Abstract
The Permian Basin has become the United States’ largest producer of oil
over the past decade. Along with the rise in production, there has been
an increase in the rate of low magnitude earthquakes, some of which have
been associated with hydrocarbon extraction and wastewater injection. A
detailed knowledge of changes to the subsurface can aid in understanding
the causes of seismicity, and these changes can be inferred from InSAR
surface deformation measurements. In this study, we show that both
cm-level cumulative deformation, as well as mm-level coseismic
deformation signals, are detectable in West Texas. In a region west of
Mentone, TX, we reconstructed the subtle coseismic deformation signal on
the order of ~5 mm associated with the recent M4.9
earthquake. Over ~100,000 km2 of the Permian Basin, we
created annual cumulative LOS deformation maps, decomposing into
vertical and eastward components where overlapping data are available.
These maps contain numerous subsidence and uplift features near active
production and disposal wells. The most important deformation signatures
are linear streaks that extend tens of kilometers near Pecos, TX, where
a cluster of increased seismic events was cataloged by TexNet. As
validated by independent GPS data, our InSAR processing strategy
achieved millimeter-level accuracy. A careful treatment of the InSAR
tropospheric noise, which can be as large as 15 cm in West Texas, is
required to detect surface deformation signals with such low
signal-to-noise ratio. We developed an outlier removal technique based
on robust statistics to detect the presence of strong, non-Gaussian
noise. We compared the surface deformation solutions of multiple InSAR
time series methods, and all of them produced more accurate and
consistent deformation trends after removing outlier InSAR measurements.
We are exploring a Bayesian generalization of SBAS velocity estimation
by including probabilistic data rejection to determine which pixels
should be excluded from the model fitting. This technique provides a
full posterior distribution of the model parameters along with the
best-fit surface velocity.