Bayesian Estimation of Surface Strain Rates from GNSS Measurements:
application to the Southwestern US
Seismic hazard assessment in active fault zones can benefit of strain
rate measurements derived from geodetic data. Producing a continuous
strain rate map from discrete data is an inverse problem traditionally
tackled with standard interpolation schemes. Most algorithms require
user-defined regression parameters that determine the smoothness of the
recovered velocity field, and the amplitude of its spatial derivatives.
This may lead to biases in the strain rates estimation which could
eventually impact studies on earthquake hazard.
Here we propose a transdimensional Bayesian method to estimate surface
strain rates from GNSS velocities. We parameterize the velocity field
with a variable number of Delaunay triangles, and use a reversible jump
Monte-Carlo Markov Chain algorithm to sample the probability
distribution of surface velocities and spatial derivatives. The solution
is a complete probability distribution function for each component of
the strain rate field. We conduct synthetic tests and compare our
approach to a standard b-spline interpolation scheme. Our method is more
resilient to data errors and uneven data distribution, while providing
uncertainties associated with recovered velocities and strain rates.
We apply our method to the Southwestern US, an extensively studied and
monitored area and infer probabilistic strain rates along the main fault
systems, including the San Andreas one, from the inversion of
interseismic GNSS velocities.
Our approach provide a full description of the strain rate tensor for
zones where strain rates are highly contrasted, with no need to manually
tune user-defined parameters. We recover sharp velocity gradients,
without systematic artifacts.