uncertainties into travel time estimates used for seismic tomography. The computational effort is such that routine, global estimation of earthquake mechanisms and source time functions from teleseismic broadband waveforms is feasible." @default.
- W2152954335 created "2016-06-24" @default.
- W2152954335 creator A5002235823 @default.
- W2152954335 creator A5063637184 @default.
- W2152954335 date "2014-11-17" @default.
- W2152954335 modified "2023-10-01" @default.
- W2152954335 title "Fully probabilistic seismic source inversion – Part 1: Efficient parameterisation" @default.
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- W2152954335 doi "https://doi.org/10.5194/se-5-1055-2014" @default.
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