We present a fully Bayesian inverse scheme to determine second moments
of the stress glut using teleseismic earthquake seismograms. The second
moments form a low-dimensional, physically-motivated representation of
the rupture process that captures its spatial extent, source duration,
and directivity effects. We determine an ensemble of second moment
solutions by employing Hamiltonian Monte Carlo and automatic
differentiation to efficiently approximate the posterior. This method
explicitly constrains the parameter space to be symmetric positive
definite, ensuring the derived source properties have physically
meaningful values. The framework accounts for the autocorrelation
structure of the errors and incorporates hyperpriors on the uncertainty.
We validate this methodology using a synthetic test and subsequently
apply it to the 2019 Mw7.1 Ridgecrest earthquake using teleseismic data.
The distributions of second moments determined for this event provide
probabilistic descriptions of low-dimensional rupture characteristics
that are generally consistent with results from previous studies. The
success of this case study suggests that probabilistic and comparable
finite source properties may be discerned for large global events
regardless of the quality and coverage of local instrumentation.