Estimation of Full Moment Tensors, Including Uncertainties, for Nuclear Explosions, Volcanic Events, and Earthquakes

A seismic moment tensor is a 3 × 3 symmetric matrix that provides a compact representation of a seismic source. We develop an algorithm to estimate moment tensors and their uncertainties from observed seismic data. For a given event, the algorithm performs a grid search over the six‐dimensional space of moment tensors by generating synthetic waveforms for each moment tensor and then evaluating a misfit function between the observed and synthetic waveforms. “The” moment tensor M 0 for the event is then the moment tensor with minimum misfit. To describe the uncertainty associated with M 0 , we first convert the misfit function to a probability function. The uncertainty, or rather the confidence, is then given by the “confidence curve” , where is the probability that the true moment tensor for the event lies within a certain neighborhood of M that has fractional volume V . The area under the confidence curve provides a single, abbreviated “confidence parameter” for M 0 . We apply the method to data from events in different regions and tectonic settings: 17 nuclear explosions and 12 earthquakes at the Nevada Test Site, 63 small ( M w <2.5) events at Uturuncu volcano in Bolivia, and 21 moderate ( M w >4) earthquakes in the southern Alaska subduction zone. Characterization of moment tensor uncertainties puts us in better position to discriminate among moment tensor source types and to assign physical processes to the events.